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417 results for “federal”
Federal sampler data for Saddle and C1, 2016 - ongoing.
We use an aluminum Standard Federal Snow Sampler to measure snow depth and snow water equivalent (SWE). This device, compared to traditional snow pit techniques, provides efficient measurement capabilities that are less disruptive to the snowpack. Manual Federal Sampler measurements provide high accuracy point data to determine the amount of water in the surrounding snowpack. With a high downstream reliance on water within the regional snowpack, our weekly measurements give a time series of SWE throughout the winter season, offering consistent and dependable information for water resource planning and preparation.
Federation of Vehicular Data in Smart Villages with Socioeconomic Information
<p>We present a dataset tailored for monitoring vehicle activity in a rural environment, specifically the Barranco de Poqueira region, covering the municipalities of Pampaneira, Bubión, and Capileira within the Sierra Nevada National Park, Granada, Spain. The dataset is generated by four Hikvision License Plate Recognition (LPR) cameras, capturing vehicle entries and exits in each village. To enrich the dataset, we include additional contextual details such as vacation calendars, vehicle origins, and socio-demographic information. Spanning from February 2022 to August 2023, the dataset is organized into three files: one with raw data directly from the cameras, another aggregated at the visit level with contextual information, and a third aggregated by vehicles with context details. With potential applications in mobility studies, urban planning, tourism, and socio-demographic analysis, the dataset is structured into three distinct files, encompassing a total of 43 different variables.</p> <p>The <strong>RAW_SMART_POQUEIRA.csv</strong> file contains information about 4 variables: num_plate_ID, camera_ID, date, and direction.</p> <p>The file <strong>VEHICLES_SMART_POQUEIRA.csv</strong> contains information about 33 variables: num_plate_ID, visit_time, distance, num_holiday, num_workday, num_high_season, num_low_season, entry_in_high_season, entry_in_holiday, nights, visits_dif_weeks, visits_dif_months, total_entries, avg_visit, std_visit, avg_nights, std_nights, avg_holiday, std_holiday, avg_workday, std_workday, avg_high_season, std_high_season, avg_low_season, std_low_season, route, country, km_to_dest, population, avg_gross_income, avg_disposable_income, autonomous_community, and province.</p> <p>The file <strong>VISITS_SMART_POQUEIRA.csv</strong> contains information about 26 variables: num_plate_ID, entry_cam, entry_date, entry_time, exit_cam, exit_date, exit_time, visit_time, route, distance, num_holiday, num_workday, num_high_season, num_low_season, nights, visits_dif_weeks, visits_dif_months, entry_in_holiday, entry_in_high_season, country, km_to_dest, population, avg_gross_income, avg_disposable_income, autonomous_community, and province.</p>
Federer Chronosequence: Litterfall Biomass and Chemistry; 1993-1997, 2003-2006
The project reports the flux of biomass and nutrients in leaf litterfall in 14 stands (the “Federer Chronosequence”) of northern hardwood forests. These 14 stands are located in the White Mountains of New Hampshire. Monitoring occurred from August 1993 through summer 1997 and again from August 2003 through summer 2006. The litterfall year is defined as starting in August (when we first set out baskets) and ending in August of the following year, years are named by the fall (in which most litter falls). In some years, we have litter mass by basket (3 per transect): 1993, 1994, 1995, 2003, 2005. In other years, we have only transect means: 1996, 2004. Seasonal masses are provided for 2005. Litterfall was sorted by species in all years except for 2005. Twig mass is reported only for 2005. Litter chemistry was measured in fresh litter samples collected in the same stands from 1994 through 2004. Raw, unedited data sorted by season can be found in “Other Entities”, though note the substantial changes that occurred between these values and the processed values published in this dataset. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. Related publications: Yanai, R. D., Arthur, M. A., Acker, M., Levine, C. R., & Park, B. B. (2012). Variation in mass and nutrient concentration of leaf litter across years and sites in a northern hardwood forest. Canadian Journal of Forest Research, 42(8), 1597-1610. Acker, M. 2006.Base cation concentration and content in litterfall and woody debris across a northern hardwood forest chronosequence. MS Thesis. Lexington, KY: University of Kentucky. Yang, Y., Yanai, R. D., See, C. R., & Arthur, M. A. (2017). Sampling effort and uncertainty in leaf litterfall mass and nutrient flux in northern hardwood forests. Ecosphere, 8(11), e01999.
