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.
722
datasets available to search
ShareScore release 0.9.0
Dataset results
722 results for “use case”
H2020 Platone German Demonstrator Use Case 1 Asset and Topolgy Data
<p>This dataset asset and topology data of the field test setup. The dataset gives details about:</p> <p>- Low Voltage (LV) network</p> <p>- Tranformer located in the secondary substation</p> <p>- Community Battery Energy Storage System (CBES) connected to the LV-busbar in the 2nd. Substation</p> <p>- PV and number of households located in the energy community</p> <p>- PV installed generation power of the community</p> <p>- Domestic Storages and Inverter</p> <p> </p>
H2020 Platone German Demonstrator Use Case 1 Market Data
<p>This dataset contains settings of the Local Energy Management System, that have been set via a Graphical User Interface (GUI). The dataset contain follwowing data:</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Timestamp</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Use Case</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_UC_ID</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Option</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Priority</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Submission_Time</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_UC_Start_Date</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_UC_End_Date</p> <p> </p> <p> </p>
H2020 Platone Italian Demonstrator Use Case 1-2 Measurements
<p>Description of the database "areti_profile_flexibility_customer_2021":</p> <p>Data about the flexibility measurement of the users involved in the trial.</p> <p>In the database you will find: </p> <p>Date: Measurement date (Mmm dd, yyyy);<br> Timestamp: Measurement time (hh:mm:ss.sss @UTC);<br> pod: Point of Delivery of the users' place (PoD) identification code;<br> measures.energy.absorbedActiveEnergy.value: Quarter-hour sample of active energy absorbed (kWh) [as per ID 6 in Tab. A.5 - CEI 13-82];<br> measures.energy.injectedActiveEnergy.value: Quarter-hour sample of active energy injected (kWh) [as per ID 7 in Tab. A.5 - CEI 13-82];<br> measures.energy.absorbedInductiveReactiveEnergy.value: Quarter-hour sample of inductive reactive energy when active energy absorbed (kVARh) [as per ID 9 in Tab. A.5 - CEI 13-82]; <br> measures.energy.absorbedCapacitiveReactiveEnergy.value: Quarter-hour sample of capacitive reactive energy when active energy absorbed (kVARh) [as per ID 10 in Tab. A.5 - CEI 13-82]; <br> measures.energy.injectedInductiveReactiveEnergy.value: Quarter-hour sample of inductive reactive energy when active energy injected (kVARh) [as per ID 11 in Tab. A.5 - CEI 13-82]; <br> measures.energy.injectedCapacitiveReactiveEnergy.value: Quarter-hour sample of capacitive reactive energy when active energy injected (kVARh) [as per ID 12 in Tab. A.5 - CEI 13-82];<br> measures.power.activePower.value: Quarter-hour average of active power exchange (kW) [as per ID 16 in Tab. A.5 - CEI 13-82];</p> <p> </p> <p>(Useful link to consult Italian UC:</p> <p><a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-1-voltage-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=ptAsS52VBberHmZqIzYEXZs1PQrXQ6TDz6mNK%2FNWnk0%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-1-voltage-management/</a></p> <p><a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-2-congestion-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=xWNOqSeS5JxDoBEWZ4aB63gLmsnTA8YGGfCOoLjo1eo%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-2-congestion-management/</a></p> <p><a href="https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf">https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf</a>)</p>
Dataset for Sandboxing use case SUC2 related to cyber attacks affecting Wide Area Protection
