Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
10,929
datasets available to search
ShareScore release 0.7.1
Dataset results
10,929 results for “Communities”
COSI-Article matrix: linking ISCB Communities of Special Interest to Wikipedia
<p>Wikipedia is regarded as one of the most important channels for the public communication of science; English Wikipedia has around 1,500 articles relating to computational biology, which are frequently accessed as an educational resource. Joint efforts between the International Society for Computational Biology (ISCB) and the Computational Biology taskforce of WikiProject Molecular Biology (a group of expert Wikipedia editors) have considerably improved computational biology representation on Wikipedia in recent years. However, there is still an urgent need for further quality improvement, primarily while comparing to related scientific fields such as genetics and medicine. Facilitating the involvement of members from ISCB COSIs (Communities of Special Interest) would improve a vital open educational resource in computational biology, additionally allowing COSIs to provide a quality educational resource particular to their subfield.</p> <p>This first version of the COSI-Article matrix is a binary matrix identifying relevant ISCB COSIs for all Wikipedia articles relating to computational biology, defining a domain-specific open educational resource for each COSI. In addition, quality and importance ratings for each article allow identification of areas where domain experts could improve computational biology representation.</p>
NC Community College President Data Set
<p>NCCCPDS. A working data set of presidents serving in North Carolina community colleges from the mid-1960s. Includes name, year, college, gender identity, degree, degree university, and field. Data were retrieved from publicly available documents including course catalogs, newspapers, obituaries, and university alumni records.</p>
Simulation Data for "Community-Driven Code Comparisons for Three-Dimensional Dynamic Modeling of Sequences of Earthquakes and Aseismic Slip"
<p>Simulation data from Jiang et al. (2022), "Community-Driven Code Comparisons for Three-Dimensional Dynamic Modeling of Sequences of Earthquakes and Aseismic Slip," <em>Journal of Geophysical Research: Solid Earth</em><em>.</em></p> <p>The archive includes simulation data for 3D SEAS benchmarks BP4-QD and BP5-QD that are analyzed in our paper (descriptions in NOTES.txt) </p> <p><strong>BP4-QD Benchmark Simulations:</strong><br>1000 m: jiang.5, lambert.8, barbot.3, barbot.2, dliu.2, li.4<br>500 m: jiang.3, lambert.3, barbot.5, barbot.7, ozawa</p> <p><strong>BP5-QD Benchmark Simulations:</strong><br>2000 m: jiang.6, lambert.8, liu.4, cattania.5, dli.7, barbot.3, dliu.10, li.3<br>1000 m: jiang.2, lambert.7, liu.5, cattania.3, ozawa, dli.5, barbot, dliu.6, li.2<br>500 m: jiang.4, lambert.9, liu.6, cattania.4, ozawa.2, dli.6, barbot.2, dliu.8<br>250 m: lambert.10, liu.7</p> <p><strong>BP5-QD with Off-Fault Data:</strong><br>1000 m: lambert.7, dli.5, barbot, dliu.6, li.2<br>500 m: lambert.9, dli.6, barbot.2, dliu.8</p> <p>Tables 2–4 in our paper summarizes details of numerical codes and selected simulations.</p> <p>The benchmark descriptions and the full suite of simulation data are available at SEAS online platform https://strike.scec.org/cvws/seas/.</p>
Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey - Extended Data 3 - Raw Data survey entries
<p>This document provides extended, supplementary data and information to the manuscript "Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey" by Schmidt C., Hanne J, Moore J, Meesters C, Ferrando-May E, Weidtkamp-Peters S, and members of the NFDI4BIOIMAGE initiative. [version 1; peer review: awaiting peer review] F1000Research 2022, 11:638, https://doi.org/10.12688/f1000research.121714.1</p> <p>This extended data includes:</p> <p>- The raw dataset of survey entries, anonymized (IP addresses and personal comments deleted)</p>
Community Established Best Practice Recommendations for Tephra Studies-from Collection through Analysis