Swiss Federal Supreme Court Dataset (SCD)
<p>The Swiss Federal Supreme Court Dataset (SCD) provides a record of all 127,477 cases decided by the Swiss Federal Supreme Court between 2007 and December 2024. The SCD includes 31 variables that document basic case information, the court composition, the area of law, information about the appealed judgment, the parties, the case outcome, and about citations and publication status.</p> <p>The dataset can be used as data infrastructure for both qualitative and quantitative analysis of Federal Supreme Court jurisprudence. It is generated using a fully automated pipeline and will be updated regularly to include the latest judgments and possible expansions.</p>
Federalism Conflicts on the Constitutionality of Laws in Mexico (01.01.1995-21.06.2021)
<p>This dataset Federalism Conflicts on the Constitutionality of Laws in Mexico during a selected period. The dataset covers judgments issued on "constitutional controversies," a procedure through which the Mexican Supreme Court resolves federalism conflicts. The dataset provides information pertaining to cases filed and resolved, including the plaintiff, the defendant, and the type of normative provision challenged. It also provides an overview of the outcomes of all normative provisions challenged (upheld, invalidated, dismissed out of procedural grounds, or dismissed out of the lack of a supermajority vote to invalidate them). Data was downloaded on June 22, 2022, and manually classified in the following months. The dataset may be employed to calculate unconstitutionality rates or to study the impact of supermajority rules on the role of constitutional courts as arbiters of federalism conflicts. Full text for all judgments is provided.</p>
MNIST-Federated-Learning
<p>Please find below the descriptions of the three configurations for partitioning <strong>the MNIST Train dataset into 10 clients and the MNIST Train data: </strong><br> </p> <ol> <li><strong>Balanced Distribution:</strong> In the first configuration, the MNIST dataset is partitioned among 10 clients in a balanced manner. This means that the data samples from each class are evenly distributed among the clients. Each client receives a roughly equal number of images from each digit class, ensuring that the distribution of samples across clients is proportional and representative of the overall dataset. <strong> [ Config 1]</strong></li> <li><strong>Heterogeneous Distribution (One Class per Client)</strong>: In the second configuration, the MNIST dataset is partitioned in a heterogeneous manner, where each client is assigned a single digit class exclusively. This means that one client will only receive images of the digit '0', another client will receive images of the digit '1', and so on. In this setup, each client becomes an expert in classifying a specific digit, allowing for specialized training and evaluation. <strong>[ Config 2]</strong></li> <li><strong>Mixed Distribution:</strong> In the third configuration, the MNIST dataset is partitioned using a mixed distribution approach. This means that the data samples from all digit classes are distributed among the 10 clients, but the distribution is not necessarily balanced. The number of samples assigned to each client may vary for different digit classes, resulting in an uneven distribution across the clients. This configuration aims to capture both the overall diversity of the dataset and the varying difficulty levels of classifying different digits. <strong>[ Config 3 ]</strong></li> </ol> <p> </p> <p>Mnist-dataset/<br> ├── config1/<br> │ ├── client-1/<br> │ │ └── data.csv<br> │ ├── client-2/<br> │ │ └── data.csv<br> │ ├── client-3/<br> │ │ └── data.csv<br> │ └── ...<br> ├── config2/<br> │ ├── client-1/<br> │ │ └── data.csv<br> │ ├── client-2/<br> │ │ └── data.csv<br> │ ├── client-3/<br> │ │ └── data.csv<br> │ └── ...<br> ├── config3/<br> │ ├── client-1/<br> │ │ └── data.csv<br> │ ├── client-2/<br> │ │ └── data.csv<br> │ ├── client-3/<br> │ │ └── data.csv<br> │ └── ...<br> └── mnist_test.csv<br> </p> <p>***</p> <p>License: Yann LeCun and Corinna Cortes hold the copyright of MNIST dataset, which is a derivative work from original NIST datasets. MNIST dataset is made available under the terms of the <a href="https://creativecommons.org/licenses/by-sa/3.0/">Creative Commons Attribution-Share Alike 3.0 license.</a></p> <p>***</p>
Árvores medicinais do Horto de Plantas Medicinais da Universidade Federal da Grande Dourados
<p>No vídeo, apresentam-se algumas espécies de plantas medicinais arbóreas, existentes no Horto de Plantas Medicinais, da Universidade Federal da Grande Dourados, em Dourados-MS, Brasil. Destacam-se seu uso medicinal e a importância para a fauna em uma área urbana, especialmente, para os pássaros, borboletas e abelhas.</p>
Data of Female members of National Federations Sport Governing Boards. Database GESPORT Project.
