Relevant bibliographies by topics / Data-driven experiments

Academic literature on the topic 'Data-driven experiments'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Data-driven experiments"

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Perng, Sung-Yueh, Rob Kitchin, and Leighton Evans. "Locative media and data-driven computing experiments." Big Data & Society 3, no. 1 (January 5, 2016): 205395171665216. http://dx.doi.org/10.1177/2053951716652161.

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Cruz, Sérgio Manuel Serra da, and José Antonio Pires do Nascimento. "Towards integration of data-driven agronomic experiments with data provenance." Computers and Electronics in Agriculture 161 (June 2019): 14–28. http://dx.doi.org/10.1016/j.compag.2019.01.044.

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De Persis, Claudio, and Pietro Tesi. "Designing Experiments for Data-Driven Control of Nonlinear Systems." IFAC-PapersOnLine 54, no. 9 (2021): 285–90. http://dx.doi.org/10.1016/j.ifacol.2021.06.085.

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Koning, A. J., J. P. Delaroche, and O. Bersillon. "Nuclear data for accelerator driven systems: Nuclear models, experiments and data libraries." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 414, no. 1 (September 1998): 49–67. http://dx.doi.org/10.1016/s0168-9002(98)00528-2.

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Frederik, Joeri, Lars Kröger, Gerd Gülker, and Jan-Willem van Wingerden. "Data-driven repetitive control: Wind tunnel experiments under turbulent conditions." Control Engineering Practice 80 (November 2018): 105–15. http://dx.doi.org/10.1016/j.conengprac.2018.08.011.

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Lin, Shangjing, Jianguo Yu, and Ji Ma. "Big Data Driven Mobile Cellular Networks: Modelling, Experiments, and Applications." IOP Conference Series: Materials Science and Engineering 466 (December 28, 2018): 012074. http://dx.doi.org/10.1088/1757-899x/466/1/012074.

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Van Ameijde, Jeroen. "Data-driven Urban Design." SPOOL 9, no. 1 (May 27, 2022): 35–48. http://dx.doi.org/10.47982/spool.2022.1.03.

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Nicholas Negroponte and MIT’s Architecture Machine Group speculated in the 1970s about computational processes that were open to participation, incorporating end-user preferences and democratizing urban design. Today’s ‘smart city’ technologies, using the monitoring of people’s movement and activity patterns to offer more effective and responsive services, might seem like contemporary interpretations of Negroponte’s vision, yet many of the collectors of user information are disconnected from urban policy making. This article presents a series of theoretical and procedural experiments conducted through academic research and teaching, developing user-driven generative design processes in the spirit of ‘The Architecture Machine’. It explores how new computational tools for site analysis and monitoring can enable data-driven urban place studies, and how these can be connected to generative strategies for public spaces and environments at various scales. By breaking down these processes into separate components of gathering, analysing, translating and implementing data, and conceptualizing them in relation to urban theory, it is shown how data-driven urban design processes can be conceived as an open-ended toolkit to achieve various types of user-driven outcomes. It is argued that architects and urban designers are uniquely situated to reflect on the benefits and value systems that control data-driven processes, and should deploy these to deliver more resilient, liveable and participatory urban spaces.
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Fiordalis, Andrew, and Christos Georgakis. "Data-driven, using design of dynamic experiments, versus model-driven optimization of batch crystallization processes." Journal of Process Control 23, no. 2 (February 2013): 179–88. http://dx.doi.org/10.1016/j.jprocont.2012.08.011.

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Murari, A., E. Peluso, T. Craciunescu, S. Dormido-Canto, M. Lungaroni, R. Rossi, L. Spolladore, J. Vega, and M. Gelfusa. "Frontiers in data analysis methods: from causality detection to data driven experimental design." Plasma Physics and Controlled Fusion 64, no. 2 (December 31, 2021): 024002. http://dx.doi.org/10.1088/1361-6587/ac3ded.

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Abstract On the route to the commercial reactor, the experiments in magnetical confinement nuclear fusion have become increasingly complex and they tend to produce huge amounts of data. New analysis tools have therefore become indispensable, to fully exploit the information generated by the most relevant devices, which are nowadays very expensive to both build and operate. The paper presents a series of innovative tools to cover the main aspects of any scientific investigation. Causality detection techniques can help identify the right causes of phenomena and can become very useful in the optimisation of synchronisation experiments, such as the pacing of sawteeth instabilities with ion cyclotron radiofrequency heating modulation. Data driven theory is meant to go beyond traditional machine learning tools, to provide interpretable and physically meaningful models. The application to very severe problems for the tokamak configuration, such as disruptions, could help not only in understanding the physics but also in extrapolating the solutions to the next generation of devices. A specific methodology has also been developed to support the design of new experiments, proving that the same progress in the derivation of empirical models could be achieved with a significantly reduced number of discharges.
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Knox, Joseph E., Kameron Decker Harris, Nile Graddis, Jennifer D. Whitesell, Hongkui Zeng, Julie A. Harris, Eric Shea-Brown, and Stefan Mihalas. "High-resolution data-driven model of the mouse connectome." Network Neuroscience 3, no. 1 (January 2019): 217–36. http://dx.doi.org/10.1162/netn_a_00066.

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Knowledge of mesoscopic brain connectivity is important for understanding inter- and intraregion information processing. Models of structural connectivity are typically constructed and analyzed with the assumption that regions are homogeneous. We instead use the Allen Mouse Brain Connectivity Atlas to construct a model of whole-brain connectivity at the scale of 100 μm voxels. The data consist of 428 anterograde tracing experiments in wild type C57BL/6J mice, mapping fluorescently labeled neuronal projections brain-wide. Inferring spatial connectivity with this dataset is underdetermined, since the approximately 2 × 105 source voxels outnumber the number of experiments. To address this issue, we assume that connection patterns and strengths vary smoothly across major brain divisions. We model the connectivity at each voxel as a radial basis kernel-weighted average of the projection patterns of nearby injections. The voxel model outperforms a previous regional model in predicting held-out experiments and compared with a human-curated dataset. This voxel-scale model of the mouse connectome permits researchers to extend their previous analyses of structural connectivity to much higher levels of resolution, and it allows for comparison with functional imaging and other datasets.
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Dissertations / Theses on the topic "Data-driven experiments"

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Cedeno, Vanessa Ines. "Pipelines for Computational Social Science Experiments and Model Building." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/91445.

