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.
7,378
datasets available to search
ShareScore release 0.9.0
Dataset results
7,378 results for “case”
FIGURE 2 in Phloeocharis subtilissima Mannerheim (Staphylinidae: Phloeo charinae) and Cephennium gallicum Ganglbauer (Scydmaenidae) new to North America: a case study in the introduction of exotic Coleoptera to the port of Halifax, with new records of other species
FIGURE 2: Phloeocharis subtilissima Mannerheim, Point Pleasant Park, Halifax, Nova Scotia, Canada. Living specimen in bark of red maple (Acer rubrum).
FIGURE 1 in Phloeocharis subtilissima Mannerheim (Staphylinidae: Phloeo charinae) and Cephennium gallicum Ganglbauer (Scydmaenidae) new to North America: a case study in the introduction of exotic Coleoptera to the port of Halifax, with new records of other species
FIGURE 1: Phloeocharis subtilissima Mannerheim, Point Pleasant Park, Halifax, Nova Scotia, Canada. Dorsal habitus.
FIGURE 4 in Phloeocharis subtilissima Mannerheim (Staphylinidae: Phloeo charinae) and Cephennium gallicum Ganglbauer (Scydmaenidae) new to North America: a case study in the introduction of exotic Coleoptera to the port of Halifax, with new records of other species
FIGURE 4: Cephennium gallicum Ganglbauer, Point Pleasant Park, Halifax, Nova Scotia, Canada. Lateral habitus.
from A remarkable case of mosaic parapatry in millipedes - ZooKeys 156: 71-84 (20 December 2011) https://doi.org/10.3897/zookeys.156.1893
- Figure 6. Centroids of triangles in Figure 5. Co-occurrences of Tasmaniosoma compitale and Tasmaniosoma hickmanorum males are circled. Arrow points to area shown in Figure 8. Scale bar = 25 km.
Supplementary material 1: Parapatry_records from A remarkable case of mosaic parapatry in millipedes - ZooKeys 156: 71-84 (20 December 2011) https://doi.org/10.3897/zookeys.156.1893
ZIP archive contains: 1. All currently known locality records for the parapatric millipedes Tasmaniosoma compitale and T. hickmanorum, in spreadsheet (CSV) format. 2. 4 KML files giving localities for (1) males of Tasmaniosoma compitale and T. hickmanorum, (2) females of these species, (3) cooccurrences of these species (4) unidentified females of these species. KML annotation gives museum registration number for specimen lot.
from A remarkable case of mosaic parapatry in millipedes - ZooKeys 156: 71-84 (20 December 2011) https://doi.org/10.3897/zookeys.156.1893
- Figure 1. Localities for male Tasmaniosoma hickmanorum. Scale bar = 100 km. Rectangle indicates map extent in Figs 2–6.
from A remarkable case of mosaic parapatry in millipedes - ZooKeys 156: 71-84 (20 December 2011) https://doi.org/10.3897/zookeys.156.1893
- Figure 5. Edited Delaunay triangulation of localities for male Tasmaniosoma compitale (filled circles), Tasmaniosoma hickmanorum (open circles) and co-occurrences (stars); see text for explanation. Scale bar = 25 km.
from A remarkable case of mosaic parapatry in millipedes - ZooKeys 156: 71-84 (20 December 2011) https://doi.org/10.3897/zookeys.156.1893
- Figure 2. Localities for Tasmaniosoma hickmanorum males (filled squares) and females identified by colour (open squares). Ellipse encloses Arthur-Hellyer 'island' (see text for explanation). Scale bar = 25 km.
from A remarkable case of mosaic parapatry in millipedes - ZooKeys 156: 71-84 (20 December 2011) https://doi.org/10.3897/zookeys.156.1893
- Figure 4. Delaunay triangulation of localities for male Tasmaniosoma compitale (filled circles), Tasmaniosoma hickmanorum (open circles) and co-occurrences (stars). The set of localities used has been trimmed to the vicinity of the parapatric boundary, and Tasmaniosoma hickmanorum localities in the Arthur-Hellyer 'island' and Tasmaniosoma compitale localities at the Vale of Belvoir have been excluded. Scale bar = 25 km.