<p><span>This dataset is related to the operation of the second KIOS CoE sandboxing use case (SUC2) which inclused 3 scenarios (S1-S3) which examins the behavious a WAP scheme of power grids in case of a short circuit fault and in case of two types of cyber attacks. The description of the architecture of the University of Cyprus/ KIOS CoE sandboxing environmnet used for extracting these datasets along with the full list of scenarios and their detailed implementation are described in the supporting documents.</span></p> <p><span>Brief description of each of the 3 scenarios of this SUC2 are provided below.</span></p> <p><span>The datasets for the first scenario (S1) of SUC2</span><span> examines the operation of a wide area protection scheme in a transmission line which receives data sent from PMUs at the two ends of the lines, when a short-circuit fault occurred in the range of the transmission line between buses 7 and 8 of the system. More details about the scenario SUC2/S1 related to this scenario's dataset can be found in Section </span><span>1.3.1</span><span> of the SUC2 supporting document. </span><span><span>The dataset includes electrical measurements of the current flow in line 7-8 (of the IEEE 9-bus system), in both magnitude and sinusoidal form</span><span>.</span><span> The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files, which were recorded with a 30-second and 40-second time resolution, respectively. The measurements of RMS values were recorded by the Typhoon controller as they were sent by the two PMUs, while the sine wave measurements were recorder through the OPAL-RT</span></span></p> <p><span>The datasets for second scenario (S2) of SUC2 investigates the operation of a wide area protection scheme which receives data sent from PMUs when a MITM FDI cyber-attack is conducted on the measurements of bus 7</span><span>, virtually implemented within the sandboxing, and introduces a multiplicative change to the current measurements before they are received by the Typhoon controller via IEEE C37.118 protocol</span><span>. Section 1.3.2 of the SUC2 supporting document provides more details about the scenario related to this dataset. </span><span>This dataset includes electrical measurements of the current flow, in magnitude and sinusoidal format, of the transmission line between buses 7 and 8 of the <span>digital twin of the IEEE 9-bus system.</span> The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files which were recorded with a 30-second and 40-second time resolution, respectively. The measurements of magnitude values were recorded by the Typhoon controller, while the data from the sinusoidal waveform were recorder by OPAL-RT. </span></p> <p><span>Thie dataset of the SUC2/S3 examines the operation of a wide area protection scheme which receives data sent from PMUs when a combined MITM with DoS cyber-attack is conducted, as actual attack, in the isolated communication network of the sandboxing environment, disrupting the C37.118 UDP communication exchanged between OPAL-RT 5707, where the digital twin of IEEE 9-bus system was implemented, and Typhoon controller. More details about this scenario associated to this dataset can be found in Section </span><span>1.3.3<span></span></span><span> of the supporting document of SUC2.</span></p> <p><span>This dataset includes electrical measurements of current’s flow magnitude of the transmission line between buses 7 and 8 of the <span>digital twin of the IEEE 9-bus system.</span> The dataset was recorded by the Typhoon controller, and it is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files which were recorded with a 30-second and 40-second time resolution, respectively. In addition, the dataset includes network traffic packets captured as .pcapng<span> </span>and .csv files. <span> </span></span></p>
Stocktaking GO FAIR Discovery IN - Use cases, infrastructure
<p>In order to build a better ecosystem for data discovery tools the Data Discovery Implementation Group of GO Fair (https://www.go-fair.org/implementation-networks/overview/discovery) collected use cases between 2019 and 2020 from a variety of sources. We also detail the ‘Actors’ for these use cases and the ‘Source’ providing links, whenever possible. Since we found over a hundred individual use cases, we decided to cluster them to provide a better overview. The clustering, as well as the results of a small survey among data infrastructure specialists to find how they rate the importance of the clusters are detailed in the documentation to this dataset, a draft of which can currently be found <a href="https://docs.google.com/document/d/1sq78eCFYgmWcMFYcbNonA2KrkUO1qdGr7d49tHuRcRM/edit?usp=sharing">here</a>. The code and data to produce the figures in the documentation are available as R code in the GO_FAIR_Discovery_Use_case-master.zip file. The use cases themselves are available as Excel sheet and csv. </p>
Virtual Reality Dataset used for Proof of Concept in the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION)