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There has been significant growth in online social science experiments in order to understand behavior at-scale, with finer-grained data collection. Considerable work is required to perform data analytics for custom experiments. In this dissertation, we design and build composable and extensible automated software pipelines for evaluating social phenomena through iterative experiments and modeling. To reason about experiments and models, we design a formal data model. This combined approach of experiments and models has been done in some studies without automation, or purely conceptually. We are motivated by a particular social behavior, namely collective identity (CI). Group or CI is an individual's cognitive, moral, and emotional connection with a broader community, category, practice, or institution. Extensive experimental research shows that CI influences human decision-making. Because of this, there is interest in modeling situations that promote the creation of CI in order to learn more from the process and to predict human behavior in real life situations. One of our goals in this dissertation is to understand whether a cooperative anagram game can produce CI within a group. With all of the experimental work on anagram games, it is surprising that very little work has been done in modeling these games. Also, abduction is an inference approach that uses data and observations to identify plausibly (and preferably, best) explanations for phenomena. Abduction has broad application in robotics, genetics, automated systems, and image understanding, but have largely been devoid of human behavior. We use these pipelines to understand intra-group cooperation and its effect on fostering CI. We devise and execute an iterative abductive analysis process that is driven by the social sciences. In a group anagrams web-based networked game setting, we formalize an abductive loop, implement it computationally, and exercise it; we build and evaluate three agent-based models (ABMs) through a set of composable and extensible pipelines; we also analyze experimental data and develop mechanistic and data-driven models of human reasoning to predict detailed game player action. The agreement between model predictions and experimental data indicate that our models can explain behavior and provide novel experimental insights into CI.
Doctor of Philosophy
To understand individual and collective behavior, there has been significant interest in using online systems to carry out social science experiments. Considerable work is required for analyzing the data and to uncover interesting insights. In this dissertation, we design and build automated software pipelines for evaluating social phenomena through iterative experiments and modeling. To reason about experiments and models, we design a formal data model. This combined approach of experiments and models has been done in some studies without automation, or purely conceptually. We are motivated by a particular social behavior, namely collective identity (CI). Group or CI is an individual’s cognitive, moral, and emotional connection with a broader community, category, practice, or institution. Extensive experimental research shows that CI influences human decision-making, so there is interest in modeling situations that promote the creation of CI to learn more from the process and to predict human behavior in real life situations. One of our goals in this dissertation is to understand whether a cooperative anagram game can produce CI within a group. With all of the experimental work on anagrams games, it is surprising that very little work has been done in modeling these games. In addition, to identify best explanations for phenomena we use abduction. Abduction is an inference approach that uses data and observations. Abduction has broad application in robotics, genetics, automated systems, and image understanding, but have largely been devoid of human behavior. In a group anagrams web-based networked game setting we do the following. We use these pipelines to understand intra-group cooperation and its effect on fostering CI. We devise and execute an iterative abductive analysis process that is driven by the social sciences. We build and evaluate three agent-based models (ABMs). We analyze experimental data and develop models of human reasoning to predict detailed game player action. We claim our models can explain behavior and provide novel experimental insights into CI, because there is agreement between the model predictions and the experimental data.
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Merikle, Elizabeth Paige 1965. "Facilitation of performance on a picture fragment completion test: Data-driven potentiation in perception." Thesis, The University of Arizona, 1991. http://hdl.handle.net/10150/277941.

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A technique commonly used to study the structure of memory entails preceding a task by a brief masked presentation of a potentially relevant stimulus. In two experiments, I examined the type of facilitation obtained on a picture fragment completion task by prior presentation of either the name of the completed object, a complete picture of the object, or the fragment itself. In Experiment 1a significantly more ambiguous picture fragments (i.e. fragments supporting a number of interpretations) were identified after exposure to pictures than to picture names or picture fragments. Experiment 1b verified that the information in the masked primes was not available to conscious awareness. These results suggest that under limited encoding conditions only bottom-up activation provided by prior presentation of the fragments aids shape recognition under degraded conditions. Implications for the structures and processes involved in shape recognition are discussed.
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Oskarsdottir, Eyglo Myrra. "Towards a Data-Driven Pricing Decision With the Help of A/B Testing." Thesis, KTH, Nationalekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199224.

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An A/B test is implemented on a SaaS rm's product page to examine the di erence in conversion rates from website visitors who are randomly assigned to two di erent product-landing pages that show di erent prices. To count as a successful conversion the visitors that view a product-landing page have to click on a \Free Trial" button. Half of the group will be assigned the treatment page, which will state higher prices and the other half will be assigned the controlled page, which will state today's current price. The only variant that will di er from the two pages will be the stated price of the product and all other factors will be kept constant. The controlled experiment is executed to get a sense of customers' price sensitivity, hence this thesis contributes to microeconomic research of the private sector, more specically to the ICT industry by using a novel approach with the help of A/B testing on prices. The results showed no statistical signicance difference between the two variations, which can be translated to accepting the null hypothesis; the demand for a particular Software-As-A-Service product will hold unchanged after the proposed price increase. At rst, this could be a surprising result but when looking into the industry, which the rm participates in and their early mover advantages this result could have been assumed.
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Emerton, Guy. "Data-driven methods for exploratory analysis in chemometrics and scientific experimentation." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86366.

