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.
107
datasets available to search
ShareScore release 0.9.0
Dataset results
107 results for “Archetype”
Figure 1. Hypothetical salticid archetype for a in A salticid archetype for salticid spiders
Figure 1. Hypothetical salticid archetype for a salticid spider (1) and representatives of five lepidopteran genera that share these archetypal features (2-6), apparently the result of convergent evolution. Attribution and ©: 1, David E. Hill; 2, Ian McMillan; 3, Christian Schwartz; 4, Arnold Wijker; 5, CheongWeei Gan; 6, Eric Carpenter.
Figure 24 in A salticid archetype for salticid spiders
Figure 24. Olethreutes arcuella. Olethreutes is part of a very large tortricid subfamily (Olethreutinae) with many small, hightly, ornamented species, very few of which come close to displaying any features of the hypothetical salticid archetype (Figure 29). Extra "eyes" of O. arcuella may induce a supernormal response by salticids. Attribution and ©: 1, Kostas Zontanos; 2, Felix Riegel; 3, sabine-g; 4, Mirko Tomasi; 5, Marie Lou Legrand; 6, oe5hm; 7, Andrei; 9, 15, Paolo Mazzei; 8, Franziska Bauer; 10, Joey Bom; 11, Xavier Mas; 12, Vojtek Pavel; 13-14, Ryszard.
Figure 28 in A salticid archetype for salticid spiders
Figure 28. Developmental stages of Phidippus princeps from Greenville County, South Carolina. 1, The first emergent or freeliving stage (instar II) is black, with pedipalps and legs that fluoresce (emit bright yellow-green) in near-UV light. 2, The pedipalps and proximal segments of the legs of early instars like this one also fluoresce, but the face now has a cover of setae, including a darker band through the anterior eye row, typical of later instars through the penultimate stage. 3, At the prepenultimate stage males and females are similar. 4, Penultimate male, with distinct enlargement of the pedipalps but otherwise coloration like that of the female. 5, Penultimate female. 6, Adult female, with bright white setae covering the face, highlighting the anterior eyes. Attribution and ©: 1-6, David E. Hill.
Archetype-Based Redshift Estimation for the Dark Energy Spectroscopic Instrument Survey
<p>Supplementary material to DESI's publication "Archetype-Based Redshift Estimation for the Dark Energy Spectroscopic Instrument Survey" by Anand et al. 2024 to comply with the data management plan. The material includes all the data shown in the figures of the results of the paper.</p>
European Archetype Distribution Grid Models
<p>This database contains publicly available representative European archetype distribution grid models, which where identified through an extensive literature review. All included grid models were converted into a standardised data format. The collection of identified grid models from the literature was also expanded through the creation of new grid models for Finland and the Netherlands. </p> <p>Currently, the databse covers grid models for the following regions: </p> <ul> <li>Germany <ul> <li>Kerber Networks (Kerber, 2011)</li> <li>Synthetic Voltage Control LV Networks (Lindner et al, 2016)</li> <li>SimBench (Meinecke et al, 2020)</li> </ul> </li> </ul> <ul> <li>Spain <ul> <li>Spanish MV Grid Model (Buigues Beraza, 2011)</li> </ul> </li> </ul> <ul> <li>UK <ul> <li>Low Voltage Network Models (Espinosa, 2015a/b)</li> <li>UKGDS (SEDG, no date)</li> </ul> </li> </ul> <ul> <li>Finland <ul> <li>Finland LV Grids Model (Newly generated)</li> </ul> </li> </ul> <ul> <li>Netherland<br> <ul> <li>Dutch MV grids (Newly generated)</li> </ul> </li> </ul> <ul> <li>Europe (general European grid models) <ul> <li>European representative Electricity Distribution Grids (Mateo et al, 2018)</li> <li>Non-Synthetic Test Systems of European Distribution Networks (Taye et al, 2024)</li> </ul> </li> </ul> <p>The database is part of the deliverable D8.2 of the DriVe2X project. The accompanying report for D8.2 provides a detailed description of the database structure and the created data format. Additionally, it introduces all grid models included as of the publication date. Some grid models from the literature required minor adjuistments to be integrated into the database. Detailed information about these adjustment is also provided in the D8.2 report. The report is currently being prepared and will be made available from the project download section as soon as it is published.</p> <p><strong>Please note: </strong><br>Despite great care, there is no claim to completeness and correctness of the grid models contained in this database. <br><br></p> <p><strong>References</strong></p> <p>DriVe2X website: <a href="https://drive2x.eu/" target="_blank" rel="noopener">Link</a><br>Cordis website: <a href="https://cordis.europa.eu/project/id/101056934" target="_blank" rel="noopener">Link</a><br>ie³ institute: <a href="https://ie3.etit.tu-dortmund.de/">Link</a></p> <p>------</p> <p>Kerber, G. (2011). <em>Aufnahmefähigkeit von Niederspannungsverteilnetzen für die Einspeisung aus Photovoltaikkleinanlagen</em> (Doctoral dissertation, Technische Universität München).</p> <p>Lindner, M., Aigner, C., Witzmann, R., Wirtz, F., Berber, I., Gödde, M., & Frings, R. (2016). ‚Aktuelle Musternetze zur Untersuchung von Spannungsproblemen in der Niederspannung‘, <em>14. Symposium Energieinnovation</em>.