<p>This database has been built by the authors. The data has been collected from the websites of the national federations of Italy, Portugal, Turkey, Spain and the United Kingdom in 2018.</p> <p>With the support of the European Commission. Erasmus+ Project. "Corporate governance in sport organizations: a gendered approach". Project Reference -EPP-1-2017-1-ES-SPO-SCP</p>
Defective Drugs in Russian Federation
<p>Using this dataset it's possible to estimate control measures over quality control of medicines in Russian Federation. Datasets provide information about events of control and destruction of defective drugs.</p>
National Checklists 2017: Federated States of Micronesia Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Federated States of Micronesia collected using effechecka and geonames polygons
National Checklists 2019: Federated States of Micronesia Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Federated States of Micronesia collected using effechecka and geonames polygons
btw17 query auto completion - query suggestions for German politicians and parties before the federal election 2017
<p>The dataset contains the query suggestions for 5 major German parties (terms: "afd", "csu", "dielinke", "fdp", "grüne", "spd") and ten popular politicians and party leaders (terms: "Alexander Gauland", "Alice Weidel", "Angela Merkel", "Cem Özdemir", "Christian Lindner", "Dietmar Bartsch", "Katrin Göring-Eckardt", "Martin Schulz", "Sahra Wagenknecht").</p> <p>The data was crawled on (mostly) two times per day from Tue Aug 04, 2017 to Tue Oct 31, 2017. The dataset contains 20001 suggestions from Bing search (http://api.bing.net/osjson.aspx), 11935 suggestions from Duck-Duck-Go (https://duckduckgo.com/ac/) and 33521 suggestions from Google search (http://clients1.google.de/complete/search). Note, that for some terms and dates no suggestions were returned by some of the APIs.</p> <p>German language settings were used for Google and Bing, English language setting was used for Duck-Duck-Go. The API requests were sent with an IP address from Cologne, Germany. </p> <p>The UTF-8 encoded comma separated text file contains the following columns:</p> <p><source>: google, bing or ddg</p> <p><queryterm>: the query term</p> <p><date>: the date and time of the API call formatted as ISO8601</p> <p><suggestterm>: the suggested query completion (the query term was removed from the suggestion)</p> <p><position>: the position of the query suggestion within the list returned by the API (ranges from 0 to 19)</p> <p> </p> <p> </p> <p><br> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Federated Learning for Distributed Intrusion Detection Systems in Public Networks - Validation Dataset
<p>This dataset has been meticulously prepared and utilized as a validation set during the evaluation phase of "Meta IDS" to asses the performance of various machine learning models. It is now made available for interested users and researchers who seek a reliable and diverse dataset for training and testing their own custom models.</p> <p>The validation dataset comprises a comprehensive collection of labeled entries, that determines whether the packet type is "malicious" or "benign." It covers complex design patterns that are commonly encountered in real-world applications. The dataset is designed to be representative, encompassing edge and fog layers that are in contact with cloud layer, thereby enabling thorough testing and evaluation of different models. Each sample in the dataset is labeled with the corresponding ground truth, providing a reliable reference for model performance evaluation.</p> <p> </p> <p>To ensure convenient distribution and storage, the dataset has been broken down into three separate batches, each containing a portion of the dataset. This allows for convenient downloading and management of the dataset. The three batches are provided as individual compressed files.</p> <p> </p> <p>In order to extract the data, follow the following instructions:</p> <ul> <li>Download and install bzip2 (if not already installed) from the official website or your package manager.</li> <li>Place the compressed dataset file in a directory of your choice.</li> <li>Open a terminal or command prompt and navigate to the directory where the compressed dataset file is located.