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Thesis (MSc)--Stellenbosch University, 2014.
ENGLISH ABSTRACT: Background New methods to facilitate exploratory analysis in scientific data are in high demand. There is an abundance of available data used only for confirmatory analysis from which new hypotheses can be drawn. To this end, two new exploratory techniques are developed: one for chemometrics and another for visualisation of fundamental scientific experiments. The former transforms large-scale multiple raw HPLC/UV-vis data into a conserved set of putative features - something not often attempted outside of Mass-Spectrometry. The latter method ('StatNet'), applies network techniques to the results of designed experiments to gain new perspective on variable relations. Results The resultant data format from un-targeted chemometric processing was amenable to both chemical and statistical analysis. It proved to have integrity when machine-learning techniques were applied to infer attributes of the experimental set-up. The visualisation techniques were equally successful in generating hypotheses, and were easily extendible to three different types of experimental results. Conclusion The overall aim was to create useful tools for hypothesis generation in a variety of data. This has been largely reached through a combination of novel and existing techniques. It is hoped that the methods here presented are further applied and developed.
AFRIKAANSE OPSOMMING: Agtergrond Nuwe metodes om ondersoekende ontleding in wetenskaplike data te fasiliteer is in groot aanvraag. Daar is 'n oorvloed van beskikbaar data wat slegs gebruik word vir bevestigende ontleding waaruit nuwe hipoteses opgestel kan word. Vir hierdie doel, word twee nuwe ondersoekende tegnieke ontwikkel: een vir chemometrie en 'n ander vir die visualisering van fundamentele wetenskaplike eksperimente. Die eersgenoemde transformeer grootskaalse veelvoudige rou HPLC / UV-vis data in 'n bewaarde stel putatiewe funksies - iets wat nie gereeld buite Massaspektrometrie aangepak word nie. Die laasgenoemde metode ('StatNet') pas netwerktegnieke tot die resultate van ontwerpte eksperimente toe om sodoende ân nuwe perspektief op veranderlike verhoudings te verkry. Resultate Die gevolglike data formaat van die ongeteikende chemometriese verwerking was in 'n formaat wat vatbaar is vir beide chemiese en statistiese analise. Daar is bewys dat dit integriteit gehad het wanneer masjienleertegnieke toegepas is om eienskappe van die eksperimentele opstelling af te lei. Die visualiseringtegnieke was ewe suksesvol in die generering van hipoteses, en ook maklik uitbreibaar na drie verskillende tipes eksperimentele resultate. Samevatting Die hoofdoel was om nuttige middele vir hipotese generasie in 'n verskeidenheid van data te skep. Dit is grootliks bereik deur 'n kombinasie van oorspronklike en bestaande tegnieke. Hopelik sal die metodes wat hier aangebied is verder toegepas en ontwikkel word.
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Kalibjian, J. R. "A Packet Based, Data Driven Telemetry System for Autonomous Experimental Sub-Orbital Spacecraft." International Foundation for Telemetering, 1993. http://hdl.handle.net/10150/608857.

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International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada
A data driven telemetry system is described that responds to the rapid nature in which experimental satellite telemetry content is changed during the development process. It also meets the needs of a diverse experiment in which the many phases of a mission may contain radically different types of telemetry data. The system emphasizes mechanisms for achieving high redundancy of critical data. A practical example of such an implementation, Brilliant Pebbles Flight Experiment Three (FE-3), is cited.
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Zhao, He Sokhansanj Bahrad. "Systematic data-driven modeling of cellular systems for experimental design and hypothesis evaluation /." Philadelphia, Pa. : Drexel University, 2009. http://hdl.handle.net/1860/3133.

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Guerrero, Ludueña Richard E. "Data Driven Approach to Enhancing Efficiency and Value in Healthcare." Doctoral thesis, Universitat de Barcelona, 2017. http://hdl.handle.net/10803/456670.

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Healthcare is changing, and the era of data-driven healthcare organisations is increasingly popular. Data-Driven approaches (e.g., Machine Learning, Metaheuristics, Modelling and Simulation, Data Analytics, and Data Visualisation) can be used to increase efficiency and value in health services. Despite extensive research and technological development, the evidence impact of those methodologies in the healthcare sector is limited. In this Thesis we argue that an approach without borders in terms of academic societies and field of study could help to tackle this lack of impact to enhance efficiency and value in healthcare. This Thesis is based on solving practical problems in healthcare, with the research drawing upon both theoretical and empirical analysis. The research is organised in four stages. In the first, a variety of techniques from Modelling and Simulation were studied and used to analyse current performance and to model improved and more efficient future states of healthcare systems. The focus was the analysis of capacity, demand, activity, and queues both at hospital and population levels. In the second part, a Genetic Algorithm was used to solve a Routing Home Healthcare problem. In the third part, Social Network Analysis was used to visualise and analyse email networks. In the final part, a new healthcare system performance metric is proposed and implemented using a case study. New frameworks to implement these methodologies in the context of real-world problems are presented throughout the Thesis. In collaboration with University of Southampton, Wessex Academic Health Science Network (AHSN), and NHS England, several projects were developed and implemented for healthcare improvement in the UK. The work aims to increase early detection of cancer and thereby reduce premature mortality. The research was conducted working closely with NHS organisations across the Wessex region in England to produce bespoke endoscopy service modelling, as well as population level models. At a regional level, a Colorectal Cancer Screening Programme model was developed. An analysis of endoscopy activity, capacity and demand across the region was conducted. Future demand for endoscopy services in five years' time was estimated, and we found that the system has enough capacity to attend the expected future activity. A new healthcare system performance metric is presented as a tool to improve healthcare services. Genetic Algorithm metaheuristic was applied in a variant of the Home Health Care Problem (HHCP), focusing on the route planning of clinical homecare. Working with the Hospital del Mar Medical Research Institute of Barcelona and the Agency of Health Quality and Assessment of Catalonia a study was developed to estimate future utilisation scenarios of knee arthroplasty (KA) revision in the Spanish National Health System in the short-term (2015) and long-term (2030) and their impact on primary KA utilisation. One of the findings was that the variation in the number of revisions depended on both the primary utilisation rate and the survival function applied. Future activity and resources needed was estimated. A Social Network Analysis (SNA) project was developed in collaboration with the Wessex AHSN to analyse and extract insight from an organisational email, using SNA and Data Mining. A new healthcare system performance metric - based on the Overall Equipment Effectiveness (OEE) measure - is proposed and evaluated using real data from and Endoscopy Unit from a UK based hospital. To summarise, this work identifies four key techniques to use in the investigation of health data - Machine Learning Algorithms, Metaheuristic, Discrete Event Simulation and Data Analytics & Visualisations. Following a review of the different subjects and associated issues, those four techniques were evaluated and used with an applied-focus to solve healthcare problems. Key learning points from all different studies, as well as challenges and opportunities for the application of data-driven methodologies are discussed in the final chapter of the Thesis.
La asistencia sanitaria está cambiando y la era de las organizaciones sanitarias basadas en datos es cada vez más popular. Los enfoques basados en datos (por ejemplo, Aprendizaje Automático; Meta-heurísticas; Modelamiento y Simulación; y Análisis y Visualización de datos) pueden utilizarse para aumentar la eficiencia y el valor en los servicios sanitarios. A pesar de la amplia investigación y el desarrollo tecnológico, la evidencia sobre el impacto de estas metodologías en el sector sanitario es limitada. En esta tesis argumentamos que un enfoque sin fronteras en términos de sociedades académicas y campo de estudio podría ayudar a abordar esta falta de impacto para aumentar la eficiencia y el valor en la asistencia sanitaria. Esta tesis se basa en la resolución de problemas prácticos en el sector sanitario, con un enfoque tanto teórico como práctico. La investigación se organizó en cuatro etapas. En la primera, una variedad de técnicas de modelamiento y simulación fueron estudiadas y aplicadas en el análisis y simulación de mejores y más eficientes configuraciones de sistemas sanitarios. El objetivo fue un análisis de capacidad, demanda, actividad y listas de esperas a nivel hospitalario y poblacional. En la segunda parte, un Algoritmo Genético fue implementado para resolver un problema de ruteo de personal sanitario encargado de atención de salud en el hogar. En la tercera parte, Análisis de Redes Sociales fue utilizado para visualizar y analizar una red de usuarios de correos electrónicos. En la etapa final, se propone una nueva métrica para evaluar el rendimiento de sistemas sanitarios, la cual fue implementada a través de un caso de estudio. Diferentes marcos de referencia para la implementación de estas metodologías en problemas reales se presentan a lo largo de la tesis.
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Deshpande, Shubhangi. "Data Driven Surrogate Based Optimization in the Problem Solving Environment WBCSim." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/35901.

