Статті в журналах: "Data with gaps"

1

Zielinski, Sarah. "Filling gaps in ecosystem data." Eos, Transactions American Geophysical Union 87, no. 26 (2006): 254. http://dx.doi.org/10.1029/2006eo260004.

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2

Aldhous, Peter. "Gaps loom in satellite data." Nature 355, no. 6362 (February 1992): 662. http://dx.doi.org/10.1038/355662a0.

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3

Pearman, Francis A., F. Chris Curran, Benjamin Fisher, and Joseph Gardella. "Are Achievement Gaps Related to Discipline Gaps? Evidence From National Data." AERA Open 5, no. 4 (October 2019): 233285841987544. http://dx.doi.org/10.1177/2332858419875440.

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There is growing interest in the relation between the racial achievement gap and the racial discipline gap. However, few studies have examined this relation at the national level. This study combines data from the Stanford Education Data Archive and the Civil Rights Data Collection and employs a district fixed effects analysis to examine whether and the extent to which racial discipline gaps are related to racial achievement gaps in Grades 3 through 8 in districts across the United States. In bivariate models, we find evidence that districts with larger racial discipline gaps have larger racial achievement gaps (and vice versa). Though other district-level differences account for the positive association between the Hispanic-White discipline gap and the Hispanic-White achievement gap, we find robust evidence that the positive association between the Black-White discipline gap and the Black-White achievement gap persists after controlling for a multitude of confounding factors. We also find evidence that the mechanisms connecting achievement to disciplinary outcomes are more salient for Black than White students.

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4

Shroff, Sangeeta. "Data Gaps in Agricultural Statistics:Some Issues." Artha Vijnana: Journal of The Gokhale Institute of Politics and Economics 58, no. 3 (September 1, 2016): 228. http://dx.doi.org/10.21648/arthavij/2016/v58/i3/147827.

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5

Cameron, Erin K., Inês S. Martins, Patrick Lavelle, Jérôme Mathieu, Leho Tedersoo, Felix Gottschall, Carlos A. Guerra, et al. "Global gaps in soil biodiversity data." Nature Ecology & Evolution 2, no. 7 (June 4, 2018): 1042–43. http://dx.doi.org/10.1038/s41559-018-0573-8.

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6

SCHNEIDER, MARY ELLEN. "Decoding Care Transitions Despite Data Gaps." Hospitalist News 5, no. 4 (April 2012): 3. http://dx.doi.org/10.1016/s1875-9122(12)70080-9.

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7

Gilbert, Natasha. "Data gaps threaten chemical safety law." Nature 475, no. 7355 (July 2011): 150–51. http://dx.doi.org/10.1038/475150a.

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8

Golbus, Jessica R., W. Nicholson Price, and Brahmajee K. Nallamothu. "Privacy Gaps for Digital Cardiology Data." Circulation 141, no. 8 (February 25, 2020): 613–15. http://dx.doi.org/10.1161/circulationaha.119.044966.

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9

Marqués, José Manuel, Fernando Ávila, Anahí Rodríguez-Martínez, Raúl Morales-Reséndiz, Antonio Marcos, Tamara Godoy, Pablo Villalobos, et al. "Policy report on FinTech data gaps." Latin American Journal of Central Banking 2, no. 3 (September 2021): 100037. http://dx.doi.org/10.1016/j.latcb.2021.100037.

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10

See, Craig R., Mark B. Green, Ruth D. Yanai, Amey S. Bailey, John L. Campbell, and Jeremy Hayward. "Quantifying uncertainty in annual runoff due to missing data." PeerJ 8 (July 21, 2020): e9531. http://dx.doi.org/10.7717/peerj.9531.

