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649 results for “bundles”
Very Low Birth Weight Preterm Infant Bundled Care in the NICU
ClinicalTrials.gov study NCT03370757. IPD Sharing: NO. Countries: 1. Publications: 1.
Education Bundle to Decrease Patient Refusal of VTE Prophylaxis
ClinicalTrials.gov study NCT02402881. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Data from: Gating-spring stiffness increases outer-hair-cell bundle stiffness, damping, and receptor current
Open the record for dataset details and reuse information.
Residual force enhancement is reduced in permeabilized fiber bundles from mdm muscles
Open the record for dataset details and reuse information.
Data from: High-speed motility originates from cooperatively pushing and pulling flagella bundles in bilophotrichous bacteria
Open the record for dataset details and reuse information.
Template for bundle-specific tractography
<p><strong>Bundle-specific tractography</strong></p> <p>This data is made to be used with the following script: <a href="https://github.com/scilus/scilpy/blob/master/scripts/scil_generate_priors_from_bundle.py">scil_generate_priors_from_bundle.py</a><br> This <a href="https://github.com/scilus/bstflow/">Nextflow pipeline</a> is made to simplify the execution.</p> <p>The script/pipeline and provided dataset are the same used in this publication:</p> <p><em>Rheault, Francois, et al. "Bundle-specific tractography with incorporated anatomical and orientational priors." NeuroImage 186 (2019): 382-398.</em><br> </p>
Data from: Historical dynamics of ecosystem services bundles
Managing multiple ecosystem services (ES), including addressing trade-offs between services and preventing ecological surprises, is among the most pressing areas for sustainability research. These challenges require ES research to go beyond the currently common approach of snapshot studies limited to one or two services at a single point in time. We used a spatiotemporal approach to examine changes in nine ES and their relationships from 1971 to 2006 across 131 municipalities in a mixed-use landscape in Quebec, Canada. We show how an approach that incorporates time and space can improve our understanding of ES dynamics. We found an increase in the provision of most services through time; however, provision of ES was not uniformly enhanced at all locations. Instead, each municipality specialized in providing a bundle (set of positively correlated ES) dominated by just a few services. The trajectory of bundle formation was related to changes in agricultural policy and global trends; local biophysical and socioeconomic characteristics explained the bundles' increasing spatial clustering. Relationships between services varied through time, with some provisioning and cultural services shifting from a trade-off or no relationship in 1971 to an apparent synergistic relationship by 2006. By implementing a spatiotemporal perspective on multiple services, we provide clear evidence of the dynamic nature of ES interactions and contribute to identifying processes and drivers behind these changing relationships. Our study raises questions about using snapshots of ES provision at a single point in time to build our understanding of ES relationships in complex and dynamic social-ecological systems.
Bundle of Iron Arrow Points, Mleiha, Sharjah
Bundle of iron arrow points from Mleiha, Sharjah, UAE. As found corroded together. A number of examples have been found. 1st Century BCE to 1st Century CE. Found in tombs of the period. Overlaet 2018:30-31, CAT No 29. GDH group 3. 482 photos. Completely processed (aligned, scaled, modeled, cleaned, simplified, unwrapped, textured, meshed) in Reality Capture. B. Overlaet 2018. Mleiha, An Arab Kingdom on the Caravan Trails (Brussels 30.10-30.12.2018). Sharjah: Sharjah Archaeology Authority. Source: Objaverse 1.0 / Sketchfab