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Large scale, multidisciplinary, engineering designs are always difficult due to the complexity and dimensionality of these problems. Direct coupling between the analysis codes and the optimization routines can be prohibitively time consuming. One way of tackling this problem is by constructing computationally cheap(er) approximations of the expensive simulations, that mimic the behavior of the simulation model as closely as possible. This paper presents a data driven, surrogate based optimization algorithm that uses a trust region based sequential approximate optimization (SAO) framework and a statistical sampling approach based on design of experiment (DOE) arrays. The algorithm is implemented using techniques from the two packages SURFPACK and SHEPPACK that provide a collection of approximation algorithms to build the surrogates and three different DOE techniques: full factorial (FF), Latin hypercube sampling (LHS), and central composite design (CCD) are used to train the surrogates. The biggest concern in using the proposed methodology is the generation of the required database. This thesis proposes a data driven approach where an expensive simulation run is required if and only if a nearby data point does not exist in the cumulatively growing database. Over time the database matures and is enriched as more and more optimizations are performed. Results show that the response surface approximations constructed using design of experiments can be effectively managed by a SAO framework based on a trust region strategy. An interesting result is the significant reduction in the number of simulations for the subsequent runs of the optimization algorithm with a cumulatively growing simulation database.
Master of Science
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Jha, Rajesh. "Combined Computational-Experimental Design of High-Temperature, High-Intensity Permanent Magnetic Alloys with Minimal Addition of Rare-Earth Elements." FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/2621.

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AlNiCo magnets are known for high-temperature stability and superior corrosion resistance and have been widely used for various applications. Reported magnetic energy density ((BH) max) for these magnets is around 10 MGOe. Theoretical calculations show that ((BH) max) of 20 MGOe is achievable which will be helpful in covering the gap between AlNiCo and Rare-Earth Elements (REE) based magnets. An extended family of AlNiCo alloys was studied in this dissertation that consists of eight elements, and hence it is important to determine composition-property relationship between each of the alloying elements and their influence on the bulk properties. In the present research, we proposed a novel approach to efficiently use a set of computational tools based on several concepts of artificial intelligence to address a complex problem of design and optimization of high temperature REE-free magnetic alloys. A multi-dimensional random number generation algorithm was used to generate the initial set of chemical concentrations. These alloys were then examined for phase equilibria and associated magnetic properties as a screening tool to form the initial set of alloy. These alloys were manufactured and tested for desired properties. These properties were fitted with a set of multi-dimensional response surfaces and the most accurate meta-models were chosen for prediction. These properties were simultaneously extremized by utilizing a set of multi-objective optimization algorithm. This provided a set of concentrations of each of the alloying elements for optimized properties. A few of the best predicted Pareto-optimal alloy compositions were then manufactured and tested to evaluate the predicted properties. These alloys were then added to the existing data set and used to improve the accuracy of meta-models. The multi-objective optimizer then used the new meta-models to find a new set of improved Pareto-optimized chemical concentrations. This design cycle was repeated twelve times in this work. Several of these Pareto-optimized alloys outperformed most of the candidate alloys on most of the objectives. Unsupervised learning methods such as Principal Component Analysis (PCA) and Heirarchical Cluster Analysis (HCA) were used to discover various patterns within the dataset. This proves the efficacy of the combined meta-modeling and experimental approach in design optimization of magnetic alloys.
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Marín, de Mas Igor Bartolomé. "Development and application of novel model-driven and data-driven approaches to study metabolism in the framework of systems medicine." Doctoral thesis, Universitat de Barcelona, 2015. http://hdl.handle.net/10803/296313.