</li> <li>Execute the following command to uncompress the dataset: <ul> <li>bzip2 -d filename.bz2</li> </ul> </li> <li>Replace "filename.bz2" with the actual name of the compressed dataset file.</li> </ul> <p>Once uncompressed, you will have access to the dataset in its original format for further exploration, analysis, and model training etc. The total storage required for extraction is approximately 800 GB in total, with the first batch requiring approximately 302 GB, the second batch requiring approximately 203 GB, and the third batch requiring approximately 297 GB of data storage.</p> <p> </p> <p>The first batch contains 1,049,527,992 entries, where as the second batch contains 711,043,331 entries, and for the third and last batch we have 1,029,303,062 entries. The following table provides the feature names along with their explanation and example value once the dataset is extracted.</p> <p> </p> <table align="left"> <thead> <tr> <th scope="col">Feature</th> <th scope="col">Description</th> <th scope="col">Example Value</th> </tr> </thead> <tbody> <tr> <td>ip.src</td> <td>Source IP address in the packet</td> <td>a05d4ecc38da01406c9635ec694917e969622160e728495e3169f62822444e17</td> </tr> <tr> <td>ip.dst</td> <td>Destination IP address in the packet</td> <td>a52db0d87623d8a25d0db324d74f0900deb5ca4ec8ad9f346114db134e040ec5</td> </tr> <tr> <td>frame.time_epoch</td> <td>Epoch time of the frame</td> <td>1676165569.930869</td> </tr> <tr> <td>arp.hw.type</td> <td>Hardware type</td> <td>1</td> </tr> <tr> <td>arp.hw.size</td> <td>Hardware size</td> <td>6</td> </tr> <tr> <td>arp.proto.size</td> <td>Protocol size</td> <td>4</td> </tr> <tr> <td>arp.opcode</td> <td>Opcode</td> <td>2</td> </tr> <tr> <td>data.len</td> <td>Length</td> <td>2713</td> </tr> <tr> <td>eth.dst.lg</td> <td>Destination LG bit</td> <td>1</td> </tr> <tr> <td>eth.dst.ig</td> <td>Destination IG bit</td> <td>1</td> </tr> <tr> <td>eth.src.lg</td> <td>Source LG bit</td> <td>1</td> </tr> <tr> <td>eth.src.ig</td> <td>Source IG bit</td> <td>1</td> </tr> <tr> <td>frame.offset_shift</td> <td>Time shift for this packet</td> <td>0</td> </tr> <tr> <td>frame.len</td> <td>frame length on the wire</td> <td>1208</td> </tr> <tr> <td>frame.cap_len</td> <td>Frame length stored into the capture file</td> <td>215</td> </tr> <tr> <td>frame.marked</td> <td>Frame is marked</td> <td>0</td> </tr> <tr> <td>frame.ignored</td> <td>Frame is ignored</td> <td>0</td> </tr> <tr> <td>frame.encap_type</td> <td>Encapsulation type</td> <td>1</td> </tr> <tr> <td>gre</td> <td>Generic Routing Encapsulation</td> <td>'Generic Routing<br> Encapsulation (IP)’</td> </tr> <tr> <td>ip.version</td> <td>Version</td> <td>6</td> </tr> <tr> <td>ip.hdr_len</td> <td>Header length</td> <td>24</td> </tr> <tr> <td>ip.dsfield.dscp</td> <td>Differentiated Services<br> Codepoint</td> <td>56</td> </tr> <tr> <td>ip.dsfield.ecn</td> <td>Explicit Congestion<br> Notification</td> <td>2</td> </tr> <tr> <td>ip.len</td> <td>Total length</td> <td>614</td> </tr> <tr> <td>ip.flags.rb</td> <td>Reserved bit</td> <td>0</td> </tr> <tr> <td>ip.flags.df</td> <td>Don't fragment</td> <td>1</td> </tr> <tr> <td>ip.flags.mf</td> <td>More fragments</td> <td>0</td> </tr> <tr> <td>ip.frag_offset</td> <td>Fragment offset</td> <td>0</td> </tr> <tr> <td>ip.ttl</td> <td>Time to live</td> <td>31</td> </tr> <tr> <td>ip.proto</td> <td>Protocol</td> <td>47</td> </tr> <tr> <td>ip.checksum.status</td> <td>Header checksum status</td> <td>2</td> </tr> <tr> <td>tcp.srcport</td> <td>TCP source port</td> <td>53425</td> </tr> <tr> <td>tcp.flags</td> <td>Flags</td> <td>0x00000098</td> </tr> <tr> <td>tcp.flags.ns</td> <td>Nonce</td> <td>0</td> </tr> <tr> <td>tcp.flags.cwr</td> <td>Congestion Window Reduced<br> (CWR)</td> <td>1</td> </tr> <tr> <td>udp.srcport</td> <td>UDP source port</td> <td>64413</td> </tr> <tr> <td>udp.dstport</td> <td>UDP destination port</td> <td>54087</td> </tr> <tr> <td>udp.stream</td> <td>Stream index</td> <td>1345</td> </tr> <tr> <td>udp.length</td> <td>Length</td> <td>225</td> </tr> <tr> <td>udp.checksum.status</td> <td>Checksum status</td> <td>3</td> </tr> <tr> <td>packet_type</td> <td>Type of the packet which is either "benign" or "malicious"</td> <td>0</td> </tr> </tbody> </table> <p>Furthermore, in compliance with the GDPR and to ensure the privacy of individuals, all IP addresses present in the dataset have been anonymized through hashing. This anonymization process helps protect the identity of individuals while preserving the integrity and utility of the dataset for research and model development purposes.