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Long-term streamflow datasets inevitably include gaps, which must be filled to allow estimates of runoff and ultimately catchment water budgets. Uncertainty introduced by filling gaps in discharge records is rarely, if ever, reported. We characterized the uncertainty due to streamflow gaps in a reference watershed at the Hubbard Brook Experimental Forest (HBEF) from 1996 to 2009 by simulating artificial gaps of varying duration and flow rate, with the objective of quantifying their contribution to uncertainty in annual streamflow. Gaps were filled using an ensemble of regressions relating discharge from nearby streams, and the predicted flow was compared to the actual flow. Differences between the predicted and actual runoff increased with both gap length and flow rate, averaging 2.8% of the runoff during the gap. At the HBEF, the sum of gaps averaged 22 days per year, with the lowest and highest annual uncertainties due to gaps ranging from 1.5 mm (95% confidence interval surrounding mean runoff) to 21.1 mm. As a percentage of annual runoff, uncertainty due to gap filling ranged from 0.2–2.1%, depending on the year. Uncertainty in annual runoff due to gaps was small at the HBEF, where infilling models are based on multiple similar catchments in close proximity to the catchment of interest. The method demonstrated here can be used to quantify uncertainty due to gaps in any long-term streamflow data set, regardless of the gap-filling model applied.

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11

Giest, Sarah, and Annemarie Samuels. "‘For good measure’: data gaps in a big data world." Policy Sciences 53, no. 3 (April 22, 2020): 559–69. http://dx.doi.org/10.1007/s11077-020-09384-1.

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12

Barbedo, Jayme Garcia Arnal. "Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps." Sensors 22, no. 6 (March 16, 2022): 2285. http://dx.doi.org/10.3390/s22062285.

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Acquiring useful data from agricultural areas has always been somewhat of a challenge, as these are often expansive, remote, and vulnerable to weather events. Despite these challenges, as technologies evolve and prices drop, a surge of new data are being collected. Although a wealth of data are being collected at different scales (i.e., proximal, aerial, satellite, ancillary data), this has been geographically unequal, causing certain areas to be virtually devoid of useful data to help face their specific challenges. However, even in areas with available resources and good infrastructure, data and knowledge gaps are still prevalent, because agricultural environments are mostly uncontrolled and there are vast numbers of factors that need to be taken into account and properly measured for a full characterization of a given area. As a result, data from a single sensor type are frequently unable to provide unambiguous answers, even with very effective algorithms, and even if the problem at hand is well defined and limited in scope. Fusing the information contained in different sensors and in data from different types is one possible solution that has been explored for some decades. The idea behind data fusion involves exploring complementarities and synergies of different kinds of data in order to extract more reliable and useful information about the areas being analyzed. While some success has been achieved, there are still many challenges that prevent a more widespread adoption of this type of approach. This is particularly true for the highly complex environments found in agricultural areas. In this article, we provide a comprehensive overview on the data fusion applied to agricultural problems; we present the main successes, highlight the main challenges that remain, and suggest possible directions for future research.

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13

Miglani, Varun. "Data Gaps on Contract Farming in India." Artha Vijnana: Journal of The Gokhale Institute of Politics and Economics 58, no. 3 (September 1, 2016): 261. http://dx.doi.org/10.21648/arthavij/2016/v58/i3/147832.

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14

Dou, Zhengxiong, and Xiaonan Wang. "Data delivery in VANET with network gaps." Journal of High Speed Networks 26, no. 2 (June 12, 2020): 111–24. http://dx.doi.org/10.3233/jhs-200633.

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15

Cooksey, Catherine, and Raju Datla. "Workshop on Bridging Satellite Climate Data Gaps." Journal of Research of the National Institute of Standards and Technology 116, no. 1 (January 2011): 505. http://dx.doi.org/10.6028/jres.116.002.

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16

Hendrickson, Ole. "Old-growth forests: Data gaps and challenges." Forestry Chronicle 79, no. 3 (June 1, 2003): 645–51. http://dx.doi.org/10.5558/tfc79645-3.