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The general aim of this thesis is to develop and apply new computational tools to overcome existing limitations in the analysis of metabolism. This thesis is focused on developing new computational strategies to overcome the following identified limitations: i) The existing metabolic flux analysis tools does not account for the existence of metabolic channeling: Here we developed a new computational tool based on non-stationary 13 C-FBA to evaluate different models reflecting different topologies of intracellular metabolism, using the channeling in hepatocytes as case of concep ii) Metabolic drug-target discovery based on GSMM does not consider the different cell subpopulations existing within the tumor: Here we develope a method that integrate trancriptomic data into a comparative genome-scale metabolic network reconstruction analysis in the context of intra-tumoral heterogeneity . We determined subpopulation-specific drug targets . Additionally we determined a metabolic gene signature associated to tumor progression in pc that was correlated with other types of cancer. Iii) Current mechanistic and probabilistic computational approaches are not suitable to study the complexity of the crosstalk between metabolic and gene regulatory networks.: Here we developed a novel computational method combining probabilistic and mechanistic approaches to integrate multi-level omic data into a discrete model-based analysis. This method allowed to analyze the mechanism underlying the crosstalk between metabolism and gene regulation, using as case of concept the study of the abnormal adaptation to training in COPD patients.
La presente tesis doctoral se centra en el desarrollo de herramientas computacionales que permitan el estudio de los mecanismos moleculares que ocurren dentro de la célula. Mas específicamente estudia el metabolismo celular desde diferentes puntos de vista usando y desarrollando métodos computacionales basados en diversas metodologías. Así pues en un primer capitulo se desarrolla un método basado en el analista de los flujos metabólicos en estado no estacional isotópico utilizando modelos cinéticos para estudiar el fenómeno de la canalización metabólica en hepatocitos. Este fenómeno modifica la topología metabólica alterando el fenotipo. Nuestro método nos permitió discriminar varios modelos con distintas topología prediciendo la existencia de canalización metabólica en la glucólisis. En el segundo capitulo se desarrolló un método para analizar el metabolismo tumoral teniendo en cuenta la heterogeneidad de poblaciones. En concreto estudiamos dos subpoblaciones extraídas de una linea celular de cáncer de próstata. Para ello utilizamos un modelo a gran escala de todo el metabolismo celular humano. El análisis reflejó la existencia de diferencias notables a nivel de vías metabólicas concretas, confiriendo a cada subpoblacion sensibilidades distintas a diferentes fármacos. En esta linea se demostró que mientras las células PC-3M eran sensibles al etomoxir e insensibles al calcitriol, las PC-3S presentaban una sensibilidad opuesta. En el tercero y ultimo capitulo de la tesis desarrollamos un nuevo método computacional que integra aproximaciones probabilísticas y mecanicistas para integrar diferentes tipos de datos en un análisis basado en modelos discretos. Para ello utilizamos como caso de concepto el estudio de la adaptación anómala al entrenamiento de pacientes con EPOC. El análisis reveló diferencias importantes a nivel de metabolismo energético en comparación con el grupo control.
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Books on the topic "Data-driven experiments"

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Bazerman, Max H., and Michael Luca. Power of Experiments: Decision Making in a Data-Driven World. MIT Press, 2020.

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Bazerman, Max H., and Michael Luca. Power of Experiments: Decision Making in a Data-Driven World. MIT Press, 2020.

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Bazerman, Max H., and Michael Luca. Power of Experiments - Decision Making in a Data-Driven World. MIT Press, 2020.

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Bazerman, Max H., and Michael Luca. Power of Experiments: Decision Making in a Data-Driven World. MIT Press, 2021.

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Scaletti, Carla. Sonification ≠ Music. Edited by Roger T. Dean and Alex McLean. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.013.9.

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Starting from the observation that symbolic language is not the only channel for human communication, this chapter examines ‘data sonification’, a means of understanding, reasoning about, and communicating meaning that extends beyond that which can be conveyed by symbolic language alone. Data sonification is a mapping from data generated by a model, captured in an experiment, or otherwise gathered through observation to one or more parameters of an audio signal or sound synthesis model for the purpose of better understanding, communicating, or reasoning about the original model, experiment, or system. Although data sonification shares techniques and materials with data-driven music, it is in the interests of the practitioners of both music composition and data sonification to maintain a distinction between the two fields.
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Rissen, Paul. Experiment-Driven Product Development: How to Use a Data-Lnformed Approach to Learn, Iterate, and Succeed Faster. Apress L. P., 2019.

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Whitehouse, Harvey. The Ritual Animal. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780199646364.001.0001.

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The ritual animal longs to belong. Rituals are a way of defining the boundaries of social groups and binding their members together. The ritual modes theory set out in this book seeks to unravel the psychology behind these processes, and to explain how ritual behaviour evolved, including how different modes of ritual performance have shaped global history over many millennia. Testing the theory has meant designing experiments run with children in psychology labs and on remote Pacific islands, gathering survey data with armed insurgents in the Middle East and Muslim fundamentalists in Indonesia, monitoring heart rate and stress among football fans in Brazil, and measuring changes in the brain as people observe traditional Chinese rituals in Singapore. The results of all this research point to new ways of addressing cooperation problems: from preventing violent extremism to motivating action on the climate crisis. Although this book is about the role of ritual in the evolution of social complexity, more broadly it models a new approach to the science of the social—an approach that is driven by real-world observation but grounded in the cognitive and evolutionary sciences. More ambitiously still, it shows how cumulative theory building can be used to deliver practical benefits for society at large, perhaps even addressing problems on a global scale by harnessing the formidable cohesive and cooperative capacities of the ritual animal.
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Thomsen, Bodil Marie Stavning, ed. Affects, Interfaces, Events. Imbricate! Press, 2021. http://dx.doi.org/10.22387/imbaie.

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This book engages with how affective encounters are shaped and conditioned by interfacial events. Together, the chapters explore the implications of this on a micro-perceptual and macro-relational level through an experimental middling of approaches and examples. While broadly departing from a Spinozist and Deleuzian theoretical foundation, the book weaves together a compelling number of conceptual and empirical trajectories. Always attuned to the implications, modulations and tonalities arising in the readings through art, journalism, bodies, an/archives, data and design, Affects, Interfaces, Events allows for a truly transdisciplinary resonance driven by theory, technology and practice.
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Campbell, John, Joey Huston, and Frank Krauss. Soft QCD. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199652747.003.0007.

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In Chapter 7, we discuss various aspects of the strong interaction containing a strong non-perturbative or low-scale component. We first briefly summarise the treatment of the total and inelastic cross sections through the analyticity of the scattering amplitude, which leads to the language of Regge poles. We introduce multiple parton interactions, with the underlying theoretical ideas for their description mainly driven by experimental data, before remarking on double parton scattering. Hadronisation, i.e. the transition from the parton of perturbation theory to the observable hadrons, is introduced by first using the idea of fragmentation functions and then discussing two popular phenomenological models describing this transition. We conclude this chapter by remarking on some ideas concerning the description of hadron decays through effective models.
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Bucy, Erik P., and Patrick Stewart. The Personalization of Campaigns: Nonverbal Cues in Presidential Debates. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228637.013.52.