Bundle of Iron Arrow Points, Mleiha, Sharjah
Bundle of iron arrow points from Mleiha, Sharjah, UAE. As found corroded together. A number of examples have been found. 1st Century BCE to 1st Century CE. Found in tombs of the period. Overlaet 2018:30-31, CAT No 29. GDH group 4. 565 photos. Completely processed (aligned, scaled, modeled, cleaned, simplified, unwrapped, textured, meshed) in Reality Capture. B. Overlaet 2018. Mleiha, An Arab Kingdom on the Caravan Trails (Brussels 30.10-30.12.2018). Sharjah: Sharjah Archaeology Authority. Source: Objaverse 1.0 / Sketchfab
Data bundle for egon-data: A transparent and reproducible data processing pipeline for energy system modeling
<p><strong>egon-data</strong> provides a transparent and reproducible open data based data processing pipeline for generating data models suitable for energy system modeling. The data is customized for the requirements of the research project <strong>eGon</strong>. The research project aims to develop tools for an open and cross-sectoral planning of transmission and distribution grids. For further information please visit the eGon <a href="https://ego-n.org/">project website</a> or its <a href="https://github.com/openego/eGon-data">Github repository.</a></p> <p>egon-data retrieves and processes data from several different external input sources. As not all data dependencies can be downloaded automatically from external sources we provide a data bundle to be downloaded by egon-data.</p> <p>The following data sets are part of the available data bundle:</p> <ol> <li> <p><strong>climate_zones_germany</strong></p> <ul> <li> <p>Climate zones in Germany</p> </li> <li> <p>source: Own representation based on DWD TRY climate zones</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>cutouts</strong></p> <ul> <li> <p>Weather data from Europe in 2011. Source: ERA5</p> </li> </ul> </li> <li> <p><strong>demand_regio_backup</strong></p> <ul> <li> <p>Electricity and heat demands</p> </li> </ul> </li> <li> <p><strong>emobility</strong></p> <ul> <li> <p>Data on eMobility mit_trip_data:<br>motorized individual travel - individual trips of electric vehicles (EV) generated with a modified version of simBEV v0.1.3 (https://github.com/rl-institut/simbev/tree/1f87c716d14ccc4a658b8d2b01fd12b88a4334d5). simBEV generates driving profiles for BEVs and PHEVs based upon MID data (BMVI) per RegioStaR7 region type (BBSR).</p> </li> <li> <p>Reiner Lemoine Institut, June 2022</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>entsoe</strong></p> <ul> <li> <p> </p> </li> </ul> </li> <li> <p><strong>gas_data</strong></p> <ul> <li> <p>CH4 infrastructure</p> </li> <li> <p>Biogas demand</p> </li> <li> <p>CH4 demand</p> </li> <li> <p>Source: SciGRID_gas</p> </li> </ul> </li> <li> <p><strong>geothermal_potential</strong></p> <ul> <li> <p>Spatial distribution of deep geothermal potentials in Germany</p> </li> <li> <p>source: <a href="https://doi.org/10.3390/en11020332">Assessment and Public Reporting of Geothermal Resources in Germany: Review and Outlook</a></p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>household_electricity_demand_profiles</strong></p> <ul> <li> <p>Annual profiles in hourly resolution of electricity demand of private households for different household types (singles, couples, other) with varying number of elderly and children.<br>The profiles were created using a bottom-up load profile generator by Fraunhofer IEE developed in the Bachelor's thesis "Auswirkungen verschiedener Haushaltslastprofile auf PV-Batterie-Systeme" by Jonas Haack, Fachhochschule Flensburg, December 2012.