<p>This dataset contains the <strong>dataset </strong>used in the Virtual Reality POC for the validation of the Conflict Detection and Resolution (CD&R) use case.</p> <p>This dataset represent a extract of different (using K-means) candidate solution, either good or bad ones.</p>
Solutions and Genetic algorithm dataset of the Scenarios used for the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION )
<p>This dataset contains the <strong>solution </strong>of the scenarios used for one of the validation of the ARTIMATION project: Conflict Detection and Resolution (CD&R) use case (link).</p> <p>The solution are computed by a Genetic Algorithm developped by Nicolas Durand.<br> <br> Inside, one can find:</p> <p>-One archive, "GA_Scenario_Solution_Dataset.zip", containing 10 couple of files (so 20 files). Each couple of file "sol_X_1.csv" and "sols_X_1.csv" are reciprocally the solutino given by the Genetic Algorithm to scenario X, and all the candidate solution explroed by the GA while solving scenario X. This archive also contain other versions of the solutions made by the GA with other parameters.<br> <br> -One archive, "GA_Toy_Dataset.zip" , containing solution to random scenarios, used to develop the first interfaces.</p> <p>Those solutions are used to developp the heatmatrix and heatmaps of the project (link), and visualisations for the validation (link).</p>
Heatmatrix and Heatmap Layers and Alternatives used in the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION)
<p>This dataset contains the <strong>visualisations </strong>of the solutions of Conflict Detection and Resolution (CD&R) use case.</p> <p>The solution are computed by a Genetic Algorithm developped by Nicolas Durand.<br> <br> Inside, one can find:</p> <p>-One archive, "Heatmatrix.zip" , containing the heatmatrix creating using the solutions dataset.</p> <p>-One archive, "Heatmaps_Layer_Alternatives.zip", containing all the layers created and used to created the heatmaps, the heatmaps, and alternative heatmaps (with other candidate solutions).</p> <p>Those layers and heatmaps are used to develop other visualisation used in the validation.</p>
Validation Videos and eXplainable levels used in the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION)
<p>This dataset contains the <strong>explaination levels and the video </strong>used in the validation of the Conflict Detection and Resolution (CD&R) use case.</p> <p>The solution are computed by a Genetic Algorithm developped by Nicolas Durand.<br> <br> Inside the dataset, one can find:</p> <p>-One archive, " Validation_Videos_Traffic.zip ", containing the video of traffic of every scenario.</p> <p>-One archive, " Validation_XAI_levels.zip", containing the Blackbox, Heatmap, and Storyboard eXplainable levels for each scenario.</p> <p>The videos and XAI levels are used in the validation exercice.</p>
Dataset of a 5G RTSP video streaming use case
<p><strong>About the project: monitoring 5G RTSP video streaming</strong></p> <p>This dataset collects data from a 5G video streaming use case. A video is streamed by a cvlc server (realized as a Kubernetes pod) through RTSP to a variable number of 5G UE clients that activate according to a daily traffic pattern. The values of the 4 dataset features (number of active UEs, gNB's downlink bit rate, pod's outbound traffic, and pod's CPU usage) are collected by a custom monitoring system deployed in the context of the MONB5G project.</p> <p><strong>Setup/Equipment</strong></p> <p>The Kubernetes cluster, including the server pod, runs in a COTS server. The 5G core and gNB is realized through Amarisoft Callbox Ultimate. The UEs are emulated through Amarisoft Simbox. In order to display the video in ffplay clients, we use the Remote UE from Amarisoft, so traffic from Simbox is forwarded to an external VM with GUI.</p> <p><strong>Video</strong></p> <p>The streamed video is Big Buck Bunny at 30 FPS from <a href="https://peach.blender.org/">https://peach.blender.org/</a>.</p> <table> <tbody> <tr> <td> <p>Video codec </p> </td> <td> <p>Advanced Video Codec (AVC) </p> </td> </tr> <tr> <td> <p>Width </p> </td> <td> <p>1920 pixels </p> </td> </tr> <tr> <td> <p>Height </p> </td> <td> <p>1080 pixels </p> </td> </tr> <tr> <td> <p>Display aspect radio </p> </td> <td> <p>16:9 </p> </td> </tr> <tr> <td> <p>Duration </p> </td> <td> <p>10 min 34 s </p> </td> </tr> <tr> <td> <p>Max Bitrate </p> </td> <td> <p>16.7 Mb/s </p> </td> </tr> <tr> <td> <p>Frame rate </p> </td> <td> <p>30 FPS </p> </td> </tr> </tbody> </table> <p><strong>What does this Zenodo project contain?