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Nonverbal cues are important elements of persuasive communication whose influence in political debates are receiving renewed attention. Recent advances in political debate research have been driven by biologically grounded explanations of behavior that draw on evolutionary theory and view televised debates as contests for social dominance. The application of biobehavioral coding to televised presidential debates opens new vistas for investigating this time-honored campaign tradition by introducing a systematic and readily replicated analytical framework for documenting the unspoken signals that are a continuous feature of competitive candidate encounters. As research utilizing biobehavioral measures of presidential debates and other political communication progresses, studies are becoming increasingly characterized by the use of multiple methodologies and merging of disparate data into combined systems of coding that support predictive modeling.Key elements of nonverbal persuasion include candidate appearance, communication style and behavior, as well as gender dynamics that regulate candidate interactions. Together, the use of facial expressions, voice tone, and bodily gestures form uniquely identifiable display repertoires that candidates perform within televised debate settings. Also at play are social and political norms that govern candidate encounters. From an evaluative standpoint, the visual equivalent of a verbal gaffe is the commission of a nonverbal expectancy violation, which draws viewer attention and interferes with information intake. Through second screens, viewers are able to register their reactions to candidate behavior in real time, and merging biobehavioral and social media approaches to debate effects is showing how such activity can be used as an outcome measure to assess the efficacy of candidate nonverbal communication during televised presidential debates.Methodological approaches employed to investigate nonverbal cues in presidential debates have expanded well beyond the time-honored technique of content analysis to include lab experiments, focus groups, continuous response measurement, eye tracking, vocalic analysis, biobehavioral coding, and use of the Facial Action Coding System to document the muscle movements that comprise leader expressions. Given the tradeoffs and myriad considerations involved in analyzing nonverbal cues, critical issues in measurement and methodology must be addressed when conducting research in this evolving area. With automated coding of nonverbal behavior just around the corner, future research should be designed to take advantage of the growing number of methodological advances in this rapidly evolving area of political communication research.
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Book chapters on the topic "Data-driven experiments"

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Jiang, Lili, and Vicenç Torra. "On the Effects of Data Protection on Multi-database Data-Driven Models." In Lecture Notes in Computer Science, 226–38. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98018-4_19.

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AbstractThis paper analyses the effects of masking mechanism for privacy preservation in data-driven models (regression) with respect to database integration. Especially two data masking methods (microaggregation and rank swapping) are applied on two public datasets to evaluate the linear regression model in terms of privacy protection and prediction performance. Our preliminary experimental results show that both methods achieve a good trade-off of privacy protection and information loss. We also show that for some experiments although data integration produces some incorrect links, the linear regression model is still comparable, with respect to prediction error, to the one inferred from the original data.
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Concilio, Grazia, and Paola Pucci. "The Data Shake: An Opportunity for Experiment-Driven Policy Making." In The Data Shake, 3–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63693-7_1.

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AbstractThe wider availability of data and the growing technological advancements in data collection, management, and analysis introduce unprecedented opportunities, as well as complexity in policy making. This condition questions the very basis of the policy making process towards new interpretative models. Growing data availability, in fact, increasingly affects the way we analyse urban problems and make decisions for cities: data are a promising resource for more effective decisions, as well as for better interacting with the context where decisions are implemented. By dealing with the operative implications in the use of a growing amount of available data in policy making processes, this contribution starts discussing the chance offered by data in the design, implementation, and evaluation of a planning policy, with a critical review of the evidence-based policy making approaches; then it introduces the relevance of data in the policy design experiments and the conditions for its uses.
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Genç, Zülküf, Michel Oey, Hendrik van Antwerpen, and Frances Brazier. "Dynamic Data-Driven Experiments in the Smart Grid Domain with a Multi-agent Platform." In Multi-Agent Based Simulation XVI, 121–31. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31447-1_8.

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Starke, Ludger, Thoralf Niendorf, and Sonia Waiczies. "Data Preparation Protocol for Low Signal-to-Noise Ratio Fluorine-19 MRI." In Methods in Molecular Biology, 711–22. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_43.

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AbstractFluorine-19 MRI shows great promise for a wide range of applications including renal imaging, yet the typically low signal-to-noise ratios and sparse signal distribution necessitate a thorough data preparation.This chapter describes a general data preparation workflow for fluorine MRI experiments. The main processing steps are: (1) estimation of noise level, (2) correction of noise-induced bias and (3) background subtraction. The protocol is supplemented by an example script and toolbox available online.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This analysis protocol chapter is complemented by two separate chapters describing the basic concept and experimental procedure.
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Tomaselli, Venera, and Giulio Giacomo Cantone. "Multipoint vs slider: a protocol for experiments." In Proceedings e report, 91–96. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.19.

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Since the broad diffusion of Computer-Assisted survey tools (i.e. web surveys), a lively debate about innovative scales of measure arose among social scientists and practitioners. Implications are relevant for applied Statistics and evaluation research since while traditional scales collect ordinal observations, data from sliders can be interpreted as continuous. Literature, however, report excessive times of completion of the task from sliders in web surveys. This experimental protocol is aimed at testing hypotheses on the accuracy in prediction and dispersion of estimates from anonymous participants who are recruited online and randomly assigned into tasks in recognition of shades of colour. The treatment variable is two scales: a traditional multipoint 0-10 multipoint vs a slider 0-100. Shades have a unique parametrisation (true value) and participants have to guess the true value through the scale. These tasks are designed to recreate situations of uncertainty among participants while minimizing the subjective component of a perceptual assessment and maximizing information about scale-driven differences and biases. We propose to test statistical differences in the treatment variable: (i) mean absolute error from the true value (ii), time of completion of the task. To correct biases due to the variance in the number of completed tasks among participants, data about participants can be collected through both pre-tasks acceptance of web cookies and post-tasks explicit questions.
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Harlander, Uwe, Thomas von Larcher, Grady B. Wright, Michael Hoff, Kiril Alexandrov, and Christoph Egbers. "Orthogonal Decomposition Methods to Analyze PIV, LDV, and Thermography Data of Thermally Driven Rotating Annulus Laboratory Experiments." In Modeling Atmospheric and Oceanic Flows, 315–36. Hoboken, NJ: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781118856024.ch17.

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Parker, Austin, Gerardo I. Simari, Amy Sliva, and V. S. Subrahmanian. "Experimental Evaluation." In Data-driven Generation of Policies, 37–45. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4939-0274-3_5.