<p>Tephra is a unique volcanic product with an unparalleled role in understanding past eruptions, long-term behavior of volcanoes, and the effects of volcanism on climate and the environment. Tephra deposits also provide spatially widespread, extremely high-resolution time-stratigraphic markers across a range of sedimentary settings and are used in a range of disciplines (e.g., volcanology, climate science, archaeology, ecology, and impact assessment). Nonetheless, the study of tephra deposits is challenged by a lack of standardization that often inhibits data integration across geographic regions and across disciplines.</p> <p>Here we present comprehensive recommendations for tephra data gathering and reporting that were developed by the tephra science community to serve as guidelines for future investigators and to ensure that sufficient data are gathered for transparency and interoperability. Recommendations include standardized field and laboratory data collection along with reporting and correlation guidance. These are organized as tabulated lists of key metadata with their definition and purpose. They are system independent and usable for template, tool, and database development. This new standardized framework promotes consistent tephra documentation and archiving, fosters interdisciplinary communication, and improves effectiveness of data sharing among diverse communities of researchers. Wider adoption will help to expand the applicability and usability of tephra data and facilitate scientific collaboration and data reuse.</p> <p>For additional details, see the accompanying manuscript:</p> <p>Wallace, K.*, Bursik, M. Kuehn, S., Kurbatov, A., Abbott, P., Bonadonna, C., Cashman, K., Davies, S., Jensen, B., Lane, C., Plunkett, G., Smith, V. Tomlinson, E., Thordarsson, T., and Walker, D. Community established best practice recommendations for tephra studies—from collection through analysis. <em>Sci Data</em> <strong>9, </strong>447 (2022). <a href="https://doi.org/10.1038/s41597-022-01515-y">https://doi.org/10.1038/s41597-022-01515-y</a></p> <p>*corresponding author: Kristi Wallace, <a href="mailto:kwallace@usgs.gov">kwallace@usgs.gov</a></p> <p>Open access article is available online here <a href="https://doi.org/10.1038/s41597-022-01515-y">https://doi.org/10.1038/s41597-022-01515-y</a> or as a PDF here <a href="https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41597-022-01515-y.pdf&data=05%7C01%7Ckwallace%40usgs.gov%7C673f9f39fd3e4122dd9b08da6f3b9667%7C0693b5ba4b184d7b9341f32f400a5494%7C0%7C0%7C637944598967375940%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=%2BKVfwK2FbUKAoJf2gMerCmBMEQE1rvMDkS6xIk3DGKY%3D&reserved=0">https://www.nature.com/articles/s41597-022-01515-y.pdf</a>.</p>
Supporting data for "Estimating animal density for a community of species using information obtained only from camera-traps"
<p>Data underlying a paper published in Methods in Ecology and Evolution (<a href="https://doi.org/10.1111/2041-210X.13930">https://doi.org/10.1111/2041-210X.13930</a>).</p> <p>These data are suitable for estimating animal density using the Random Encounter Model and include: i) detection counts for 35 species across 510 camera-trap locations; ii) movement speeds (estimated by tracking animal movements in camera-trap image sequences), iii) activity times (filtered so that records of the same species at the same location are > 60 minutes apart), and iv) measurements of the angular and radial distance from camera-traps for animals that were detected.</p>
Open Science and Authorship of Supplementary Material for the MES research community
<p>This spreadsheet contains the data and the results from the analysis described in the paper "Open Science and Authorship of Supplementary Material. Evidence from a Research Community." being accepted at STI 2022.</p>
Testing a biological mechanism of the insurancehypothesis in experimental aquatic communities - Data Deposit
<p>1.The insurance hypothesis predicts a stabilizing effect of increasing species richness on commu-nity and ecosystem properties. Difference among species’ responses to environmental fluctuationsprovides a general mechanism for the hypothesis. Previous experimental investigations of theinsurance hypothesis have not examined this mechanism directly.</p> <p>2.First, responses to temperature of four protist species were measured in laboratory microcosms.For each species, we measured the response of intrinsic rate of increase (r) and carrying capacity(K) to temperature.</p> <p>3.Next, communities containing pairs of species were exposed to temperature fluctuations. Com-munity biomass varied less when correlation inKbetween species (but notr) was more negative,and this resulted from more negative covariances in population sizes, as predicted. Results werecontingent on species identity, with findings differing between analyses including or not includingcommunities containing one particular species.</p> <p>4.These findings provide the clearest support to date for this mechanism of the insurance hypo-thesis. Biodiversity, in terms of differences in species’ responses to environmental fluctuations (i.e.functional response diversity) stabilizes community dynamics.</p>