<br>The columns are named as follows: "<HH_TYPE_PREFIX>a<PROFILE_ID>", e.g. P2a0000 is the first profile of a couple's household with 2 children. See publication below for the list of prefixes. Values are given in Wh.<br>A related conference paper can be obtained here: http://publica.fraunhofer.de/documents/N-374761.html</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>household_heat_demand_profiles</strong></p> <ul> <li> <p>Sample heat time series including hot water and space heating for single- and multi-familiy houses. The profiles were created using the loadprofile generator by Fraunhofer IEE developed in the Master's thesis "Synthesis of a heat and electrical load profile for single and multi-family houses used for subsequent performance tests of a multi-component energy system", Simon Ruben Drauz, RWTH Aachen University, March 2016</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>hydrogen_network</strong></p> <ul> <li> <p>Planned H2 infrastructure</p> </li> <li> <p>Forecast H2 demand</p> </li> <li> <p>Source: fnb-gas</p> </li> </ul> </li> <li> <p><strong>hydrogen_storage_potential_saltstructures</strong></p> <ul> <li> <p>The data are taken from figure 7.1 in Donadei, S., et al., (2020), p. 7-5..</p> </li> <li> <p>Source: Flach lagernde Salze, (c) BGR Hannover, 2021.<br>Datenquelle: InSpEE-Salzstrukturen, (c) BGR, Hannover, 2015. &<br>Donadei, S., Horváth, B., Horváth, P.-L., Keppliner, J., Schneider, G.-S., &<br>Zander-Schiebenhöfer, D. (2020). Teilprojekt Bewertungskriterien und<br>Potenzialabschätzung. BGR. Informationssystem Salz: Planungsgrundlagen,<br>Auswahlkriterien und Potenzialabschätzung für die Errichtung von Salzkavernen<br>zur Speicherung von Erneuerbaren Energien (Wasserstoff und Druckluft) –<br>Doppelsalinare und flach lagernde Salzschichten: InSpEE-DS. Sachbericht.<br>Hannover: BGR.</p> </li> <li> <p>License: The original data are licensed under the GeoNutzV, see <a href="https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf">https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf</a></p> </li> </ul> </li> <li> <p><strong>industrial_gas_demand</strong></p> </li> <li> <p><strong>industrial_sites</strong></p> <ul> <li> <p>Information about industrial sites with DSM-potential in Germany from a Master's thesis by Danielle Schmidt. The data set includes own information on the coordinates of every industrial site.</p> </li> <li> <p>source: Schmidt, Danielle. (2019). Supplementary material to the masters thesis: NUTS-3 Regionalization of Industrial Load Shifting Potential in Germany using a Time-Resolved Model [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3613767</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>mastr_geocoding</strong></p> </li> <li> <p><strong>nep2035_version2021</strong></p> <ul> <li> <p>Data extracted from the German grid development plan - power</p> </li> <li> <p>source: Netzentwicklungsplan Strom 2035 (2021), erster Entwurf | Übertragungsnetzbetreiber (M) CC-BY-4.0</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>pipeline_classification_gas</strong></p> <ul> <li> <p>Parameters for the classification of gas pipelines</p> </li> <li> <p>source: Single parameters extracted from <a href="https://www.econstor.eu/bitstream/10419/173388/1/1011162628.pdf">Electricity, Heat and Gas Sector Data for Modelling the German System</a></p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>pypsa_eur</strong></p> </li> <li> <p><strong>regions_dynamic_line_rating</strong></p> <ul> <li> <p>German regions suitable to model dynamic line rating</p> </li> <li> <p>source: Own representation based on <a href="https://www.transnetbw.de/files/pdf/netzentwicklung/netzplanungsgrundsaetze/UENB_PlGrS_Juli2020.pdf">Grundsätze für die Ausbauplanung des Deutschen Übertragungsnetze (2020)</a></p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>re_potential_areas</strong></p> <ul> <li> <p>Eligible areas for wind turbines and ground-mounted PV systems.