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470 results for “Evaluation dataset”
The datasets used to evaluate the ZHMolGraph
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TAU Urban Acoustic Scenes 2024 Mobile, Evaluation dataset
<p>TAU Urban Acoustic Scenes 2024 Mobile evaluation dataset consists of 1-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Traveling by a tram - <em>tram</em></li> <li>Traveling by a bus - <em>bus</em></li> <li>Traveling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul>
Dataset from the second wave of a pre-post study to test an interactive blended learning tool for delirium management in Belgian Nursing Homes by measuring delirium knowledge and evaluation of the tool
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The track 3.2 round 2 evaluation dataset of ADD 2022
<p>Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 (<a href="http://addchallenge.cn/add2022">http://addchallenge.cn/add2022</a>) includes three tracks: low-quality fake audio detection (LF), partially fake audio detection (PF) and audio fake game (FG). The LF track focuses on dealing with bona fide and fully fake utterances with various real-world noises etc. The PF track aims to distinguish the partially fake audio from the real. The FG track is a rivalry game, which includes two tasks: an audio generation task and an audio fake detection task. In this paper, we describe the datasets, evaluation metrics, and protocols. We also report major findings that reflect the recent advances in audio deepfake detection tasks.</p> <p>The ADD 2022 dataset is publicly available.</p> <p>This data set is licensed with a CC BY-NC-ND 4.0 license.</p> <p>If you use this dataset, please cite the following paper:</p> <blockquote> <p>Jiangyan Yi, Ruibo Fu, Jianhua Tao, Shuai Nie, Haoxin Ma, Chenglong Wang, Tao Wang, Zhengkun Tian, Ye Bai, Cunhang Fan, Shan Liang, Shiming Wang, Shuai Zhang, Xinrui Yan, Le Xu, Zhengqi Wen, Haizhou Li:<br>ADD 2022: the first Audio Deep Synthesis Detection Challenge. ICASSP 2022: 9216-9220</p> </blockquote>
The track 2 evaluation dataset of ADD 2022
<p>Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 (<a href="http://addchallenge.cn/add2022">http://addchallenge.cn/add2022</a>) includes three tracks: low-quality fake audio detection (LF), partially fake audio detection (PF) and audio fake game (FG). The LF track focuses on dealing with bona fide and fully fake utterances with various real-world noises etc. The PF track aims to distinguish the partially fake audio from the real. The FG track is a rivalry game, which includes two tasks: an audio generation task and an audio fake detection task. In this paper, we describe the datasets, evaluation metrics, and protocols. We also report major findings that reflect the recent advances in audio deepfake detection tasks.</p> <p>The ADD 2022 dataset is publicly available.</p> <p>This data set is licensed with a CC BY-NC-ND 4.0 license.</p> <p>If you use this dataset, please cite the following paper:</p> <blockquote> <p>Jiangyan Yi, Ruibo Fu, Jianhua Tao, Shuai Nie, Haoxin Ma, Chenglong Wang, Tao Wang, Zhengkun Tian, Ye Bai, Cunhang Fan, Shan Liang, Shiming Wang, Shuai Zhang, Xinrui Yan, Le Xu, Zhengqi Wen, Haizhou Li:<br>ADD 2022: the first Audio Deep Synthesis Detection Challenge. ICASSP 2022: 9216-9220</p> </blockquote>
The track 3.2 round 1 evaluation dataset of ADD 2022
<p>Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 (<a href="http://addchallenge.cn/add2022">http://addchallenge.cn/add2022</a>) includes three tracks: low-quality fake audio detection (LF), partially fake audio detection (PF) and audio fake game (FG). The LF track focuses on dealing with bona fide and fully fake utterances with various real-world noises etc. The PF track aims to distinguish the partially fake audio from the real. The FG track is a rivalry game, which includes two tasks: an audio generation task and an audio fake detection task. In this paper, we describe the datasets, evaluation metrics, and protocols. We also report major findings that reflect the recent advances in audio deepfake detection tasks.</p> <p>The ADD 2022 dataset is publicly available.</p> <p>This data set is licensed with a CC BY-NC-ND 4.0 license.</p> <p>If you use this dataset, please cite the following paper:</p> <blockquote> <p>Jiangyan Yi, Ruibo Fu, Jianhua Tao, Shuai Nie, Haoxin Ma, Chenglong Wang, Tao Wang, Zhengkun Tian, Ye Bai, Cunhang Fan, Shan Liang, Shiming Wang, Shuai Zhang, Xinrui Yan, Le Xu, Zhengqi Wen, Haizhou Li:<br>ADD 2022: the first Audio Deep Synthesis Detection Challenge. ICASSP 2022: 9216-9220</p> </blockquote>
ADD 2023 Challenge Track 1.2 Evaluation Dataset (Round 2)