</strong></p> <ol> <li>The csv file of the dataset (dataset.csv)</li> <li>A picture displaying an overview of the setup (overview.png)</li> <li>A picture displaying Grafana charts for each featuer (grafana.png)</li> <li>A picture displaying a screenshot of the Remote UE VM with multiple UEs playing the video (ues.png)</li> </ol> <p><strong>Dataset</strong></p> <p>The dataset has 5 columns (time + 4 features). Features:</p> <ol> <li><em>Time</em>: timestamp in epoch format.</li> <li><em>Number of active UEs (N)</em>: number of UEs that are currently downloading more than 100 kbps. No unit.</li> <li><em>gNB's downlink bit rate (R)</em>: aggregate downlinkg bitrate from the gNB to all the UEs. In Mbps.</li> <li><em>Outbound traffic (O)</em>: outboun traffic at the pod's interface, transmitting the video(s) packets. In Mbps.</li> <li><em>CPU (C)</em>: CPU usage at the server pod. In millicores (mc). Each iteration represents a whole day, composed of 24 "demand periods". Each demand period takes 2 minutes and is given by the number of active UEs consuming the video stream (N). N is included for informative reasons. Sampling rate is 10 seconds, but some parameters are refreshed at a lower frequency given monitoring limitations. This means that some parameters repeat the same value in consecutive measurements.</li> </ol>
VICINITY IoT Use Case Data Sets
<p>This repository contains Internet-of-Things (IoT) use case data sets from the VICINITY project. The use cases are described under https://vicinity2020.eu, where also the contributing companies are described in more detail. </p> <p>The use cases include: </p> <ul> <li>eHealth use case data by GNOMON/CERTH (anonymized)</li> <li>Smart parking, building control use case data by HITS, TINYM</li> <li>Energy management data set by ENERC</li> <li>Several other, smaller use cases with less data from open call winners. </li> </ul> <p>The data sets have also been the basis for the publications available for download under the same URL; the folder AAU gives additionally the data from AAU's publications. </p> <p> </p>
Excavator-generated information from Linux drivers (Decoder Use-Case A)
<p>This dataset is released as part of DECODER's D6.2 deliverable. It contains the information generated by the Excavator tool for easing the verification with Frama-C of the watchdog and ethernet Linux drivers that have been selected as Use-Case A of the project.</p>
Artificial COVID-19 Cases in Paris and Geographic Data Useful for Geomasking
<p>Artificial dataset of addresses of COVID-19 cases in Paris. The dataset was created to test geomasking techniques to be used on the real data collected by the French health administration. The dataset was used in the paper "Geographically Masking Addresses to Study COVID-19 Clusters" by Walid Houfaf-Khoufaf and Guillaume Touya. The dataset contains the following files:</p> <ul> <li>roads_paris_IGN.shp contains the road lines from IGN France in Paris;</li> <li>buildings_paris_IGN.shp contains the building polygons from IGN France in Paris (useful to aggregate points to building groups);</li> <li>faces.shp contains the blocks built from the roads (useful to aggregate points to blocks);</li> <li>ban_75.shp contains all the address points in Paris from the open BAN database.</li> <li>artificial_COVID_cases.csv contains the artificial COVID cases generated from 3 months in 2020 in the Paris area.</li> </ul> <p> </p>
Agricultural land use and livestock composition by case study of the SURE-Farm project - Input data for a dynamic nitrogen flow model
<p>Dataset used as input to the model by Pinsard et al (2021) and results published in D5.5 of the SURE-Farm project.</p>
Identification at local and global scale: a case for using the Compact URI (CURIE) for life science data
<p>Panel A) A Local Resource Identifier (LRI) is not suited to global scale identification because of inevitable collisions: “9606” corresponds to a Pubmed article, a CGNC gene, a PubChem chemical, as well as an NCBI taxon (<em>Homo sapiens</em>), a BOLD taxon (<em>Bombycilla</em> <em>cedrorum</em>), and a GRIN taxon (<em>Catha</em> <em>edulis</em>)</p> <p>Panel B) Prefixing is often used to indicate the source of an LRI, but prefixes themselves are often undocumented and collide.</p> <p>Panel C) Prefixes may exist in alternate forms. When all of the alternates are not known, collapsing equivalent identifiers is tedious and incomplete.</p> <p>Panel D) CURIE syntax addresses these issues by having a prefix whose relationship with a resolving namespace is clearly documented.</p>