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How much old growth is there? How much was there? Is remaining old growth disappearing? If so, how fast and why? Is any more old growth being created? How fragmented are old-growth forests? What other forest types and land uses surround them? Scientists see these as challenging questions, never to be fully resolved. Policy makers see information gaps and want answers. They assume that because the public values old-growth forests, their continuing availability must be assured. Forest managers need to be convinced that old-growth forests provide unique values before taking costly measures to conserve them. The relative stability of old-growth forests is interesting from a management perspective. Are old-growth forests more resistant to high-intensity disturbances, such as crown fires and violent storms? Do they resist insect outbreaks? A related issue is the quality of ecosystem services provided by old-growth forests. Do they have an exceptional ability to provide clean water, to stabilize hydrologic regimes, and to moderate local climates? Can they be used to test hypotheses about complexity, stability, resilience, and ecosystem change? These questions provide a strong rationale for developing working definitions of old-growth forests, for retaining areas of old-growth forest, and for replicating old-growth features in landscapes managed for timber production. Old-growth forests are desirable sites for monitoring, serving as benchmarks for adaptive management. Knowledge about old-growth forests has already had a considerable impact on policy and management, particularly in coastal regions. Current research and monitoring systems may not be adequate for the task of identifying and describing the biological complexity and diversity inherent in old-growth forests. New investments in collecting and managing data from old-growth (and secondary) forests are needed, and will pay manifold dividends to future generations of Canadians. This paper suggests that the central role of old-growth forests in developing sustainable forest management should create an incentive for the forest science, policy, and management communities to unite in support of their conservation. Key words: biodiversity, gene conservation, resilience, ecosystem approach, information management, ecosystem services

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17

Betts, Kellyn S. "First U.S. ecosystems analysis reveals data gaps." Environmental Science & Technology 36, no. 21 (November 2002): 404A. http://dx.doi.org/10.1021/es022451k.

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18

Cooney, Catherine M. "Data gaps limit success of national indicators." Environmental Science & Technology 37, no. 21 (November 2003): 382A—383A. http://dx.doi.org/10.1021/es0326219.

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19

McDonald, Judith A., and Robert J. Thornton. "Estimating gender wage gaps: A data update." Journal of Economic Education 47, no. 2 (April 2, 2016): 140–41. http://dx.doi.org/10.1080/00220485.2016.1146100.

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20

Peebles, Laura, and Beverley Norris. "Filling ‘gaps’ in strength data for design." Applied Ergonomics 34, no. 1 (January 2003): 73–88. http://dx.doi.org/10.1016/s0003-6870(02)00073-x.

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21

&NA;, &NA;. "ANA, NCSDN MOVE TO MEND DATA GAPS." AJN, American Journal of Nursing 93, no. 3 (March 1993): 97. http://dx.doi.org/10.1097/00000446-199303000-00032.

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22

Micha, Renata, Jennifer Coates, Catherine Leclercq, U. Ruth Charrondiere, and Dariush Mozaffarian. "Global Dietary Surveillance: Data Gaps and Challenges." Food and Nutrition Bulletin 39, no. 2 (February 25, 2018): 175–205. http://dx.doi.org/10.1177/0379572117752986.

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Анотація:

Background: Detailed information on global individual-level consumption patterns is imperative for informed policy making. However, such data are dispersed and incomplete. Objective: To review and discuss the methodologies, observed data availability, challenges, and opportunities pertaining to global dietary surveillance. Methods: This investigation provides an extensive review of global dietary assessment methodologies and challenges, including at the survey level, the dietary collection and assessment level, and the dietary data processing and analysis level. The focus is on nationally representative individual-level data, and additional types of dietary data, such as dietary biomarkers, household assessment, and food availability, are reviewed as alternatives. Practical guidance is provided to inform key decisions when designing dietary surveys and collecting, analyzing, and using dietary data. This article further identifies and describes existing global and regional dietary initiatives/data sets. Results: Harmonized and standardized primary individual-level dietary data collection, processing, and analysis worldwide are currently not available. Evaluation and decision-making should be based on best available data, that is, secondary nonharmonized yet to the extent possible, standardized individual-level dietary data. Existing initiatives differ substantially in methodologies, including survey design/representativeness, coverage, diet assessment, and dietary metric standardization and processing. Data gaps have been identified that were more profound for certain countries, certain dietary indicators across countries, population subgroups, representativeness, or time periods. Conclusions: Optimizing worldwide dietary habits to improve population health requires systematically identified and evaluated data on a continuing basis. Leveraging existing available dietary data and efforts is an indispensable prerequisite for informed priority setting targeting the intersections between diet and disease.

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23

de Sousa, Rita, Chantal Reusken, and Marion Koopmans. "MERS coronavirus: Data gaps for laboratory preparedness." Journal of Clinical Virology 59, no. 1 (January 2014): 4–11. http://dx.doi.org/10.1016/j.jcv.2013.10.030.