</p> <p> </p> <p>Please note that while the dataset provides valuable insights and a solid foundation for machine learning tasks, it is not a substitute for extensive real-world data collection. However, it serves as a valuable resource for researchers, practitioners, and enthusiasts in the machine learning community, offering a compliant and anonymized dataset for developing and validating custom models in a specific problem domain.</p> <p> </p> <p>By leveraging the validation dataset for machine learning model evaluation and custom model training, users can accelerate their research and development efforts, building upon the knowledge gained from my thesis while contributing to the advancement of the field.</p>
Demographic, economic, geospatial data for municipalities of the Central Federal District in Russia (excluding the city of Moscow and the Moscow oblast) in 2010-2016
<p>The database contains demographic, economic, geospatial data for 452 municipalities of the 16 administrative units of the Central Federal District in Russia (excluding the city of Moscow and the Moscow oblast) for 2010-2016.</p> <p>The sources of data are the municipal-level statistics of Rosstat, Google Maps data and calculated indicators. The statistical data were arranged by the year, the data on municipalities for which there were administrative and territorial transformations for the period under study were excluded (in some cases, the data were provided in accordance with the administrative-territorial demarcation as of 2016).</p> <p>Municipalities' websites were used to fill the lack of population information in individual municipalities for some years.</p> <p>Calculated variables were made to estimate a number of indicators per capita, to introduce additional demographic indicators (e.g. migration inflow rate), to bring price economic indicators to base year prices (2010). For example, indicators of income of the local budget, volumes of investments in fixed assets (excluding budgetary funds), level of wages are modified to a comparable form (to 2010 prices).</p> <p>The distances on roads in different units of measurement from the geographical center of municipalities to the center of the capital of the region are calculated using the Google Maps database.</p> <p>Data mapping was performed using ArcGIS software.</p> <p>The data set consists of</p> <p>1) Municipalities_CFD_Russia_2010_2016_ENG.xlsx - The database of demographic, economic, geospatial data for 452 municipalities of the 16 administrative units of the Central Federal District in Russia (excluding the city of Moscow and the Moscow oblast) for 2010-2016,</p> <p>2) MUNICIPALITIES_CFD_RUSSIA_SHAPE.rar - The shape-files for maps construction,</p> <p>3) Fig.1. Municipalities ENG.jpg - The map of studied administrative units and municipalities of the Central Federal District in Russia .</p>
Why, what and how do European healthcare managers use performance data? Results of a survey and workshop among members of the European Hospital and Healthcare Federation (Data set; anonymised)
<p>The dataset presents results of a descriptive cross-sectional study based on a survey, delivered through an online self-reported questionnaire. The questionnaire was distributed to managers of hospitals and other health care organisations in a purposive sample of participants to the Exchange Programmes of the European Hospital and Health Care Federation (HOPE) eliciting information on the actual use of performance data in hospitals and other healthcare organisations in Europe in 2019.<br> Data collected through the online questionnaire was analysed using univariate descriptive statistics. Analyses were conducted using the R statistical program version 3.6.1. Respondents were, for certain parts of the analysis, sub-grouped by their reported managerial position and experience, as well as the type of organisation they work for. Analysis was done on a full sample of respondents, including the primary, 2019 HOPE Exchange Programme participants, and the secondary study population, 2015-2018 Exchange Programme alumni and local hosts.</p>