from A remarkable case of mosaic parapatry in millipedes - ZooKeys 156: 71-84 (20 December 2011) https://doi.org/10.3897/zookeys.156.1893
- Figure 7C, D. Centroids from Fig. 6 superimposed on simplified bedrock geology C and mean annual rainfall isohyets, in mm D. In C, colours crossed by main parapatric boundary represent (anticlockwise from top left) Quaternary coastal sand and gravel (gray-blue), Precambrian siltstone and mudstone (orange), Precambrian metamorphics (green), Cambrian conglomerate and siltstone (gray-green), Tertiary basalt (light brown) and Permian glaciomarine sedimentary rocks (red-brown). Scale bars = 25 km.
from A remarkable case of mosaic parapatry in millipedes - ZooKeys 156: 71-84 (20 December 2011) https://doi.org/10.3897/zookeys.156.1893
- Figure 3. Localities for Tasmaniosoma compitale males (filled squares) and females identified by colour (open squares). Outliers (circled) are at the Vale of Belvoir. Scale bar = 25 km.
from A remarkable case of mosaic parapatry in millipedes - ZooKeys 156: 71-84 (20 December 2011) https://doi.org/10.3897/zookeys.156.1893
- Figure 8. Rebecca Spur 3 area (marked with arrow in Fig. 6) in Google Earth image dated 11 October 2010. Markers: Tasmaniosoma compitale males (darker green) and females identified by colour (lighter green), Tasmaniosoma hickmanorum males (darker red) and females identified by colour (lighter red), and possible co-occurrence of females identified by colour (yellow).
from A remarkable case of mosaic parapatry in millipedes - ZooKeys 156: 71-84 (20 December 2011) https://doi.org/10.3897/zookeys.156.1893
- Figure 7A, B. Centroids from Fig. 6 superimposed on 100 m elevation contours A and principal rivers B. Scale bars = 25 km.
Use Case Points Benchmark Dataset
<p>This dataset was gathered by us from three software houses. This is real-life dataset. Use Case points method as originated by Karner was used for counting a steps or number of actors. Data are based on different languages, various problem domains. ISBSG style for language, domain and application type were adopted.</p> <p><br> Attributes are used as follows:<br> Project_No - only project ID for identification purposes<br> Simple Actors - Number of actor classify according UCP - simple actors.<br> Average Actors - Number of actor classify according UCP - average actors.<br> Complex Actors - Number of actor classify according UCP - complex actors.<br> UAW - Unadjusted Actor weight, computed by using UCP equation. <br> Simple UC - Number of use cases classified as simple - UCP number of steps is used.<br> Average UC - Number of use cases classified as average - UCP number of steps is used.<br> Complex UC - Number of use cases classified as complex - UCP number of steps is used.<br> UUCW - Unadjusted UseCase Weight - computed by using UCP equation.<br> TCF - Technical Complexity FactorECF - Enviromental Complexity Factors<br> Real_P20 - Real_P20 - Real Effort in Person hours, decided by productivity factor (PF = 20).<br> Real_Effort_Person_Hours - Real Effort (development time) in person-hours.<br> Sector - Problem domain of projectLanguage - Programming language used for project.<br> Methodology - Development methodology used for project development.<br> ApplicationType - Classification of project type - provided by donator. <br> DataDonator - anonymized acronym for data donator.<br> </p>
HUMANE internal case study: eVACUATE #1
<p>This case study was conducted on 14 December 2015. The purpose was to evaluate the usefulness of the HUMANE approach as perceived by relevant developers (software engineers), and additionally ask if the HUMANE typology facilitates cross-disciplinary understanding.</p> <p>The files included here provide a summary of the analysis and the transcript from a semi-structured focus group.</p>
HUMANE external case study: eVACUATE #2
<p>This case study was conducted in September to October 2016 with the purpose of providing an external validation of the HUMANE typology and method. This eVACUATE case-study comprises four different engagements in order to ensure a comprehensive evaluation: a quantitative online survey on the HUMANE design patterns; a quantitative survey on the HUMANE typology used for characterising Human-Machine Networks (HMNs); and two focus groups evaluating the HUMANE method (covering the profiling process, network diagramming, implication analysis, and design pattern approach).</p> <p>A summary of results, along with focus group transcripts, surveys and survey results are included here.</p>