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Wang, Jing, Jinglin Zhou, and Xiaolu Chen. "Simulation Platform for Fault Diagnosis." In Intelligent Control and Learning Systems, 45–58. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_4.

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AbstractThe previous chapters have described the mathematical principles and algorithms of multivariate statistical methods, as well as the monitoring processes when used for fault diagnosis. In order to validate the effectiveness of data-driven multivariate statistical analysis methods in the field of fault diagnosis, it is necessary to conduct the corresponding fault monitoring experiments. Therefore this chapter introduces two kinds of simulation platform, Tennessee Eastman (TE) process simulation system and fed-batch Penicillin Fermentation Process simulation system. They are widely used as test platforms for the process monitoring, fault classification, and identification of industrial process. The related experiments based on PCA, CCA, PLS, and FDA are completed on the TE simulation platforms.
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Khandelwal, Dhruv. "Experimental Results." In Automating Data-Driven Modelling of Dynamical Systems, 173–220. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90343-5_7.

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Sartirana, A., P. Hennion, P. Mora de Freitas, I. Semenjuk, P. Busson, M. Jouvin, G. Philippon, et al. "Grid Computing Operations for the CMS Experiment at the GRIF Tier-2." In Data Driven e-Science, 347–56. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8014-4_27.

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Conference papers on the topic "Data-driven experiments"

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Deng, Alex, and Xiaolin Shi. "Data-Driven Metric Development for Online Controlled Experiments." In KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2939672.2939700.

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Silfverberg, Miikka, and Mans Hulden. "Initial Experiments in Data-Driven Morphological Analysis for Finnish." In Proceedings of the Fourth International Workshop on Computatinal Linguistics of Uralic Languages. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/w18-0209.

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Pareek, Kaushal Arun, Daniel May, Mohamad Abo Ras, and Bernhard Wunderle. "Towards Data Driven Failure Analysis Using Infrared Thermography." In 2021 22nd International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE). IEEE, 2021. http://dx.doi.org/10.1109/eurosime52062.2021.9410885.

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Dong, Hua, and Xueying Yang. "The project management of corneal transplantation in animal experiments based on BP neural network." In 2017 IEEE 6th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2017. http://dx.doi.org/10.1109/ddcls.2017.8068121.

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Ajusha, P. V., and P. Babu Anto. "Experiments on Malayalam Language using Graph-based Data-driven Dependency Parser." In 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). IEEE, 2019. http://dx.doi.org/10.1109/icicict46008.2019.8993315.

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Phung, Chi-Dung, Nour-El-Houda Yellas, Salah Bin Ruba, and Stefano Secci. "An Open Dataset for Beyond-5G Data-driven Network Automation Experiments." In 2022 1st International Conference on 6G Networking (6GNet). IEEE, 2022. http://dx.doi.org/10.1109/6gnet54646.2022.9830292.

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Khanam, Mayana Humera, Palli Suryachandra, and Kv Madhumurthy. "Experiments on POS tagging and data driven dependency parsing for Telugu language." In the International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2345396.2345567.

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Radac, Mircea-Bogdan, Raul-Cristian Roman, Radu-Emil Precup, and Emil M. Petriu. "Data-driven model-free control of twin rotor aerodynamic systems: Algorithms and experiments." In 2014 IEEE International Symposium on Intelligent Control (ISIC). IEEE, 2014. http://dx.doi.org/10.1109/isic.2014.6967639.

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Weiderhold, Joseph, David E. Lambert, and Michael Hopson. "Experimental Design and Data Collection for Dynamic Fragmentation Experiments." In ASME 2010 Pressure Vessels and Piping Division/K-PVP Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/pvp2010-25163.

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Experiments have been conducted to investigate the fracture and fragmentation characteristics of a liquid phased sintered (LPS) tungsten and high strength steel alloys. Metal cylinders, each of which was 20.32 cm tall and 5.08 cm inner/5.88 cm outer diameter, were explosively driven to failure. Two complimentary types of experiments were conducted in this series to determine input parameters for a related continuum mechanics based modeling effort. Open air experiments utilized ultra-high speed framing photography and a photonic Doppler velocimetry system (PDV). The information from these experiments provided a case wall velocity, relative time of breakup and strain-rate during the stress loading timeframe. Complimentary experiments were conducted in a water tank to perform a soft recovery of the fragments. The fragments were subsequently cleaned, massed, and characterized according to their mass and failure strain distributions. Various methods of analyzing the data (Mott & Weibull distributions) are discussed along with the calibration of the continuum damage model parameters. Results of the failure strain analysis, fragment distribution, and damage model are then supplied for use in subsequent modeling and application designs. Further details of the modeling and simulation approach are outlined in a complimentary set of two papers presented by Lambert [1] and Hopson [2].
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Hickmann, Kyle S., Deborah Shutt, Andrew Robinson, and Jonathan Lind. "Data-driven learning of impactor strength properties from shock experiments with additively-manufactured materials." In Applications of Machine Learning 2021, edited by Michael E. Zelinski, Tarek M. Taha, and Jonathan Howe. SPIE, 2021. http://dx.doi.org/10.1117/12.2594898.

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Reports on the topic "Data-driven experiments"

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Kurbanov, Serdar. Data Driven Trigger Design and Analysis for the NOvA Experiment. Office of Scientific and Technical Information (OSTI), January 2016. http://dx.doi.org/10.2172/1334263.

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O'neil, Ryunosuke. Data-driven Alignment of the Tracker for the Mu2e Experiment. Office of Scientific and Technical Information (OSTI), January 2020. http://dx.doi.org/10.2172/1769399.

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Zevotek, Robin, Keith Stakes, and Joseph Willi. Impact of Fire Attack Utilizing Interior and Exterior Streams on Firefighter Safety and Occupant Survival: Full-Scale Experiments. UL Firefighter Safety Research Institute, January 2018. http://dx.doi.org/10.54206/102376/dnyq2164.