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24

Chiarello, Filippo, Elena Coli, Vito Giordano, Gualtiero Fantoni, and Andrea Bonaccorsi. "DATA FOR ENGINEERING DESIGN: MAPS AND GAPS." Proceedings of the Design Society 1 (July 27, 2021): 821–30. http://dx.doi.org/10.1017/pds.2021.82.

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AbstractData, information and knowledge are strongly involved in Engineering Design (ED) process. Despite the crucial role played by data in the design process, there is a lack of studies about how different data are used and generated by the various phases of the ED process. This study is a first attempt to fill this gap by mapping which data types are involved in the different ED phases from a research perspective.In order to achieve this objective, we used a methodology based on Text Mining. Firstly, we retrieve a corpus of scientific papers related to ED; then, we build two lexicons to recognize ED phases and data types; finally, we collect these entities within ED papers and map the relations between them.The methodology application allows the building of a network graph for visualizing the relations among data lexicon and ED lexicon. Then, we investigate the specific relations among data types and ED phases by building a heatmap to investigate data types from 3 different perspective.The insight coming from our analysis shows that ED studies have a great potential in the usage of many data sources, but also that there exist some gaps to be solved in order to reach a more effective data usage in the context of ED.

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25

Durkin, Allison, Brandon Willmore, Caroline Nobo Sarnoff, and David Hemenway. "The Firearms Data Gap." Journal of Law, Medicine & Ethics 48, S4 (2020): 32–38. http://dx.doi.org/10.1177/1073110520979399.

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The firearms data infrastructure in the United States is severely limited in scope and fragmented in nature. Improved data systems are needed in order to address gun violence and promote productive conversation about gun policy. In the absence of federal leadership in firearms data systems improvement, motivated states may take proactive steps to stitch gaps in data systems. We propose that states evaluate the gaps in their systems, expand data collection, and improve data presentation and availability.

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26

Kang, Minseok, Kazuhito Ichii, Joon Kim, Yohana M. Indrawati, Juhan Park, Minkyu Moon, Jong-Hwan Lim, and Jung-Hwa Chun. "New Gap-Filling Strategies for Long-Period Flux Data Gaps Using a Data-Driven Approach." Atmosphere 10, no. 10 (September 22, 2019): 568. http://dx.doi.org/10.3390/atmos10100568.

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In the Korea Flux Monitoring Network, Haenam Farmland has the longest record of carbon/water/energy flux measurements produced using the eddy covariance (EC) technique. Unfortunately, there are long gaps (i.e., gaps longer than 30 days), particularly in 2007 and 2014, which hinder attempts to analyze these decade-long time-series data. The open source and standardized gap-filling methods are impractical for such long gaps. The data-driven approach using machine learning and remote-sensing or reanalysis data (i.e., interpolating/extrapolating EC measurements via available networks temporally/spatially) for estimating terrestrial CO2/H2O fluxes at the regional/global scale is applicable after appropriate modifications. In this study, we evaluated the applicability of the data-driven approach for filling long gaps in flux data (i.e., gross primary production, ecosystem respiration, net ecosystem exchange, and evapotranspiration). We found that using a longer training dataset in the machine learning generally produced better model performance, although there was a greater possibility of missing interannual variations caused by ecosystem state changes (e.g., changes in crop variety). Based on the results, we proposed gap-filling strategies for long-period flux data gaps and used them to quantify the annual sums with uncertainties in 2007 and 2014. The results from this study have broad implications for long-period gap-filling at other sites, and for the estimation of regional/global CO2/H2O fluxes using a data-driven approach.

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27

Lompar, Miloš, Branislava Lalić, Ljiljana Dekić, and Mina Petrić. "Filling Gaps in Hourly Air Temperature Data Using Debiased ERA5 Data." Atmosphere 10, no. 1 (January 4, 2019): 13. http://dx.doi.org/10.3390/atmos10010013.

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Missing data in hourly and daily temperature data series is a common problem in long-term data series and many observational networks. Agricultural and environmental models and climate-related tools can be used only if weather data series are complete. To support user communities, a technique for gap filling is developed based on the debiasing of ERA5 reanalysis data, the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses of the global climate. The debiasing procedure includes in situ measured temperature. The methodology is tested for different landscapes, latitudes, and altitudes, including tropical and midlatitudes. An evaluation of results in terms of root mean square error (RMSE) obtained using hourly and daily data is provided. The study shows very low average RMSE for all gap lengths ranging from 1.1 °C (Montecristo, Italy) to 1.9 °C (Gumpenstein, Austria).