10 eADAGE models used for the PAO1 KEGG pathways case study in PathCORE
<p>ensemble Analysis using Denoising Autoencoders for Gene Expression<strong> </strong>(<strong>eADAGE</strong>) is an unsupervised feature construction algorithm developed by Tan et al. that uses an ensemble of neural networks (an ensemble of ADAGE models) to capture biological signatures embedded in the expression compendium. By initializing eADAGE with different random seeds, Tan et al. produced 10 eADAGE models that each extracted k=300 features from the compendium of genome-scale <em>P. aeruginosa</em> data.</p> <p>eADAGE is described in Tan et al.'s "System-wide automatic extraction of functional signatures in <em>Pseudomonas aeruginosa</em> with eADAGE" (https://doi.org/10.1101/078659). The code to construct these 10 models is available in this repository: https://bitbucket.org/greenelab/eadage (see eADAGE_construction.sh). </p>
Unfair Inequality in Education: A Benchmark for AI-Fairness Research (Aequitas WP7 Use Case S2)
<h1>Unfair Inequality in Education: A Benchmark for AI-Fairness Research</h1> <p>This dataset proposes a novel benchmark specifically designed for AI fairness research in education. It can be used for challenging tasks aimed at improving students' performance and reducing dropout rates which are also discussed in the paper to emphasize significant research directions. By prioritizing fairness, this benchmark aims to foster the development of bias-free AI solutions, promoting equal educational access and outcomes for all students.</p> <h2>Structure</h2> <p><code>benchmark</code> contains:</p> <ul> <li>the proposed dataset (<code>dataset.csv</code>), </li> <li>the mask for dealing with missing values (<code>missing_mask.csv</code>), and</li> <li> <div> <div>the meta-columns providing grouping criteria and sample weights for each student (<code>meta_cols.csv</code>).</div> </div> </li> </ul> <p><code>raw_data</code> includes:</p> <ul> <li>the original dataset (<code>original.csv</code>), and</li> <li>the intermediate stages of the pre-processing and validation pipelines (<code>split</code>, <code>pre_processed</code>, and <code>validation</code>).</li> </ul> <p><code>res</code> contains the documentation, including:</p> <ul> <li>the transformation mapping each column of the original dataset to the proposed one, along with the missingness category and original text (<code>meta_data_mapping.csv</code>),</li> <li>the value type and domains of each column of the proposed datasets (<code>meta_data_stats.json</code>), and</li> <li> <div> <div>the statistical indices of the validation pipeline (<code>bias_preservation_results.json</code>).</div> </div> </li> </ul> <p><code>src</code> contains the source code for running the pre-processing and corresponding analysis:</p> <ul> <li><code>pre_processing</code> and <code>stats</code>contain the code for the two corresponding tasks, and</li> <li><code>pre_processing.py</code> and <code>split.py</code> are two entry points.</li> </ul> <p>Finally, <code>Dockerfile</code> and <code>requirements.txt</code> set up the environment for running the applications across multiple platforms and with Python, respectively.</p>
Derived-ECVs for Case Studies (maps)
<p>Climate maps (raster layers .tif) of derived-ecvs with a spatial resolution of 5.5 km (1 km for Azores) obtained by statistically downscaling a set of CMIP6 simulations for different IPCC climate scenarios (historical, SSP1-2.6, SSP2-4.5, SSP5-8.5) and time horizons (reference, short time-horizon, medium time-horizon, long time-horizon). Data are representative of specific climate normals (yearly averaged values) and created by RethinkAction.</p> <p>We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF.</p> <p>Moreover, we acknowledge the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) to provide access to CMIP6, CERRA, ERA5 and ERA5-Land data:</p> <ul> <li>Copernicus Climate Change Service, Climate Data Store, (2021): CMIP6 climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.c866074c.</li> <li>Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q., (2021): CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.622a565a</li> <li>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D.,Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47</li> <li>Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.e2161bac</li> </ul> <p>Acknowledgement also to:</p> <ul> <li>DRAAC, 2023, Regional climate data provided by the Regional Ditectorate for the Environment and Climate Change of the Regional Autonomous Government of Azores (<a href="https://urldefense.com/v3/__https:/portal.azores.gov.pt/en/web/draac__;!!D9dNQwwGXtA!TAB9_FXZEEA4K_6AkmoIqX-krFMSiGcKRY--rOpV9psI98vjxa-sLAZQYR1s0G1fFmBrENoHHpZCdvP0s67vzss$" target="_blank" rel="noopener">https://portal.azores.gov.pt/en/web/draac</a>)</li> <li>SRAA\CCIAM, 2017. Programa Regional de Alterações Climáticas (PRAC), Secretaria Regional do Ambiente e Ação Climática (SRAA) of the Governo dos Açores, Climate Change Impacts, Adaptation and Modelling (CCIAM) of the Faculdade de Ciências da Universidade de Lisboa (FCUL), <a href="https://urldefense.com/v3/__https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search*/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1__;Iw!!D9dNQwwGXtA!SmILhnS0zICNt1ZcxuQ0VPP7VxtFeSEdLt4chAw8y5tsdlWqsgkyt9kESGhRu-00ZQqakzH36tvV-_DcQI7jGo-lOg$" target="_blank" rel="noopener">https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search#/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1</a></li> </ul> <p>This v2 includes metadata.</p>
Basic-ECVs for Case Studies (maps)
<p>Climate maps (raster layers .tif) of basic-ecvs with a spatial resolution of 5.5 km (1 km for Azores) obtained by statistically downscaling a set of CMIP6 simulations for different IPCC climate scenarios (historical, SSP1-2.6, SSP2-4.5, SSP5-8.5) and time horizons (reference, short time-horizon, medium time-horizon, long time-horizon). Data are representative of specific climate normals (yearly averaged values) and created by RethinkAction project.</p> <p>We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF.</p> <p>Moreover, we acknowledge the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) to provide access to CMIP6, CERRA, ERA5 and ERA5-Land data:</p> <ul> <li>Copernicus Climate Change Service, Climate Data Store, (2021): CMIP6 climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.c866074c.</li> <li>Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q., (2021): CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.622a565a</li> <li>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D.,Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 </li> <li>Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.e2161bac </li> </ul> <p>Acknowledgement also to: </p> <ul> <li>DRAAC, 2023, Regional climate data provided by the Regional Ditectorate for the Environment and Climate Change of the Regional Autonomous Government of Azores (<a href="https://urldefense.com/v3/__https:/portal.azores.gov.pt/en/web/draac__;!!D9dNQwwGXtA!TAB9_FXZEEA4K_6AkmoIqX-krFMSiGcKRY--rOpV9psI98vjxa-sLAZQYR1s0G1fFmBrENoHHpZCdvP0s67vzss$" target="_blank" rel="noopener">https://portal.azores.gov.pt/en/web/draac</a>)</li> <li>SRAA\CCIAM, 2017. Programa Regional de Alterações Climáticas (PRAC), Secretaria Regional do Ambiente e Ação Climática (SRAA) of the Governo dos Açores, Climate Change Impacts, Adaptation and Modelling (CCIAM) of the Faculdade de Ciências da Universidade de Lisboa (FCUL), <a href="https://urldefense.com/v3/__https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search*/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1__;Iw!!D9dNQwwGXtA!SmILhnS0zICNt1ZcxuQ0VPP7VxtFeSEdLt4chAw8y5tsdlWqsgkyt9kESGhRu-00ZQqakzH36tvV-_DcQI7jGo-lOg$" target="_blank" rel="noopener">https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search#/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1</a></li> </ul> <p> </p> <p>This v2 includes metadata.</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.