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.
3,947
datasets available to search
ShareScore release 0.9.0
Dataset results
3,947 results for “Requirement”
Supplementary material 1 from: Vissers J, Bosch FV, Bogaerts A, Cocquyt C, Degreef J, Diagre D, de Haan M, De Smedt S, Engledow H, Ertz D, Fabri R, Godefroid S, Hanquart N, Mergen P, Ronse A, Sosef M, Stévart T, Stoffelen P, Vanderhoeven S, Groom Q (2017) Scientific user requirements for a herbarium data portal. PhytoKeys 78: 37-57. https://doi.org/10.3897/phytokeys.78.10936
Gathered needs per type of researcher :
Supplementary material 1 from: Wetzel F, Hoffmann A, Häuser C, Vohland K (2016) 1st EU BON Stakeholder Roundtable (Brussels, Belgium): Biodiversity and Requirements for Policy. Research Ideas and Outcomes 2: e8600. https://doi.org/10.3897/rio.2.e8600
Acronyms - 1st EU BON Stakeholder Roundtable
Figure 1 from: Vissers J, Bosch FV, Bogaerts A, Cocquyt C, Degreef J, Diagre D, de Haan M, De Smedt S, Engledow H, Ertz D, Fabri R, Godefroid S, Hanquart N, Mergen P, Ronse A, Sosef M, Stévart T, Stoffelen P, Vanderhoeven S, Groom Q (2017) Scientific user requirements for a herbarium data portal. PhytoKeys 78: 37-57. https://doi.org/10.3897/phytokeys.78.10936
Figure 1 - Stakeholders interacting with the Botanic Garden Meise and potentially using its data portal. The stakeholders prefixed by the words 'internal' refer to those that work at the Botanic Garden, whereas those referred to as 'external' refer to researchers in other institutions.
Figure 3 from: Vissers J, Bosch FV, Bogaerts A, Cocquyt C, Degreef J, Diagre D, de Haan M, De Smedt S, Engledow H, Ertz D, Fabri R, Godefroid S, Hanquart N, Mergen P, Ronse A, Sosef M, Stévart T, Stoffelen P, Vanderhoeven S, Groom Q (2017) Scientific user requirements for a herbarium data portal. PhytoKeys 78: 37-57. https://doi.org/10.3897/phytokeys.78.10936
Figure 3 - A summary of the data elements mentioned by the different researcher types, showing which data elements researchers had in common and which were unique. This does not mean that any particular data element is not of interest to another group, only that it did not arise in the series of interviews. Details of these data elements can be found in the supplementary information. The full list of common data elements is listed in Table 2.
Figure 2 from: Vissers J, Bosch FV, Bogaerts A, Cocquyt C, Degreef J, Diagre D, de Haan M, De Smedt S, Engledow H, Ertz D, Fabri R, Godefroid S, Hanquart N, Mergen P, Ronse A, Sosef M, Stévart T, Stoffelen P, Vanderhoeven S, Groom Q (2017) Scientific user requirements for a herbarium data portal. PhytoKeys 78: 37-57. https://doi.org/10.3897/phytokeys.78.10936
Figure 2 - A user experience researcher using affinity diagramming to cluster user requirements from the results of the interviews.
Eliciting Public Discourse of SE Tool Providers in a Study on Requirements Process Debt – A Different Shade of Grey - Auxiliary Material
Open the record for dataset details and reuse information.
Automating the process of Requirements Modeling with LLMs
Open the record for dataset details and reuse information.
Figure 3 in The current state of DNA barcoding of macroalgae in the Mediterranean Sea: presently lacking but urgently required
Figure 3: Total number of DNA barcodes obtained from the literature-based results of 121 papers.
Investigating User Feedback from a Crowd in Requirements Management in Software Ecosystems
Open the record for dataset details and reuse information.
Supplementary material of Investigating User Feedback from a Crowd in Requirements Management in Software Ecosystems
Open the record for dataset details and reuse information.
Supplementary material of Investigating User Feedback from a Crowd in Requirements Management in Software Ecosystems
Open the record for dataset details and reuse information.