D1.2 Requirements and needs of scientific communities from ICT-based Research Infrastructures (Dataset)
<p>A user survey was conducted between December 2020 and January 2021 gathering inputs from potential SLICES users from the research community. The survey was distributed among the research community to identify the technological domains, the use cases, the requirements and other expectations from the future users of the SLICES research infrastructure. This dataset contains the results of the survey; 226 people participated.</p>
ALS-based DEMs (100 cm): the dynamics of the prehistoric communities located in the Mostiștea Valley and Danube Plain (between Oltenița and Călărași)
<p>This dataset is part of a larger project on the dynamics of the prehistoric communities located in the Mostiștea Valley and Danube Plain (between Oltenița and Călărași), supervised by the ArchaeoSciences Division of the Research Institute of the University of Bucharest (ICUB) and Kiel University (Germany), in partnership with HOGENT, University of Applied Sciences and Arts (Belgium), Museum of Bucharest, Museum of the Lower Danube Călărași, Museum of Gumelnița Civilization Oltenița, and "Vasile Pârvan" Institute of Archaeology (Romania), under the "Sultana School of Archaeology" initiative.</p> <p>Spatial data play a crucial role in archaeological research, and orthophotos, digital elevation models, and 3D models are frequently used for the mapping, documentation, and monitoring of archaeological sites. Thanks to the availability of compact and low-cost uncrewed airborne vehicles, the use of UAV-based photogrammetry is well matured in this field over the last two decades. More recently, compact airborne systems are also available that allow the recording of thermal data, multispectral data, and airborne laser scanning. For this project, various platforms and sensors are applied at the Chalcolithic archaeological sites in the Mostiștea Basin and Danube Valley (Southern Romania). By analyzing the performance of the systems and the resulting data, insight is given into the selection of the appropriate system for the right application. This analysis requires thorough knowledge of data acquisition and data processing as well. As both laser scanning and photogrammetry typically result in very large amounts of data, a special focus is also required on the storage and publication of the data. Hence, the objective of this project is to provide a full overview of various aspects of 3D data acquisition for UAV-based mapping. Based on the conclusions drawn in our related publications, it is stated that photogrammetry and laser scanning can result in data with similar geometrical properties when acquisition parameters are appropriately set. On the one hand, however, the used ALS-based system outperforms the photogrammetric platforms in terms of operational time and the area covered. On the other hand, conventional photogrammetry provides flexibility that might be required for very low-altitude flights, or emergency mapping. Furthermore, as the used ALS sensor only provides a geometrical representation of the topography, photogrammetric sensors are still required to obtain true color- or false color composites of the surface. Lastly, the variety of data, like pre- and post-rendered raster data, 3D models, and point clouds, requires the implementation of multiple methods for the online publication of data. Various client-side and server-side solutions are presented to make the data available for other researchers.</p>
MS-based orthophotos (50 cm): the dynamics of the prehistoric communities located in the Mostiștea Valley and Danube Plain (between Oltenița and Călărași)
<p>This dataset is part of a larger project on the dynamics of the prehistoric communities located in the Mostiștea Valley and Danube Plain (between Oltenița and Călărași), supervised by the ArchaeoSciences Division of the Research Institute of the University of Bucharest (ICUB) and Kiel University (Germany), in partnership with HOGENT, University of Applied Sciences and Arts (Belgium), Museum of Bucharest, Museum of the Lower Danube Călărași, Museum of Gumelnița Civilization Oltenița, and "Vasile Pârvan" Institute of Archaeology (Romania), under the "Sultana School of Archaeology" initiative.