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28

Popper, Arthur N. "Man-made noise and aquatic Life: data, data gaps, and speculation." Journal of the Acoustical Society of America 137, no. 4 (April 2015): 2245. http://dx.doi.org/10.1121/1.4920191.

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29

Machado, Alexandre M. S., Eduardo L. Hettwer Giehl, Luiza Pacheco Fernandes, Simon N. Ingram, and Fábio G. Daura-Jorge. "Alternative data sources can fill the gaps in data-poor fisheries." ICES Journal of Marine Science 78, no. 5 (April 16, 2021): 1663–71. http://dx.doi.org/10.1093/icesjms/fsab074.

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Abstract Assessing fish stocks harvested by small-scale fisheries is challenging. The lack of official fisheries data constrains the proper management of such fisheries. Thus, alternative sources of information are crucial to enrich data-poor fisheries. Here, we evaluated different sources of data for the mullet (Mugil liza) fishery, one of the most important but overexploited fisheries in Brazil. We gathered three alternative sources of catch data by artisanal fisheries: 14 years of self-reported catches by artisanal fishers across 24 municipalities; 16 years of catches by traditional beach seines mined from news outlets; and 13 years from a single community monitoring their beach seine catches. We tested whether alternative data sources follow the same trends of landing reports from systematic, official monitoring of the industrial fleet. We fitted Bayesian time-series models to test if environmental changes and stock abundance can predict these data. We found that only self-reported catches matched the official reporting trends, thereby improving our understanding of changes in the mullet stock. These findings reveal that self-reported catches by fishers provide reliable additional data useful for management. Self-reporting data are cost-effective, deals with the complexity of small-scale fisheries, and welcomes fishers as key stakeholders in management practices.

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30

Touaa, Z., and Nadir Sekkal. "Crystallographic input data for (001)-, (110)- and (111)-oriented superlattices." Acta Crystallographica Section B Structural Science 68, no. 4 (July 17, 2012): 378–88. http://dx.doi.org/10.1107/s0108768112030091.

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General aspects concerned with (001)-, (110)- and (111)-oriented superlattices (SLs) have been investigated. In particular, the symmetry of these systems have been derived and given in detail. As a test, the obtained data have been utilized to calculate electronic structures and gaps of a standard GaAs/AlAs system using an accurate version of the first principle full potential linear muffin-tin orbital (FPLMTO) method based on a local-density functional approximation (LDA).

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31

Caruso, Bethany A., and Sheela S. Sinharoy. "Gender data gaps represent missed opportunities in WASH." Lancet Global Health 7, no. 12 (December 2019): e1617. http://dx.doi.org/10.1016/s2214-109x(19)30449-8.

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32

Etard, Adrienne, Sophie Morrill, and Tim Newbold. "Global gaps in trait data for terrestrial vertebrates." Global Ecology and Biogeography 29, no. 12 (October 11, 2020): 2143–58. http://dx.doi.org/10.1111/geb.13184.

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33

Grainger, A. "Measuring the planet to fill terrestrial data gaps." Proceedings of the National Academy of Sciences 106, no. 49 (December 1, 2009): 20557–58. http://dx.doi.org/10.1073/pnas.0912298107.

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Tymkiv, Mariia, and Dmytro Kasiyanchuk. "Research of Data Sequencesof Groundwater Levels with Gaps." Journal of Ecological Engineering 20, no. 3 (March 1, 2019): 141–51. http://dx.doi.org/10.12911/22998993/99744.

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Newquist, Deborah D., Marguerite DeLiema, and Kathleen H. Wilber. "Beware of Data Gaps in Home Care Research." Medical Care Research and Review 72, no. 5 (June 10, 2015): 622–40. http://dx.doi.org/10.1177/1077558715588437.

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36

Voors, Adriaan A. "Trial data resolve gaps in evidence-based treatment." Nature Reviews Cardiology 10, no. 2 (January 8, 2013): 67–68. http://dx.doi.org/10.1038/nrcardio.2012.190.