Jisc Research Data Shared Service metadata focus group use cases
<p>Dataset of use cases collected between July and October 2016 during a series of metadata focus groups conducted with a number of the <strong>Research Data Shared Service</strong> pilots who volunteered for the process.</p> <p>This dataset is available in two formats (including an open format) with the same content: 180 use cases in the following user story structure: </p> <ul> <li><strong>As a</strong></li> <li><strong>Theme</strong></li> <li><strong>I want </strong></li> <li><strong>So that</strong></li> <li><strong>Comments</strong></li> </ul> <p>The .xlsx file contains additional formatting grouping the use cases by theme, role, data and community.</p>
FAIR Evaluations of University and College Repositories at DataCite Using MetaDIG Mappings for Four Use Cases.
<p>This spreadsheet has the results of an evaluation of FAIRness of 387 University and College DataCite repositories using techniques developed in the MetaDIG project. It is possible to compare scores from different repositories and to create rose diagrams showing the results for any of the repositories.</p>
Received power for Reconfigurable Intelligent Surface use case
<p>Received power in the sampled analyzed area (shown in file area.png) in a scenario with single room with a short wall, and signal transmitter behind this wall. Output was generated with usage of the MATLAB raytracing tool. </p> <p>Analyzed area was sampled in both dimensions, creating 99 points in x-axis (values between 3 meters to 7.9 meters with 5 cm step), and 89 points in y-axis (values between 2.2 meters to 6.6 meters with 5 cm step). Reconfigurable Intelligent Surface was placed with 9 configurations of angle (angle between RIS surface and wall) from -20 to 20 degrees with 5 degree step. This setup created 9 files (for each angle) and inside file there is received power for each analyzed area point (99x89).</p> <p> </p> <p>The work has been funded by the National Science Centre in Poland within the project (no. 2021/43/B/ST7/01365) of the OPUS programme.</p>
A stakeholder-centered determination of High-Value Data sets: the use-case of Latvia
<p>The data in this dataset were collected in the result of the survey of Latvian society (2021) aimed at identifying high-value data set for Latvia, i.e. data sets that, in the view of Latvian society, could create the value for the Latvian economy and society.<br> The survey is created for both individuals and businesses.<br> It being made public both to act as supplementary data for "Towards enrichment of the open government data: a stakeholder-centered determination of High-Value Data sets for Latvia" paper (author: Anastasija Nikiforova, University of Latvia) and in order for other researchers to use these data in their own work.</p> <p>The survey was distributed among Latvian citizens and organisations. The structure of the survey is available in the supplementary file available (see Survey_HighValueDataSets.odt)</p> <p>***Description of the data in this data set: structure of the survey and pre-defined answers (if any)***<br> 1. Have you ever used open (government) data? - {(1) yes, once; (2) yes, there has been a little experience; (3) yes, continuously, (4) no, it wasn’t needed for me; (5) no, have tried but has failed}<br> 2. How would you assess the value of open govenment data that are currently available for your personal use or your business? - 5-point Likert scale, where 1 – any to 5 – very high<br> 3. If you ever used the open (government) data, what was the purpose of using them? - {(1) Have not had to use; (2) to identify the situation for an object or ab event (e.g. Covid-19 current state); (3) data-driven decision-making; (4) for the enrichment of my data, i.e. by supplementing them; (5) for better understanding of decisions of the government; (6) awareness of governments’ actions (increasing transparency); (7) forecasting (e.g. trendings etc.); (8) for developing data-driven solutions that use only the open data; (9) for developing data-driven