</p> </li> <li> <p>Reiner Lemoine Institut, January 2022</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>wind_offshore_status2019</strong></p> <ul> <li> <p> </p> </li> </ul> </li> <li> <p><strong>WZ_definition</strong></p> <ul> <li> <p>Definitions of industrial and commercial branches</p> </li> <li> <p>source: <a href="https://www.destatis.de/static/DE/dokumente/klassifikation-wz-2008-3100100089004.pdf">Klassifikation der Wirtschaftszweige (WZ 2008)</a></p> </li> <li> <p>Extract from Terms of Use: © Statistisches Bundesamt, Wiesbaden 2008 Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellenangabe gestattet.</p> </li> </ul> </li> <li> <p><strong>zensus_households</strong><strong> </strong></p> <ul> <li> <p>Dataset describing the amount of people living by a certain types of family-types, age-classes,sex and size of household in Germany in state-resolution.</p> </li> <li> <p>source: Data retrieved from <a href="https://ergebnisse2011.zensus2022.de/datenbank/online">Zensus Datenbank</a> by performing these steps:</p> <ul> <li> <p>Search for: "1000A-2029"</p> </li> <li> <p>or choose topic: "Bevölkerung kompakt"</p> </li> <li> <p>Choose table code: "1000A-2029" with title "Personen: Alter (11 Altersklassen)/Geschlecht/Größe desprivaten Haushalts - Typ des privaten Haushalts (nach Familien/Lebensform)"</p> </li> <li> <p>Change setting "GEOLK1" to "Bundesländer (16)" higher resolution "Landkreise und kreisfreie Städte (412)" only accessible after registration.</p> </li> </ul> </li> <li> <p>Extract from Terms of Use: © Statistische Ämter des Bundes und der Länder 2021, Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellennachweis gestattet.</p> </li> </ul> </li> <li> <p><strong>zensus_population</strong></p> </li> <li> <p><strong>district_heating_shares_egon.csv</strong></p> </li> </ol>
Reference data bundle for PacificBiosciences/wdl-humanassembly
<p>Static input files to support alignment of generated assemblies to both GRCh38 and chm13v2.0 references.</p><p>https://github.com/PacificBiosciences/wdl-humanassembly</p>
Data Bundle for PyPSA-Eur-Sec: A Sector-Coupled Open Optimisation Model of the European Energy System
<p>While small data files used in PyPSA-Eur-Sec are included directly in the git repository, larger ones are collected in this data bundle. The data bundle’s size is around 680 MB.</p> <p><strong>Licenses</strong></p> <p>Different licenses apply to the various components of this data bundle (mostly attribution).</p> <p>For details see <a href="https://pypsa-eur-sec.readthedocs.io/en/latest/installation.html#data-requirements">https://pypsa-eur-sec.readthedocs.io/en/latest/installation.html#data-requirements</a></p> <p><strong>Changelog 0.3.1</strong></p> <ul> <li>Fix IRENASTAT encoding</li> </ul> <p><strong>Changelog 0.3.0</strong></p> <ul> <li>Add <a href="https://pxweb.irena.org/pxweb/en/IRENASTAT">IRENASTAT</a> country-level power generation capacities.</li> </ul> <p><strong>Changelog 0.2.0</strong></p> <ul> <li>add hydrogen salt cavern storage potential (h2_salt_caverns_GWh_per_sqkm.geojson)</li> </ul> <p> </p>
Figure 5. Lumbricillus nivalis, holotype. A, chaetal bundle. B, spermatheca. C in New insights into the systematics of Lumbricillus and Marionina (Clitellata: Enchytraeidae) inferred from Southern Hemisphere samples, including three new species
Figure 5. Lumbricillus nivalis, holotype. A, chaetal bundle. B, spermatheca. C, anterior part of body. D, genitalia. Abbreviations are defined under 'Taxonomy'. Scale bars: 100 μm.