</p> <p>Spatial data play a crucial role in archaeological research, and orthophotos, digital elevation models, and 3D models are frequently used for the mapping, documentation, and monitoring of archaeological sites. Thanks to the availability of compact and low-cost uncrewed airborne vehicles, the use of UAV-based photogrammetry is well matured in this field over the last two decades. More recently, compact airborne systems are also available that allow the recording of thermal data, multispectral data, and airborne laser scanning. For this project, various platforms and sensors are applied at the Chalcolithic archaeological sites in the Mostiștea Basin and Danube Valley (Southern Romania). By analyzing the performance of the systems and the resulting data, insight is given into the selection of the appropriate system for the right application. This analysis requires thorough knowledge of data acquisition and data processing as well. As both laser scanning and photogrammetry typically result in very large amounts of data, a special focus is also required on the storage and publication of the data. Hence, the objective of this project is to provide a full overview of various aspects of 3D data acquisition for UAV-based mapping. Based on the conclusions drawn in our related publications, it is stated that photogrammetry and laser scanning can result in data with similar geometrical properties when acquisition parameters are appropriately set. On the one hand, however, the used ALS-based system outperforms the photogrammetric platforms in terms of operational time and the area covered. On the other hand, conventional photogrammetry provides flexibility that might be required for very low-altitude flights, or emergency mapping. Furthermore, as the used ALS sensor only provides a geometrical representation of the topography, photogrammetric sensors are still required to obtain true color- or false color composites of the surface. Lastly, the variety of data, like pre- and post-rendered raster data, 3D models, and point clouds, requires the implementation of multiple methods for the online publication of data. Various client-side and server-side solutions are presented to make the data available for other researchers.</p>
Perceptions on the utility of community question and answer websites like Stack Overflow to software developers (Replication package)
<p>Interview Questions on the perception of the utility of CQAs like Stack Overflow to software developers. In this study, we focused on the questions highlighted in yellow.</p>
dataset: Responses of the structure and function of the understory plant communities to precipitation reduction across forest ecosystems in Germany
<p><strong>Context</strong>: Understory plant communities play a central role in forest biogeochemistry and the recruitment of trees making up the future forest. It is so far poorly understood how climate change will affect understory structure and functions in forest of different management intensity.</p> <p> </p><p><strong>Aims</strong>: We monitored understory functional traits including transpiration and carbon isotope discrimination, community structure and diversity during two growing seasons as affected by drought in forests subjected to different management intensities. We hypothesized that drought would affect ecophysiological traits such as transpiration but not species richness and diversity. Moreover, we assumed that stand-specific characteristics and forest management intensity modify the drought-resistance of the understory community.</p> <p></p> <p><strong>Methods</strong>: We set up roofs in beech and conifer stands with different management intensity in three different regions across Germany and a drought event close to the 2003 drought was imposed in two consecutive years.</p> <p><strong>Results</strong>: Precipitation reduction decreased soil water content by 2 to 8%, depending on stand and region, in comparison to the control subplots. In the first year, leaf level transpiration was reduced for different functional groups, which scaled to community transpiration modified by additional effects of drought on functional group specific leaf area. Acclimation effects in most functional groups were observed in the second year. We did not observe a significant reduction of plant diversity or a consistent management effect upon drought.</p> <p><strong>Conclusion</strong>: Our results indicate high plasticity and acclimation responses of the forest understory vegetation to changing climate conditions and recurrent drought events.