Code and data for the article "Feasible deployment of carbon capture and storage and the requirements of climate targets"
<p>Here, we present data and code for our study “Feasible deployment of carbon capture and storage and the requirements of climate targets” (2024), where we project feasible ranges of carbon capture and storage (CCS) deployment in this century based on historical evidence of CCS deployment and other policy-driven technologies.</p>
Uneven Distribution of Knowledge Requirements for Female Researchers
<p>The raw data of "Uneven Distribution of Knowledge Requirements for Female Researchers".</p>
TRMM-based gridded climatology required for computing SEEPS
<p>Gridded climatology required for computing the Stable Equitable Error in Probability Space (SEEPS) for use in METplus</p> <p>https://github.com/dtcenter/MET</p> <p>https://doi.org/10.1175/MWR-D-20-0347.1</p>
Supplementary material 3 from: Gehrke B (2018) Staying cool: preadaptation to temperate climates required for colonising tropical alpine-like environments. PhytoKeys 96: 111-125. https://doi.org/10.3897/phytokeys.96.13353
Location of the tropical alpine-like climate regions in the Tropics :
Supplementary material 1 from: Gehrke B (2018) Staying cool: preadaptation to temperate climates required for colonising tropical alpine-like environments. PhytoKeys 96: 111-125. https://doi.org/10.3897/phytokeys.96.13353
Detailed examples on how the coding was done :
Figure 3 from: Gehrke B (2018) Staying cool: preadaptation to temperate climates required for colonising tropical alpine-like environments. PhytoKeys 96: 111-125. https://doi.org/10.3897/phytokeys.96.13353
Figure 3 Proportion of plant elements in tropical alpine regions based on generic distribution patterns according to Smith and Cleef (1988).
Figure 1 from: Gehrke B (2018) Staying cool: preadaptation to temperate climates required for colonising tropical alpine-like environments. PhytoKeys 96: 111-125. https://doi.org/10.3897/phytokeys.96.13353
Figure 1 Location of the tropical alpine-like climate regions in the Tropics on a Mercator projection of the world with shaded relief and coloured height based on SRTM data with 1 arc second resolution. Credit: NASA/JPL/NIMA downloaded from http://photojournal.jpl.nasa.gov/catalog/PIA03395. Detailed maps for each region are included in the Suppl. material 3 (figures S3–S6).
Figure 2 from: Gehrke B (2018) Staying cool: preadaptation to temperate climates required for colonising tropical alpine-like environments. PhytoKeys 96: 111-125. https://doi.org/10.3897/phytokeys.96.13353
Figure 2 Relative contribution of in situ speciation and immigration to species richness in selected tropical alpine regions (pie charts on the left). Blue: in situ speciation, green: colonisation, light blue: uncertainty regarding in situ speciation, light green: uncertainty about colonisation. In the right pie charts, colonisation is further decoupled into species derived from other regions with alpine-like climate (black) and species that originated by colonisation from a different biome (red). Uncertainty is indicated by grey.
Moving-block System: Requirements and Formal Models
<p>A moving-block system (cf. <a href="https://en.wikipedia.org/wiki/Moving_block">https://en.wikipedia.org/wiki/Moving_block</a>) is a railway signalling and distancing system aimed at reducing the headways between trains along a track, therefore increasing line capacity. In railways, a block is intended as a segment of track that is assigned to a train: a train cannot enter a block that is occupied by another train. With traditional fixed-block systems, each moving train is assigned a fixed block, regardless of its speed and position within the block itself. With moving-block systems, the block is dynamically computed based on the position and speed of the train, which is continuously computed onboard and communicated to the wayside control systems. This enables the possibility of routing more trains along the same track. </p> <p>For more details, please refer to: <a href="https://en.wikipedia.org/wiki/Communications-based_train_control">https://en.wikipedia.org/wiki/Communications-based_train_control</a></p> <p>The package includes a set of <strong>requirements</strong> and <strong>models</strong> for a railway moving-block system:</p> <p>(a) a PDF document named Moving-block Model and Requirements.pdf, which includes a UML model of a moving-block system together with a set of requirements for the system;</p> <p>(b) a set of 9 folders, each one associated to a formal or semi-formal development tool. Each folder contains one or more models of the moving-block system from (a), developed by means of the tool. </p> <p>The models were developed using the following tool versions. Other versions may still open and verify the models.</p> <ul> <li>Simulink (2017b)</li> <li>UMC (4.7)</li> <li>UPPAAL SMC (4.1.4) </li> <li>Atelier B (4.2.1)</li> <li>ProB (1.10.2018)</li> <li>NuSMV (2.6.0)</li> <li>SPIN (6.4.9)</li> <li>CADP (2019-a)</li> <li>FDR4 (4.2.3)</li> </ul>
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.