Archival bundle of the data used for "Extending OpenStack Monasca for Predictive Elasticity Control"
<p>This archive contains the data used for the paper</p> <p><strong>Extending OpenStack Monasca for Predictive Elasticity Control</strong><br> <a href="mailto:giacomo.lanciano@sns.it">Giacomo Lanciano</a>*, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella</p> <p> </p> <p>Follow the instructions provided in the <a href="https://github.com/giacomolanciano/predictive-elasticity-monasca">companion repo</a> to automatically download and decompress the archive. The following files are included:</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td> <p>amphora-x64-haproxy.qcow2</p> </td> <td> <p>Image used to create Octavia amphorae</p> </td> </tr> <tr> <td> <p>distwalk-{lin,aim,mlp,rnn,stc}-<INCREMENTAL-ID>.csv</p> </td> <td> <p>Run traces</p> </td> </tr> <tr> <td> <p>distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>.log</p> </td> <td> <p>distwalk run log</p> </td> </tr> <tr> <td> <p>distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>-pred.json</p> </td> <td> <p>Predictive metric data exported from Monasca DB</p> </td> </tr> <tr> <td> <p>distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>-real.json</p> </td> <td> <p>Actual metric data exported from Monasca DB</p> </td> </tr> <tr> <td> <p>distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>-times.csv</p> </td> <td> <p>Client-side response time for each request sent during a run</p> </td> </tr> <tr> <td> <p>model_dumps/*</p> </td> <td> <p>Dumps of the models and data scalers used for the validation</p> </td> </tr> <tr> <td> <p>predictor.log</p> </td> <td> <p>monasca-predictor log</p> </td> </tr> <tr> <td> <p>predictor-times.log</p> </td> <td> <p>monasca-predictor` log (timing info only)</p> </td> </tr> <tr> <td> <p>predictor-times-{lin,mlp,rnn}.{csv,log}</p> </td> <td> <p>monasca-predictor log (timing info only, group by predictor)</p> </td> </tr> <tr> <td> <p>super_steep_behavior.csv</p> </td> <td> <p>Dataset used to train MLP and RNN models</p> </td> </tr> <tr> <td> <p>test_behavior_02_distwalk-6t_last100.dat</p> </td> <td> <p>distwalk load trace</p> </td> </tr> <tr> <td> <p>ubuntu-20.04-min-distwalk.img</p> </td> <td> <p>Image used to create Nova instances for the scaling group</p> </td> </tr> </tbody> </table> <p>This work extends our <a href="https://doi.org/10.1145/3468737.3494104">previous one</a> appeared at the <em>IEEE/ACM 14th International Conference on Utility and Cloud Computing (UCC'21)</em>. </p> <p>* <em>contact author</em></p>
Data bundle for egon-data: A transparent and reproducible data processing pipeline for energy system modeling
<p><strong>egon-data</strong> provides a transparent and reproducible open data based data processing pipeline for generating data models suitable for energy system modeling. The data is customized for the requirements of the research project <strong>eGo<sup>n</sup></strong>. The research project aims to develop tools for an open and cross-sectoral planning of transmission and distribution grids. For further information please visit the eGo<sup>n</sup> <a href="https://ego-n.org/">project website</a> or its <a href="https://github.com/openego/eGon-data">Github repository.</a></p> <p>egon-data retrieves and processes data from several different external input sources. As not all data dependencies can be downloaded automatically from external sources we provide a data bundle to be downloaded by egon-data.</p> <p>The following data sets are part of the available data bundle:</p> <ol> <li><strong>climate_zones_germany</strong> <ul> <li>Climate zones in Germany</li> <li>source: Own representation based on DWD TRY climate zones</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>emobility</strong> <ul> <li>Data on eMobility mit_trip_data:<br> motorized individual travel - individual trips of electric vehicles (EV) generated with a modified version of simBEV v0.1.3 (https://github.com/rl-institut/simbev/tree/1f87c716d14ccc4a658b8d2b01fd12b88a4334d5). simBEV generates driving profiles for BEVs and PHEVs based upon MID data (BMVI) per RegioStaR7 region type (BBSR).