Information Leaks in Federated Learning
<p>With the surge in data collection and analytics, concerns are raised with regards to the privacy of the individuals represented by the data. In settings where the data is distributed over several data holders, federated learning offers an alternative to learn from the data without the need to centralize it in the first place. This is achieved by exchanging only model parameters learned locally at each data holder. This greatly limits the amount of data to be transferred, reduces the impact of data breaches, and helps to preserve the individual’s privacy. Federated learning thus becomes a viable alternative in IoT and Edge Computing settings, especially if the data collected is sensitive.</p> <p>However, risks for data or information leaks still persist, if information can be inferred from the models exchanged. This can e.g. be in the form of membership inference attacks. In this paper, we investigate how successful such attacks are in the setting of sequential federated learning. The cyclic nature of model learning and exchange might enable attackers with more information to observe the dynamics of the learning process, and thus perform a more powerful attack.</p> <p> </p>
Federal Advisory Council Membership
<p>Membership of the Federal Advisory Council dataset. SEC filings for banks who have been members of the FAC, </p>
Fig. 1 in Malacological mapping in Austria distribution of the Austrian spring snail Bythinella austriaca (v. F , 1857) in the federal state of Salzburg
Fig. 1: Geographical map of the federal state of Salzburg including all sample points with registered occurrence of the Austrian spring snail B. austriaca. Open circles mark sample locations published in literature, whereas filled circles represent sample points of own field investigations.
Brazilian Federal legislations and Health Professional Councils regulations about telemedicine, according to historical phases and public policy purposes from 1990 to 2018.
<p>The file contains one excel file with two datasheets, one with legislations from Brazilian Federal Government and other from Health Professional Councils, from 1990 to 2019. Each spreadsheet has the original database, the number ID of the normative, what institution the document is from, its publication date, the abstract (in Portuguese), its public URL, historical phases and the purpose of it.</p>
Accounting for New Types of Resource Consumption in a Federated Cloud
<p>As infrastructures and cloud services evolve, resource consumption is more flexible and users are often allowed to reserve resources without actual consumption. Relevant standardization bodies have developed new types of accounting record specifications, and new or updated tools are required to keep track of resource usage. The reaction to this is the development of a new accounting tool – GOAT.</p> <p>GOAT – GO Accounting Tool – is a service running in the background and waiting for a connection from a compatible client. The client connects to a cloud management framework, extracts computing data about projects, servers, networks, and storages, filters them accordingly, and sends them to a server for further processing. Multiple clients can use the server at once. When the server receives accounting data, it is transformed into the configured format and writes them to the destination file. The consumer collects data into a central accounting database where it is processed to generate statistical summaries.<br> For now, the GOAT project supports two cloud computing platforms on the client side – OpenNebula and Openstack. Thanks to its use of standard accounting record formats it can work with different consumers. The ones used in the real world are APEL and Prometheus.</p> <p>This Demonstration shows how the GOAT client extracts accounting data from a cloud management platform, sends them to the GOAT server where they are transformed, and how Prometheus and Grafana process them and present various views of resource usage.</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.