</p> <p><strong>Abbreviations:</strong></p> <p>sp12 - campaign spring 2012; ls12 - campaign late summer 2012; es13 - campaign early summer 2013; ls13-campaign late summer 2013</p> <p>SEW16 - Schorfheide plot 16; SEW49 - Schorfheide plot 49; SEW48 - Schorfheide plot 48;HEW03 - Hainich plot 03; HEW12 - Hainich plot 12; HEW47- Hainich plot 47; AEW13 - Alb plot 13; AEW29 - Alb plot 29; AEW08 - Alb plot 08<br> explo - exploratory<br> SEW - Schorfheide; HEW - Hainich; AEW - Schwäbische Alb<br> in - conifer intensive managed; ma - beech managed; un - beech unmanaged<br> c- control; r - roof<br> LAIs - community leaf area index m<sup>2</sup>/m<sup>2</sup>; H - Shannon´s diversity index; Ts - community transpiration rate (weighted by LAI) mmol H<sub>2</sub>O m-<sup>2</sup> leaf area s-<sup>1</sup>; Ets - Evapotranspiration (mmol/m2/sec); E - Evaporation (mmol/m2/sec); C - leaf photosynthetic carbon isotope discrimination (∆<sup>13</sup>C) according to Farquhar et al. (1982); Cs - community photosynthetic carbon isotope discrimination (∆<sup>13</sup>C) according to Farquhar et al. (1982) (weighted by LAI)</p> <p> </p>
Soil Bacteria Community-Weighted rrn Operon Copy Number Estimation
<p>Datasets and R-Scripts for estimating community-weighted rrn operon copy number for soil bacteria communities collected from the Yukon-Kuskokwim River Delta, AK, USA, and from La Selva Biological Station, Costa Rica. File descriptions follow:</p> <p>"rrnDB_copy_number_database.csv": The Ribosomal RNA Database downloaded from <a href="rrndb.umms.med.umich.edu.">rrndb.umms.med.umich.edu.</a> Citation: </p> <ul> <li>Stoddard S.F, Smith B.J., Hein R., Roller B.R.K. and Schmidt T.M. (2015) <em>rrn</em>DB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. <em>Nucleic Acids Research</em> 2014; doi: 10.1093/nar/gku1201 [<a href="http://www.ncbi.nlm.nih.gov/pubmed/25414355">PMID:25414355</a></li> </ul> <p>"AK_16S_Genus_Abundance.csv": Count of ASVs by taxon (assigned to genus level) present in each soil sample collected in the Yukon_Kuskokwim River Delta, AK, USA.</p> <p>"Costa_Rica_16S_OTU_Abundance": Count of OTUs by taxon present in each soil sample collected in La Selva Biological Station, Costa Rica.</p> <p>"Alaska_rrn_copy_number_estimation_script.R": an R script for processing Alaska ASV count table and estimating community-weighted rrn operon copy numbers for each soil sample.</p> <p>"CostaRica_rrn_copy_number_estimation_script.R": an R script for processing Costa Rica OTU count table and estimating community-weighted rrn operon copy numbers for each soil sample.</p>
COMPREHENSIVE LIVESTOCK HEALTH PROGRAM: TARGETED TREATMENT AND HOLISTIC INTERVENTIONS FOR MAJOR PREVALENT DISEASES IN THE LIVESTOCK FARMING COMMUNITY OF DAYNILE DISTRICT, MOGADISHU, SOMALIA.
<p>The general objective of this project was to intervene with the most common livestock diseases in Dayniile district by carrying out a comprehensive campaign for treatment and control. The specific objectives consisted of a treatment campaign, improving infrastructure for establishing disinfectant foot dips and hand washing points, providing disinfectant tools, and finalising community engagement and education by doing training at the farm level.<br>The team visited different donors and added their contribution. After collecting sufficient funds from various sources, the team began the procurement of the necessary materials. This included purchasing veterinary drugs and supplies from local pharmacies and other essentials like stationery. The first activity was treatment campaigns, which were a central aspect of the project. Over 290 animals were treated for various diseases and conditions. The farm manager was informed of the diagnoses, and upon receiving their permission, the appropriate treatments were administered. The second intervention action was a vaccination campaign. The team vaccinated a total of 70 animals against clostridial bacteria, which is one of the most common camel diseases encountered in the area. The third intervention was the establishment of biosecurity facilities at select livestock farms. Among all the farms involved in the project, five were chosen for the provision of enhanced biosecurity measures. These measures included the installation of foot dips and teat dips. The fourth activity was educating livestock farmers on strategies for controlling and preventing livestock diseases. The training was held at Beder Camel Dairy Farm and attended by approximately 10 individuals, comprising 3 females and 7 males. The content of the training was three modules: the first was general farm biosecurity, the second was operational biosecurity, and the third was concern for vaccination. Recommendation: We recommend that each farm hire livestock health specialists to easily implement disease prevention steps and promptly solve each new case.<br> We recommend the livestock association, veterinary clinics, and other institutions working on livestock do routine campaigns that facilitate the determination of prevalent diseases and the treatment of those cases</p>