</li> <li>Reiner Lemoine Institut, June 2022</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>geothermal_potential</strong> <ul> <li>Spatial distribution of deep geothermal potentials in Germany</li> <li>source: <a href="https://doi.org/10.3390/en11020332">Assessment and Public Reporting of Geothermal Resources in Germany: Review and Outlook</a></li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>household_electricity_demand_profiles</strong> <ul> <li>Annual profiles in hourly resolution of electricity demand of private households for different household types (singles, couples, other) with varying number of elderly and children.<br> The profiles were created using a bottom-up load profile generator by Fraunhofer IEE developed in the Bachelor's thesis "Auswirkungen verschiedener Haushaltslastprofile auf PV-Batterie-Systeme" by Jonas Haack, Fachhochschule Flensburg, December 2012.<br> The columns are named as follows: "<HH_TYPE_PREFIX>a<PROFILE_ID>", e.g. P2a0000 is the first profile of a couple's household with 2 children. See publication below for the list of prefixes. Values are given in Wh.<br> A related conference paper can be obtained here: http://publica.fraunhofer.de/documents/N-374761.html</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>household_heat_demand_profiles</strong> <ul> <li>Sample heat time series including hot water and space heating for single- and multi-familiy houses. The profiles were created using the loadprofile generator by Fraunhofer IEE developed in the Master's thesis "Synthesis of a heat and electrical load profile for single and multi-family houses used for subsequent performance tests of a multi-component energy system", Simon Ruben Drauz, RWTH Aachen University, March 2016</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>hydrogen_storage_potential_saltstructures</strong> <ul> <li>The data are taken from figure 7.1 in Donadei, S., et al., (2020), p. 7-5..</li> <li>Source: Flach lagernde Salze, (c) BGR Hannover, 2021.<br> Datenquelle: InSpEE-Salzstrukturen, (c) BGR, Hannover, 2015. &<br> Donadei, S., Horváth, B., Horváth, P.-L., Keppliner, J., Schneider, G.-S., &<br> Zander-Schiebenhöfer, D. (2020). Teilprojekt Bewertungskriterien und<br> Potenzialabschätzung. BGR. Informationssystem Salz: Planungsgrundlagen,<br> Auswahlkriterien und Potenzialabschätzung für die Errichtung von Salzkavernen<br> zur Speicherung von Erneuerbaren Energien (Wasserstoff und Druckluft) –<br> Doppelsalinare und flach lagernde Salzschichten: InSpEE-DS. Sachbericht.<br> Hannover: BGR.</li> <li>License: The original data are licensed under the GeoNutzV, see https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf</li> </ul> </li> <li><strong>industrial_sites</strong> <ul> <li>Information about industrial sites with DSM-potential in Germany from a Master's thesis by Danielle Schmidt. The data set includes own information on the coordinates of every industrial site.</li> <li>source: Schmidt, Danielle. (2019). Supplementary material to the masters thesis: NUTS-3 Regionalization of Industrial Load Shifting Potential in Germany using a Time-Resolved Model [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3613767</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>nep2035_version2021</strong> <ul> <li>Data extracted from the German grid development plan - power</li> <li>source: Netzentwicklungsplan Strom 2035 (2021), erster Entwurf | Übertragungsnetzbetreiber (M) CC-BY-4.0</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>pipeline_classification_gas</strong> <ul> <li>Parameters for the classification of gas pipelines</li> <li>source: Single parameters extracted from <a href="https://www.econstor.eu/bitstream/10419/173388/1/1011162628.pdf">Electricity, Heat and Gas Sector Data for Modelling the German System</a></li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>pypsa_eur_sec</strong> <ul> <li>Preliminary results from scenario generator pypsa-eur-sec</li> <li>source: own calculation using pypsa-eur-sec fork (https://github.com/openego/pypsa-eur-sec)</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>regions_dynamic_line_rating</strong> <ul> <li>German regions suitable to model dynamic line rating</li> <li>source: Own representation based on <a href="https://www.transnetbw.de/files/pdf/netzentwicklung/netzplanungsgrundsaetze/UENB_PlGrS_Juli2020.pdf">Grundsätze für die Ausbauplanung des Deutschen Übertragungsnetze (2020)</a></li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>re_potential_areas</strong> <ul> <li>Eligible areas for wind turbines and ground-mounted PV systems.