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Nelson, Andy. "Crop-health survey aims to fill data gaps." Nature 541, no. 7638 (January 2017): 464. http://dx.doi.org/10.1038/541464a.

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Tollefson, Jeff. "Satellite images reveal gaps in global population data." Nature 545, no. 7653 (May 2017): 141–42. http://dx.doi.org/10.1038/545141a.

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Girardello, Marco, Anna Chapman, Roger Dennis, Lauri Kaila, Paulo A. V. Borges, and Andrea Santangeli. "Gaps in butterfly inventory data: A global analysis." Biological Conservation 236 (August 2019): 289–95. http://dx.doi.org/10.1016/j.biocon.2019.05.053.

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40

Candelas, G., L. Silva-Fernández, M. Montoro, A. Hernández, J. R. Maneiro, V. Villaverde, L. Carmona, et al. "FRI0071 ANALYSIS OF DATA GAPS IN RHEUMATOID ARTHRITIS." Annals of the Rheumatic Diseases 79, Suppl 1 (June 2020): 612.2–612. http://dx.doi.org/10.1136/annrheumdis-2020-eular.6574.

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Background:Although ideally Recommendations for the management of rheumatoid arthritis (RA) should be supported by the highest level of evidence, many of which are based on “expert opinion”. This means that there are knowledge gaps to which a part of the research efforts in this disease should be directed.Objectives:1.- Analyze the causes of the low level of evidence in some of the recommendations on diagnosis and management of RA in the main published documents2.- Identify the knowledge gaps that justify said low level of evidence3.- Design actions to respond to the knowledge gaps identified.Methods:Qualitative study. A group of six experts in systematic review of the literature was selected. Fourteen documents of national and international recommendations on RA (EULAR, ACR and SER) of the last 5 years were analyzed by a peer review. They selected recommendations with low level of evidence (Oxford 4 and 5) / grade of recommendation (C and D), and classified by areas (diagnosis, monitoring, treatment, others) and then possible causes of low level of evidence were analyzed. These were submitted to a Delphi to select the 10 recommendations in which participants considered it more critical to obtain quality evidence. Subsequently, actions were proposed to improve the levels of evidence in general and, through the PICOS structure (population, intervention, comparator, study design) specific studies were proposed to respond to the issues raised in these 10 recommendationsResults:185 recommendations were found that had a low level of evidence / grade of recommendation, most related to the treatment of RA. The two most frequent causes of this low level of evidence and / or the degree of recommendation were the absence of studies and an incorrect classification of the level of evidence and / or degree of recommendation. In addition, other reasons and methodological barriers were found for which nine critical recommendations were finally selected for which new PICOs were developed with which to propose targeted research projectsConclusion:It is necessary to improve the methodological approach in the RA recommendations guidelines to correct errors and fill gaps with appropriate studies.Table 1.Actions to increase the level of evidence / recommendation.#Action1Prioritization of research towards knowledge gaps with the design and development of specific studies2Increase knowledge of experts in the methodology of consensus documents (including RSL, formulation of recommendations, etc.)3Supervision of the entire process by expert methodologists, to ensure a correct allocation of the levels of evidence and degree of recommendation4Review and select those topics that are really of interest and should be reviewed and can be answered5Expert opinion should never become a recommendation, but will be included in the text that accompanies that recommendation.6Clear syntax will be used and short recommendations will be made7Establishment and application of homogeneous criteria to formulate recommendationsKey words: Rheumatoid arthritis, recommendations, data gapsDisclosure of Interests:gloria candelas: None declared, Lucía Silva-Fernández: None declared, Maria Montoro Employee of: Pfizer employee, Abad Hernández: None declared, Jose Ramón Maneiro: None declared, Virginia Villaverde: None declared, Loreto Carmona Grant/research support from: Novartis Farmaceutica, SA, Pfizer, S.L.U., Merck Sharp & Dohme España, S.A., Roche Farma, S.A, Sanofi Aventis, AbbVie Spain, S.L.U., and Laboratorios Gebro Pharma, SA (All trhough institution), Estíbaliz Loza Grant/research support from: Roche, Pfizer, Abbvie, MSD, Novartis, Gebro, Adacap, Astellas, BMS, Lylly, Sanofi, Eisai, Leo, Sobi, Susana Gómez Employee of: Pfizer employee, Monica Valderrama Consultant of: Pfizer employee, Ana Ortiz: None declared

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Howe, David A., and Noah Schlossberger. "Characterizing Frequency Stability Measurements Having Multiple Data Gaps." IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 69, no. 2 (February 2022): 468–72. http://dx.doi.org/10.1109/tuffc.2021.3137425.