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As research continues into how fire department interventions affect fire dynamics in the modern fire environment, questions continue to arise on the impact and implications of interior versus exterior fire attack on both occupant survivability and firefighter safety. This knowledge gap and lack of previous research into the impact of fire streams has driven the need for further research into fire department interventions at structure fires with a focus on hose streams and suppression tactics. As the third report in the project “Impact of Fire Attack Utilizing Interior and Exterior Streams on Firefighter Safety and Occupant Survival”, this report expands upon the fire research conducted to date by analyzing how firefighting tactics, specifically suppression methods, affect the thermal exposure and survivability of both building occupants and firefighters in residential structures. • Part I: Water Distribution • Part II: Air Entrainment • Part III: Full-Scale Residential Fire Experiments. This report evaluates fire attack in residential structures through twenty-six full-scale structure fire experiments. Two fire attack methods, interior and transitional, were preformed at UL’s large fire lab in Northbrook, IL, in a single-story 1,600 ft2 ranch test structure utilizing three different ventilation configurations. To determine conditions within the test structure it was instrumented for temperature, pressure, gas velocity, heat flux, gas concentration, and moisture content. Ad- ditionally, to provide information on occupant burn injuries, five sets of instrumented pig skin were located in pre-determined locations in the structure. The results were analyzed to determine consistent themes in the data. These themes were evaluated in conjunction with a panel of fire service experts to develop 18 tactical considerations for fire ground operations. As you review the following tactical considerations it is important to utilize both these research results and your per- sonal experience to develop your department’s polices and implement these considerations during structural firefighting.
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Snyder, Victor A., Dani Or, Amos Hadas, and S. Assouline. Characterization of Post-Tillage Soil Fragmentation and Rejoining Affecting Soil Pore Space Evolution and Transport Properties. United States Department of Agriculture, April 2002. http://dx.doi.org/10.32747/2002.7580670.bard.

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Tillage modifies soil structure, altering conditions for plant growth and transport processes through the soil. However, the resulting loose structure is unstable and susceptible to collapse due to aggregate fragmentation during wetting and drying cycles, and coalescense of moist aggregates by internal capillary forces and external compactive stresses. Presently, limited understanding of these complex processes often leads to consideration of the soil plow layer as a static porous medium. With the purpose of filling some of this knowledge gap, the objectives of this Project were to: 1) Identify and quantify the major factors causing breakdown of primary soil fragments produced by tillage into smaller secondary fragments; 2) Identify and quantify the. physical processes involved in the coalescence of primary and secondary fragments and surfaces of weakness; 3) Measure temporal changes in pore-size distributions and hydraulic properties of reconstructed aggregate beds as a function of specified initial conditions and wetting/drying events; and 4) Construct a process-based model of post-tillage changes in soil structural and hydraulic properties of the plow layer and validate it against field experiments. A dynamic theory of capillary-driven plastic deformation of adjoining aggregates was developed, where instantaneous rate of change in geometry of aggregates and inter-aggregate pores was related to current geometry of the solid-gas-liquid system and measured soil rheological functions. The theory and supporting data showed that consolidation of aggregate beds is largely an event-driven process, restricted to a fairly narrow range of soil water contents where capillary suction is great enough to generate coalescence but where soil mechanical strength is still low enough to allow plastic deforn1ation of aggregates. The theory was also used to explain effects of transient external loading on compaction of aggregate beds. A stochastic forInalism was developed for modeling soil pore space evolution, based on the Fokker Planck equation (FPE). Analytical solutions for the FPE were developed, with parameters which can be measured empirically or related to the mechanistic aggregate deformation model. Pre-existing results from field experiments were used to illustrate how the FPE formalism can be applied to field data. Fragmentation of soil clods after tillage was observed to be an event-driven (as opposed to continuous) process that occurred only during wetting, and only as clods approached the saturation point. The major mechanism of fragmentation of large aggregates seemed to be differential soil swelling behind the wetting front. Aggregate "explosion" due to air entrapment seemed limited to small aggregates wetted simultaneously over their entire surface. Breakdown of large aggregates from 11 clay soils during successive wetting and drying cycles produced fragment size distributions which differed primarily by a scale factor l (essentially equivalent to the Van Bavel mean weight diameter), so that evolution of fragment size distributions could be modeled in terms of changes in l. For a given number of wetting and drying cycles, l decreased systematically with increasing plasticity index. When air-dry soil clods were slightly weakened by a single wetting event, and then allowed to "age" for six weeks at constant high water content, drop-shatter resistance in aged relative to non-aged clods was found to increase in proportion to plasticity index. This seemed consistent with the rheological model, which predicts faster plastic coalescence around small voids and sharp cracks (with resulting soil strengthening) in soils with low resistance to plastic yield and flow. A new theory of crack growth in "idealized" elastoplastic materials was formulated, with potential application to soil fracture phenomena. The theory was preliminarily (and successfully) tested using carbon steel, a ductile material which closely approximates ideal elastoplastic behavior, and for which the necessary fracture data existed in the literature.
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van den Boogaard,, Vanessa, and Fabrizio Santoro. Co-Financing Community-Driven Development Through Informal Taxation: Experimental Evidence from South-Central Somalia. Institute of Development Studies (IDS), September 2021. http://dx.doi.org/10.19088/ictd.2021.016.

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Community contributions are often required as part of community-driven development (CDD) programmes, with payment encouraged through matching grants. However, little remains known about the impact of matching grants, or the implications of requiring community contributions in order for communities to receive development funding. This paper describes research where we partner with two non-governmental organisations (NGOs) – one international and one Somali – and undertake a randomised control trial of a CDD matching grant programme designed to incentivise informal contributions for local public goods in Gedo region in south-central Somalia. We rely on household survey data collected from 1,297 respondents in 31 treatment and 31 control communities, as well as surveys of village leaders and data on informal contributions from the mobile money platform used by community leaders to collect revenue. Two key findings emerge. First, our research shows that working with communities and incentivising informal revenue generation can be an effective way to deliver public goods and to support citizens and communities. Second, building on research exploring the potential for development interventions to spur virtuous or adverse cycles of governance, we show that development partners may work directly with community leaders and informal taxing institutions without necessarily undermining – and indeed perhaps strengthening – state legitimacy and related ongoing processes of statebuilding in the country. Indeed, despite playing no direct role in the matching grant programme, taxpayer perceptions of the legitimacy of the local government improved as a result of the programme. These findings deepen our understanding of how community contributions may be incentivised through matching grant programmes, and how they may contribute to CDD and public goods provision in a context of weak institutional capacity.
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