solutions, using open data as a supplement to existing data; (10) for training and education purposes; (11) for entertainment; (12) other (open-ended question)<br> 4. What category(ies) of “high value datasets” is, in you opinion, able to create added value for society or the economy? {(1)Geospatial data; (2) Earth observation and environment; (3) Meteorological; (4) Statistics; (5) Companies and company ownership; (6) Mobility}<br> 5. To what extent do you think the current data catalogue of Latvia’s Open data portal corresponds to the needs of data users/ consumers? - 10-point Likert scale, where 1 – no data are useful, but 10 – fully correspond, i.e. all potentially valuable datasets are available<br> 6. Which of the current data categories in Latvia’s open data portals, in you opinion, most corresponds to the “high value dataset”? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}<br> 7. Which of them form your TOP-3? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}<br> 8. How would you assess the value of the following data categories?<br> 8.1. sensor data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable<br> 8.2. real-time data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable<br> 8.3. geospatial data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable<br> 9. What would be these datasets? I.e. what (sub)topic could these data be associated with? - open-ended question<br> 10. Which of the data sets currently available could be valauble and useful for society and businesses? - open-ended question<br> 11. Which of the data sets currently NOT available in Latvia’s open data portal could, in your opinion, be valauble and useful for society and businesses? - open-ended question<br> 12. How did you define them? - {(1)Subjective opinion; (2) experience with data; (3) filtering out the most popular datasets, i.e. basing the on public opinion; (4) other (open-ended question)}<br> 13. How high could be the value of these data sets value for you or your business? - 5-point Likert scale, where 1 – not valuable, 5 – highly valuable<br> 14. Do you represent any company/ organization (are you working anywhere)? (if “yes”, please, fill out the survey twice, i.e. as an individual user AND a company representative) - {yes; no; I am an individual data user; other (open-ended)}<br> 15. What industry/ sector does your company/ organization belong to? (if you do not work at the moment, please, choose the last option) - {Information and communication services; Financial and ansurance activities; Accommodation and catering services; Education; Real estate operations; Wholesale and retail trade; repair of motor vehicles and motorcycles; transport and storage; construction; water supply; waste water; waste management and recovery; electricity, gas supple, heating and air conditioning; manufacturing industry; mining and quarrying; agriculture, forestry and fisheries professional, scientific and technical services; operation of administrative and service services; public administration and defence; compulsory social insurance; health and social care; art, entertainment and recreation; activities of households as employers;; CSO/NGO; Iam not a representative of any company<br> 16. To which category does your company/ organization belong to in terms of its size? - {small; medium; large; self-employeed; I am not a representative of any company}<br> 17. What is the age group that you belong to? (if you are an individual user, not a company representative) - {11..15, 16..20, 21..25, 26..30, 31..35, 36..40, 41..45, 46+, “do not want to reveal”}<br> 18. Please, indicate your education or a scientific degree that corresponds most to you? (if you are an individual user, not a company representative) - {master degree; bachelor’s degree; Dr. and/ or PhD; student (bachelor level); student (master level); doctoral candidate; pupil; do not want to reveal these data}</p> <p>***Format of the file***<br> .xls, .csv (for the first spreadsheet only), .odt</p> <p>***Licenses or restrictions***<br> CC-BY</p> <p> </p> <p> </p>
H2020 Platone German Demonstrator Use Case 1 Measurement Data
<p>This dataset contains measurement datas collected from mesurements devices in the field (substation, battery storage, etc) during the application of Use Case 1 (Islanding/Maximization of local self-consumption).</p> <p> </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.