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42

Poisot, Timothée, Gabriel Bergeron, Kevin Cazelles, Tad Dallas, Dominique Gravel, Andrew MacDonald, Benjamin Mercier, Clément Violet, Steve Vissault, and Daniel Chapman. "Global knowledge gaps in species interaction networks data." Journal of Biogeography 48, no. 7 (April 29, 2021): 1552–63. http://dx.doi.org/10.1111/jbi.14127.

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43

Romero, H. A., L. T. Ostrom, and C. A. Wilhelmsen. "What Difference Can the Data Make?" Proceedings of the Human Factors and Ergonomics Society Annual Meeting 37, no. 10 (October 1993): 841–45. http://dx.doi.org/10.1177/154193129303701041.

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The anthropometric data which is readily available to the ergonomic practitioner contains gaps in the statures of individuals covered. This study fills in those gaps by analyzing the data sources available to interpolate the dimensions for those statures not represented. The interpolation method used was linear regression relating a specific dimension to the standing stature of the individual. Additionally, this paper compares several sources of data to demonstrate significant differences. Both these pieces of information are important to the practitioner. By filling in the gaps, the practitioner is provided with initial quantitative reference points for individuals when properly arranging a workstation. Currently, only qualitative information is provided concerning optimum workstation design for individuals not represented in the data sources. By demonstrating the lack of a significant difference between data sources, the practitioner may use whichever source is readily available. The results show a significant relationship between the individual's stature and eight different workstation measurements. Finally, there is no significant difference between the data sources examined.

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44

Noskov, Sergey I. "CONSTRUCTION OF EXPERT-STATISTICAL MODELS FROM INCOMPLETE DATA." T-Comm 15, no. 6 (2021): 33–39. http://dx.doi.org/10.36724/2072-8735-2021-15-6-33-39.

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The article deals with the problem of constructing a linear regression model based on incomplete data containing gaps, using statistical and expert information. The reasons for the gaps in the data can be, in particular, a temporary malfunction (failure) of the measuring equipment when taking various technical characteristics, or negligence in the work of statistical services when fixing the reporting indicators. Very often, gaps arise when processing various kinds of sociological information in the form of questionnaires, when respondents refuse to answer a specific question (but answer others) or give an inadmissible, in particular, evasive answer. The approach proposed in the work involves filling the gaps with intervals, the boundaries of which are formed by experts, guided by both their experience and knowledge about the object of research, and using the well-known methods of point filling in the gaps. After that, the estimation of the parameters of the model, depending on the nature of the initial uncertainty in the data, is reduced to solving problems of linear or partially Boolean linear programming. The case is considered when the solution of the formalizing uncertainty in the initial data of the interval system of linear algebraic equations is not unique. The problem of constructing a linear regression equation for the influence of the volume of export of large-tonnage containers and the freight turnover of the PRC railway transport on the volume of import of large-capacity containers at the Zabaikalsk-Manchuria railway checkpoint is solved.

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45

Ren, Chang, Luliang Tang, Jed Long, Zihan Kan, and Xue Yang. "Modelling Place Visit Probability Sequences during Trajectory Data Gaps Based on Movement History." ISPRS International Journal of Geo-Information 10, no. 7 (July 2, 2021): 456. http://dx.doi.org/10.3390/ijgi10070456.