</li> <li>Reiner Lemoine Institut, January 2022</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li><strong>WZ_definition</strong> <ul> <li>Definitions of industrial and commercial branches</li> <li>source: <a href="https://www.destatis.de/static/DE/dokumente/klassifikation-wz-2008-3100100089004.pdf">Klassifikation der Wirtschaftszweige (WZ 2008)</a></li> <li>Extract from Terms of Use: © Statistisches Bundesamt, Wiesbaden 2008 Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellenangabe gestattet.</li> </ul> </li> <li><strong>zensus_households</strong> <ul> <li>Dataset describing the amount of people living by a certain types of family-types, age-classes,sex and size of household in Germany in state-resolution.</li> <li>source: Data retrieved from <a href="https://ergebnisse2011.zensus2022.de/datenbank/online">Zensus Datenbank</a> by performing these steps: <ul> <li>Search for: "1000A-2029"</li> <li>or choose topic: "Bevölkerung kompakt"</li> <li>Choose table code: "1000A-2029" with title "Personen: Alter (11 Altersklassen)/Geschlecht/Größe desprivaten Haushalts - Typ des privaten Haushalts (nach Familien/Lebensform)"</li> <li>Change setting "GEOLK1" to "Bundesländer (16)" higher resolution "Landkreise und kreisfreie Städte (412)" only accessible after registration.</li> </ul> </li> <li>Extract from Terms of Use: © Statistische Ämter des Bundes und der Länder 2021, Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellennachweis gestattet.</li> </ul> </li> </ol> <p> </p>
Resource bundle for somatic-conda (hg19)
<p>Resource bundle for the use of somatic-conda workflow (including hg19 only).</p>
Bundle of Iron Arrow Points, Mleiha, Sharjah
Bundle of iron arrow points from Mleiha, Sharjah, UAE. As found corroded together. A number of examples have been found. 1st Century BCE to 1st Century CE. Found in tombs of the period. Overlaet 2018:30-31, CAT No 29. 463 photos. Completely processed (aligned, scaled, modeled, cleaned, simplified, unwrapped, textured, meshed) in Reality Capture. B. Overlaet 2018. Mleiha, An Arab Kingdom on the Caravan Trails (Brussels 30.10-30.12.2018). Sharjah: Sharjah Archaeology Authority. Source: Objaverse 1.0 / Sketchfab
Bundle Reconstitution Assay Dataset
<p>This dataset contains the raw TIRF microscopy data of bundle reconstitution assays, as well as the script used to analyze the data.</p>
Data bundle for "Short communication: The effect of cooling rate and grain size on hydride microstructure in Zircaloy-4"
<p>This is a data bundle for "Short communication: The effect of cooling rate and grain size on hydride microstructure in Zircaloy-4"<br> R.Birch S.Wang V.Tong T. B.Britton<br> Imperial College London, London, SW7 2AZ<br> https://doi.org/10.1016/j.jnucmat.2018.11.011</p> <p>The data bundle was prepared by Ben Britton (b.britton@imperial.ac.uk).</p> <p>The figures are presented in the powerpoint (whcich can be extracted as a zip if needed).</p>
Data Bundle for "Rapid electron backscatter diffraction mapping: Painting by numbers"
<p>This data is a release of EBSD data for "Rapid electron backscatter diffraction mapping: Painting by numbers"<br> Figure 5 and Figure 6 contain the EBSD data.<br> FFArgus.png = far field ARGUS image <br> NFArgus.png = near field ARGUS image<br> IPF = image data for the EBSD data<br> *.ctf = export of Bruker CTF data for full EBSD map to plot EBSD maps (e.g. in MTEX)<br> *.txt = reconstructed EBSD data in columns: euler1 euler 2 euler 3 euler 3 xpos ypos phaseID<br> *.prg = Bruker project file (use this to link the EBSD patterns to the NF Argus image)<br> EBSP folder = EBSPs as captured.</p> <p>The data bundle was prepared by Ben Britton (b.britton@imperial.ac.uk).</p> <p>The figures are presented in the powerpoint (which can be extracted as a zip if needed).</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.