2024.05.08 OCFL Community Meeting
<p>This month's community meeting included:</p> <ul> <li>A demonstration of back up and restore in Islandora by Josh d'Entremont at Acadia University</li> <li>Announcement about an upcoming OCFL Workshop at IPRES</li> </ul> <p>The full notes and the recording can be found at <a href="https://github.com/OCFL/spec/wiki/2024.05.08-Community-Meeting">https://github.com/OCFL/spec/wiki/2024.05.08-Community-Meeting</a>.</p> <p>The next community meeting is Wednesday, June 11th, 8pm EDT / 5pm PDT / Thursday, June 12th, 12am GMT / 1am BST / 10am AEST (<a href="https://dateful.com/convert/australia-sydney?t=10am&d=2024-06-12&tz2=EST-EDT-Eastern-Time">Convert to your timezone</a>).</p>
OSDG Community Dataset (OSDG-CD)
<p><strong>The </strong><strong>OSDG Community Dataset (OSDG-CD)</strong> is a public dataset of thousands of text excerpts, which were validated by over 1,400 <a href="https://osdg.ai/community">OSDG Community Platform (OSDG-CP)</a> citizen scientists from over 140 countries, with respect to the <strong>Sustainable Development Goals</strong> (SDGs).</p> <p> </p> <p><strong>Dataset </strong><strong>I</strong><strong>nformation</strong></p> <p>In support of the global effort to achieve the <a href="https://sdgs.un.org/goals">Sustainable Development Goals (SDGs)</a>, <a href="https://osdg.ai">OSDG</a> is realising a series of SDG-labelled text datasets. The <strong>OSDG Community Dataset (OSDG-CD)</strong> is the direct result of the work of more than 1,400 volunteers from over 130 countries who have contributed to our understanding of SDGs via the <a href="https://osdg.ai/community">OSDG Community Platform (OSDG-CP)</a>. The dataset contains tens of thousands of text excerpts (henceforth: texts) which were validated by the Community volunteers with respect to SDGs. The data can be used to derive insights into the nature of SDGs using either ontology-based or machine learning approaches.</p> <p>📘 The file contains <strong>43,0210</strong> (+390)<strong> text excerpts</strong> and a total of <strong>310,328</strong> (+3,733) <strong>assigned labels</strong>.</p> <p>To learn more about the project, please visit the <a href="https://osdg.ai">OSDG website</a> and the official <a href="https://github.com/osdg-ai">GitHub page</a>. Explore a detailed overview of the OSDG methodology in our recent paper "<a href="https://arxiv.org/abs/2211.11252">OSDG 2.0: a multilingual tool for classifying text data by UN Sustainable Development Goals (SDGs)</a>".</p> <p> </p> <p><strong>Source Data</strong></p> <p>The dataset consists of paragraph-length text excerpts derived from publicly available documents, including reports, policy documents and publication abstracts. A significant number of documents (more than 3,000) originate from UN-related sources such as <a href="https://sdg-pathfinder.org/">SDG-Pathfinder</a> and <a href="https://www.sdgfund.org/library">SDG Library</a>. These sources often contain documents that already have SDG labels associated with them. Each text is comprised of 3 to 6 sentences and is about 90 words on average.</p> <p> </p> <p><strong>Methodology</strong></p> <p>All the texts are evaluated by volunteers on the <strong>OSDG-CP.</strong> The platform is an ambitious attempt to bring together researchers, subject-matter experts and SDG advocates from all around the world to create a large and accurate source of textual information on the SDGs. The Community volunteers use the platform to participate in labelling exercises where they validate each text's relevance to SDGs based on their background knowledge.</p> <p>In each exercise, the volunteer is shown a text together with an SDG label associated with it – this usually comes from the source – and asked to either accept or reject the suggested label.</p> <p><strong>There are 3 types of exercises: </strong></p> <ol> <li>All volunteers start with the <strong>mandatory</strong> <strong>introductory exercise</strong> that consists of 10 pre-selected texts. Each volunteer must complete this exercise before they can access 2 other exercise types. Upon completion, the volunteer reviews the exercise by comparing their answers with the answers of the rest of the Community using aggregated statistics we provide, i.e., the share of those who accepted and rejected the suggested SDG label for each of the 10 texts. This helps the volunteer to get a feel for the platform. </li> <li><strong>SDG-specific exercises</strong> where the volunteer validates texts with respect to a single SDG, e.g., SDG 1 No