</p> <p>Meinecke, S., Drauz, S., Bornhorst, N., Spalthoff, C., Lauven, L.P., Cronbach, D., Menke, J.H., Kneiske, T., Braun, M., Klettke, A., Sarajlić, D., Sprey, J., Kittl, C., van Leeuwen, T., Rehtanz, C., Moser, A. (2020). <em>Simbench-dokumentation, dokumentationsversion de-1.0. 1, elektrische benchmarknetzmodelle.</em></p> <p>Buigues Beraza, G. (2011). <em>Metodología para la detección y localización de faltas en redes de distribución con puesta a tierra activa</em> (Doctoral dissertation, Universidad del País Vasco-Euskal Herriko Unibertsitatea).</p> <p>Espinosa, A. N. (2015). <em>Dissemination Document “Low Voltage Networks Models and Low Carbon Technology Profiles”</em>.</p> <p>Espinosa, A. N. (2015). <em>Low carbon technologies in low voltage distribution networks: probabilistic assessment of impacts and solutions</em>.</p> <p>Centre for Sustainable Electricity and Distributed Generation (SEDG) (no date) UKGDS [Online]. Available at: https://github.com/sedg/ukgds (Accessed: 08 March 2024)</p> <p>Mateo, C., Prettico, G., Gómez, T., Cossent, R., Gangale, F., Frías, P., & Fulli, G. (2018) 'European representative electricity distribution networks', <em>International Journal of Electrical Power & Energy Systems</em>, 99, 273-280.</p> <p>Taye, T., Mohamed, B., Suarez-Ramon, L., & Arboleyá, P. (2024) 'A set of Non-Synthetic test systems of European LV Rural, LV urban and hybrid MV/LV industrial distribution networks', <em>International Journal of Electrical Power & Energy Systems</em>, 158, 109941.</p> <p> </p>
Archetype-based Life-Cycle Assessment of National Residential Building Stocks: Resource Use and Greenhouse Gas Emissions in Western Asia and Northern Africa
<p><strong>Dataset Name:</strong><br><em>Literature Data and Archetype Parameter Sheets for the publication, named Archetype-based Life-Cycle Assessment of National Residential Building Stocks: Resource Use and Greenhouse Gas Emissions in Western Asia and Northern Africa.</em></p> <p><strong>Description:</strong><br>This dataset includes Excel sheets containing literature sources and archetypal data on Western Asian and North African countries' residential dwelling typologies. As well as Vacancy rates used and simulation results.</p> <p><strong>Files:</strong><br>The following files are included in the dataset:</p> <ul> <li> <em>[CountryName]_LiteratureSources.xlsx:</em> Excel sheet containing literature sources and references,</li> <li> <em>[CountryName]_ArchetypeParameters.xlsx</em>: Archetype models' semantic, geometric, and technical data used in the generation of energy models,</li> <li><em> VacantHouses.xlsx</em>: Vacant house rates for the countries, the found articles on the web, literature sources, etc.,</li> <li> <em>Resource Use Results:</em> BuildME Simulation Results</li> </ul> <p><strong>Usage:</strong><br>The dataset is intended for researching and analyzing the Western Asian and North African countries' residential buildings. The literature sources included in the [CountryName]_LiteratureSources.xlsx and [CountryName]_ArchetypeParameters.xlsx files can be used to verify, support, or reproduce the research findings.</p> <p><strong>License:</strong><br>The dataset is licensed under Creative Commons Attribution 4.0 International.</p> <p><strong>Citation:</strong><br>If you use this dataset in your research, please cite it as follows and contact the corresponding author:</p> <p>Akin, Sahin, Aida Eghbali, Chibuikem Chrysogonus Nwagwu, and Edgar Hertwich. 2024. “Archetype-based Life-Cycle Assessment of National Residential Building Stocks: Resource Use and Greenhouse Gas Emissions in Western Asia and Northern Africa” https://doi.org/10.5281/zenodo.13380340.</p> <p><strong>Contact:</strong><br>The archetypes' energy models (DesignBuilder or IDF files) can be provided on request. If you have any questions or comments about the dataset, please contact <strong>sahin.akin@ntnu.no, the corresponding author.</strong></p>
ETHOS.BUILDA: Residential Building TABULA Archetype Dataset Germany
<h2>Introduction</h2> <p>This dataset contains all residential buildings in Germany with their construction year, size class, refurbishment state, and <a href="https://episcope.eu/iee-project/tabula/">TABULA archetype</a>. It is a partial dump of the ETHOS.BUILDA database (version v8_20240916). ETHOS.BUILDA is a database containing building-level data for the German building stock. It is based on various data sources that are combined and enriched with machine learning approaches to generate one consistent and complete building dataset. </p> <p>ETHOS.BUILDA is made available under the <a href="http://opendatacommons.org/licenses/odbl/1.0" target="_blank" rel="noopener">Open Database License (ODbL)</a>. The licenses of the contents of the database depend on the data source. The sources of the building attributes and information on the type of processing that was done to assign the information from the raw data to the building in ETHOS.BUILDA are provided for each individual data point.