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The acquisition of human trajectories facilitates movement data analytics and location-based services, but gaps in trajectories limit the extent in which many tracking datasets can be utilized. We present a model to estimate place visit probabilities at time points within a gap, based on empirical mobility patterns derived from past trajectories. Different from previous models, our model makes use of prior information from historical data to build a chain of empirically biased random walks. Therefore, it is applicable to gaps of varied lengths and can be fitted to empirical data conveniently. In this model, long gaps are broken into a chain of multiple episodes according to past patterns, while short episodes are estimated with anisotropic location transition probabilities. Experiments show that our model is able to hit almost 60% of the ground truth for short gaps of several minutes and over 40% for longer gaps up to weeks. In comparison, existing models are only able to hit less than 10% and 1% for short and long gaps, respectively. Visit probability distributions estimated by the model are useful for generating paths in data gaps, and have potential for disaggregated movement data analysis in uncertain environments.

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Badenko, Vladimir, Dmitry Zotov, and Alexander Fedotov. "Hybrid processing of laser scanning data." E3S Web of Conferences 33 (2018): 01047. http://dx.doi.org/10.1051/e3sconf/20183301047.

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In this article the analysis of gaps in processing of raw laser scanning data and results of bridging the gaps discovered on the base of usage of laser scanning data for historic building information modeling is presented. The results of the development of a unified hybrid technology for the processing, storage, access and visualization of combined laser scanning and photography data about historical buildings are analyzed. The first result of the technology application for the historical building of St. Petersburg Polytechnic University shows reliability of the proposed approaches.

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47

Fortune, Nicola, Hannah Badland, Shane Clifton, Eric Emerson, Jerome Rachele, Roger J. Stancliffe, Qingsheng Zhou, and Gwynnyth Llewellyn. "The Disability and Wellbeing Monitoring Framework: data, data gaps, and policy implications." Australian and New Zealand Journal of Public Health 44, no. 3 (April 20, 2020): 227–32. http://dx.doi.org/10.1111/1753-6405.12983.

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48

Bedard, Kelly, Maxine Lee, and Heather Royer. "Using Longitudinal Data to Explore the Gender Gap for Academic Economists." AEA Papers and Proceedings 111 (May 1, 2021): 69–73. http://dx.doi.org/10.1257/pandp.20211087.

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There are widespread gender inequities within the economics discipline. In this paper, we collect and analyze new longitudinal salary and personnel data from top economics departments at public institutions. A panel spanning 2005 to 2018 allows us to follow individuals and facilitates the examination of gender gaps in career progression, salary growth, and mobility. Using these data, we document the growth of salary gender gaps with the length of time in the profession, emerging roughly 10 years after the start of one's career. Some of these gaps are attributable to women moving through academic ranks more slowly than men.

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Abdel Hakim, Alaa E., and Wael Deabes. "Can People Really Do Nothing? Handling Annotation Gaps in ADL Sensor Data." Algorithms 12, no. 10 (October 17, 2019): 217. http://dx.doi.org/10.3390/a12100217.

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In supervised Activities of Daily Living (ADL) recognition systems, annotating collected sensor readings is an essential, yet exhaustive, task. Readings are collected from activity-monitoring sensors in a 24/7 manner. The size of the produced dataset is so huge that it is almost impossible for a human annotator to give a certain label to every single instance in the dataset. This results in annotation gaps in the input data to the adopting learning system. The performance of the recognition system is negatively affected by these gaps. In this work, we propose and investigate three different paradigms to handle these gaps. In the first paradigm, the gaps are taken out by dropping all unlabeled readings. A single “Unknown” or “Do-Nothing” label is given to the unlabeled readings within the operation of the second paradigm. The last paradigm handles these gaps by giving every set of them a unique label identifying the encapsulating certain labels. Also, we propose a semantic preprocessing method of annotation gaps by constructing a hybrid combination of some of these paradigms for further performance improvement. The performance of the proposed three paradigms and their hybrid combination is evaluated using an ADL benchmark dataset containing more than 2.5 × 10 6 sensor readings that had been collected over more than nine months. The evaluation results emphasize the performance contrast under the operation of each paradigm and support a specific gap handling approach for better performance.

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Dang, Hai‐Anh, Dean Jolliffe, and Calogero Carletto. "DATA GAPS, DATA INCOMPARABILITY, AND DATA IMPUTATION: A REVIEW OF POVERTY MEASUREMENT METHODS FOR DATA‐SCARCE ENVIRONMENTS." Journal of Economic Surveys 33, no. 3 (January 29, 2019): 757–97. http://dx.doi.org/10.1111/joes.12307.

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