Poverty. </li> <li><strong>All SDGs exercise</strong> where the volunteer validates a random sequence of texts where each text can have any SDG as its associated label. </li> </ol> <p>After finishing the introductory exercise, the volunteer is free to select either <em>SDG-specific</em> or <em>All SDGs</em> exercises. Each exercise, regardless of its type, consists of 100 texts. Once the exercise is finished, the volunteer can either label more texts or exit the platform. Of course, the volunteer can finish the exercise early. All progress is saved and recorded still. </p> <p>To ensure quality, each text is validated by <strong>up to 9 different volunteers</strong> and all texts included in the public release of the data have been validated by <strong>at least 3 different volunteers</strong>.</p> <p>It is worth keeping in mind that all exercises present the volunteers with a binary decision problem, i.e., either <strong>accept</strong> or <strong>reject</strong> a suggested label. The volunteers are never asked to select one or more SDGs that a certain text might relate to. The rationale behind this set-up is that asking a volunteer to select from 17 SDGs is extremely inefficient. Currently, all texts are validated against only one associated SDG label. </p> <p> </p> <p><strong>Column Description</strong></p> <ul> <li><code>doi</code> - Digital Object Identifier of the original document</li> <li><code>text_id</code> - unique text identifier</li> <li><code>text</code> - text excerpt from the document</li> <li><code>sdg</code> - the SDG the text is validated against</li> <li><code>labels_negative</code> - the number of volunteers who rejected the suggested SDG label</li> <li><code>labels_positive</code> - the number of volunteers who accepted the suggested SDG label</li> <li><code>agreement</code> - agreement score based on the formula \(agreement = \frac{|labels_{positive} - labels_{negative}|}{labels_{positive} + labels_{negative}}\)</li> </ul> <p> </p> <p><strong>Further </strong><strong>I</strong><strong>nformation</strong></p> <p>Do not hesitate to share with us your outputs, be it a research paper, a machine learning model, a blog post, or just an interesting observation. All queries can be directed to community@osdg.ai.</p>
Community science approach reveals temporal and eutrophication-related spatial patterns in bladderwrack-associated invertebrate fauna
<p>Data related to the "Community science approach reveals temporal and eutrophication-related spatial patterns in bladderwrack-associated invertebrate fauna" paper by Salo, Nieminen, Salovius-Laurén and Rinne published in Estuarine, Coastal and Shelf Science in 2024. <a href="https://doi.org/10.1016/j.ecss.2024.108822">https://doi.org/10.1016/j.ecss.2024.108822</a></p> <p>The data describes the community data collected with the community science method described in the paper. </p>
Data supporting: Combined stress of an insecticide and heatwaves or elevated temperature induce community and food web effects in a Mediterranean freshwater ecosystem
<p>Data used to obtain the results of the research paper entitled: "Combined stress of an insecticide and heatwaves or elevated temperature induce community and food web effects in a Mediterranean freshwater ecosystem", published in the journal "Water Research". The data derives from an outdoor (meso-) cosm experiment in Spain (Imdea Water, Alcala de Henares) in which the transportable temperature and heatwave control device (TENTACLE) was used to investigate the multiple stressors effects of two different climate change scenarios related to temperature (i.e., elevated temperature and reoccurring heatwaves) in combination with the neonicotinoid insecticide imidacloprid.</p>
Drosophila Larvae Tracking: movies of drosophila larvae communities
<h2>33 movies of drosophila larvae communities</h2> <p>The task associated to this dataset is tracking multiple drosophila larvae. Such a tracking is required in the quest to elucidate the genetic basis of Drosophila's behaviour. This dataset was used in the article "<a href="https://hci.iwr.uni-heidelberg.de/sites/default/files/publications/files/219478572/fiaschi_14_tracking.pdf" target="_blank" rel="noopener">Tracking indistinguishable translucent objects over time using weakly supervised structured learning</a>". We provide the raw data, an intermediate segmentation of the foreground and the gold standard used in the <a href="https://hci.iwr.uni-heidelberg.de/sites/default/files/publications/files/219478572/fiaschi_14_tracking.pdf">evaluation of that tracking algorithm</a>. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.