</p> <h2>Data structure and file overview</h2> <p>Building data is provided per federal state, the files are named according to the <a href="https://en.wikipedia.org/wiki/NUTS_statistical_regions_of_Germany" target="_blank" rel="noopener">NUTS-1</a> region names. The building data has the following fields:</p> <table> <tbody> <tr> <td><strong>field name</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>ID</td> <td>unique identifier of the building</td> </tr> <tr> <td>position</td> <td>location of building centroid in WKT-format, EPSG:3035</td> </tr> <tr> <td>construction_year</td> <td> <p>value: construction year, </p> <p>source: source of the construction year data,</p> <p>lineage: construction year assignment method</p> </td> </tr> <tr> <td>size_class</td> <td> <p>value: size class of the building, </p> <p>source: source of the size class data,</p> <p>lineage: size class assignment method</p> </td> </tr> <tr> <td>refurbishment_state</td> <td> <p>value: refurbishment state of the building, </p> <p>source: source of the refurbishment state data,</p> <p>lineage: refurbishment state assignment method</p> </td> </tr> <tr> <td>tabula_type</td> <td> <p>value: TABULA type of the building, </p> <p>source: source of the TABULA data,</p> <p>lineage: TABULA type assignment method</p> </td> </tr> </tbody> </table> <p>A mapping of the abbreviations of "source" and "lineage" of individual data points to the descriptions is provided in sources.csv and lineages.csv. There is no entry for the source "v3_model.json", as it refers to the internally trained machine learning model for the respective attribute and not to an external data source.</p> <p>The full footprint polygons from which the centroids are derived and the sources of the footprints are found in the related dataset linked as "is supplemented by".</p> <h2>Acknowledgements</h2> <p>This work was supported by the Helmholtz Association under the program "Energy System Design". </p> <p>Furthermore, the authors would like to express their gratitude to the Federal Ministry for Economic Affairs and Climate Action (BMWK.IIB4) for providing the necessary resources to conduct this study. Our research was supported by the WAAGE Grant Program (Grant No. 03EI1044/03EE 5031D), and we appreciate their financial assistance.</p>
Figure 26 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 26. Responses to the Phidippus, high detail text prompt by the Huggingface Stable Diffusion 2.1 engine (#6).
Figure 30 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 30. Responses to the Springspinne, high detail text prompt by the Huggingface Stable Diffusion 2.1 engine (#6).
Figure 18 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 18. Responses to the jumping spider, high detail text prompt by the Starryai Argo 2 engine (#8).
Figure 15 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 15. Responses to the jumping spider, high detail text prompt by the Replicate Stable Diffusion engine (#7).
Figure 14 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 14. Responses to the jumping spider, high detail text prompt by the Huggingface Stable Diffusion 2.1 engine (#6).
Figure 8 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 8. Responses to the jumping spider, high detail text prompt by the Stable Diffusion Playground engine (#4).
Figure 6 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 6. Responses to the jumping spider, high detail text prompt by the Stable Diffusion Playground engine (#4).
Figure 4 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 4. Responses to the jumping spider, high detail text prompt by the NightCafe Stable Diffusion engine (#3).
Figure 25 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 25. Responses to the peacock jumping spider, high detail prompt by the Huggingface Stable Diffusion 2.1 engine (#6).
Figure 11 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 11. Responses to the jumping spider, high detail text prompt by the Huggingface Stable Diffusion 2.1 engine (#6).
Figure 2 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 2. Responses to the jumping spider, high detail text prompt by the Pixray vqgan engine (Table 1: #1). Although primitive, elements of the salticid archetype shown in Figure 1.1, including a horizontal row of eyes, a clypeus, and a fringe above the eyes, can be seen here.
Figure 1. 1 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 1. 1, Hypothetical archetype of a salticid spider, showing key features that may be used by another salticid to recognize this image as a salticid (after Hill 2022). 2, Example of a crambid moth that displays the key features of this archetype on its wings. 3, Image generated in response to the text input jumping spider, highly detailed by an intelligent machine (Replicate stable diffusion; see Table 1, #7). Can intelligent machines provide us with insight into the nature of the salticid archetype? Attribution and ©: 2, Arnold Wijker (https://www.inaturalist.org/observations/19305397), modified under a CC BY-NC 4.0 license.
Figure 24 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 24. Responses to the peacock spider, high detail text prompt by the Huggingface Stable Diffusion 2.1 engine (#6).
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.