<div> <div>Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.</div> <br> <div>The ADD 2023 dataset is publicly available.</div> <br> <div>This data set is licensed with a CC BY-NC-ND 4.0 license.</div> <br> <div>If you use this dataset, please cite the following paper:</div> <blockquote> <div>Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130</div> </blockquote> </div>
ADD 2023 Challenge Track 1.2 Evaluation Dataset (Round 1)
<div> <div>Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.</div> <br> <div>The ADD 2023 dataset is publicly available.</div> <br> <div>This data set is licensed with a CC BY-NC-ND 4.0 license.</div> <br> <div>If you use this dataset, please cite the following paper:</div> <blockquote> <div>Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130</div> </blockquote> </div>
ADD 2023 Challenge Track 3 Evaluation Dataset
<div> <div>Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.</div> <br> <div>The ADD 2023 dataset is publicly available.</div> <br> <div>This data set is licensed with a CC BY-NC-ND 4.0 license.</div> <br> <div>If you use this dataset, please cite the following paper:</div> <blockquote> <div>Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130</div> </blockquote> </div>
ADD 2023 Challenge Track 1.1 Evaluation Dataset
<div> <div>Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.</div> <br> <div>The ADD 2023 dataset is publicly available.</div> <br> <div>This data set is licensed with a CC BY-NC-ND 4.0 license.</div> <br> <div>If you use this dataset, please cite the following paper:</div> <blockquote> <div>Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130</div> </blockquote> </div>
ADD 2023 Challenge Track 2 Evaluation Dataset
<div> <div>Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.</div> <br> <div>The ADD 2023 dataset is publicly available.</div> <br> <div>This data set is licensed with a CC BY-NC-ND 4.0 license.</div> <br> <div>If you use this dataset, please cite the following paper:</div> <blockquote> <div>Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130</div> </blockquote> </div>
Rationalizing Systems Analysis for the Evaluation of Adaptation Strategies in Complex Human-Water Systems - Outputs Dataset
<p>Dataset containing the outputs of the paper entitled Rationalizing Systems Analysis for the Evaluation of Adaptation Strategies in Complex Human-Water Systems. Outputs are subdivided into PMAUP and SWAT-PMAUP folders. Scenario runs are available in the folder scenario, inside SWAT-PMAUP, together with the SWAT model setup used throughout the manuscript.</p>
TUT Urban Acoustic Scenes 2018 Mobile, Evaluation dataset
<p>TUT Urban Acoustic Scenes 2018 Mobile evaluation dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Travelling by a tram - <em>tram</em></li> <li>Travelling by a bus - <em>bus</em></li> <li>Travelling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul> <p>Recordings were made with three devices that captured audio simultaneously. The dataset contains in total 42 hours of audio.</p>
TUT Urban Acoustic Scenes 2018, Evaluation dataset
<p>TUT Urban Acoustic Scenes 2018 evaluation dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Travelling by a tram - <em>tram</em></li> <li>Travelling by a bus - <em>bus</em></li> <li>Travelling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul> <p>The dataset contains in total 10 hours of audio.</p>
TAU Urban Acoustic Scenes 2019, Evaluation dataset
<p>TAU Urban Acoustic Scenes 2019 evaluation dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Travelling by a tram - <em>tram</em></li> <li>Travelling by a bus - <em>bus</em></li> <li>Travelling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul> <p>The dataset contains in total 20 hours of audio.</p>
TAU Urban Acoustic Scenes 2019 Mobile, Evaluation dataset
<p>TAU Urban Acoustic Scenes 2019 Mobile, Evaluation dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Travelling by a tram - <em>tram</em></li> <li>Travelling by a bus - <em>bus</em></li> <li>Travelling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul> <p>The dataset contains in total 30 hours of audio.</p>
TAU Urban Acoustic Scenes 2019 Openset, Evaluation dataset
<p>TAU Urban Acoustic Scenes 2019 Openset evaluation dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Travelling by a tram - <em>tram</em></li> <li>Travelling by a bus - <em>bus</em></li> <li>Travelling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul> <p>In addition, material for Unknown scene <em>unknown</em> is provided.</p> <p>The dataset contains in total 20 hours of audio.</p>
Dataset for Evaluating Sustain-Pedal Detection from Polyphonic Piano Music
<p>To evaluate methods of sustain-pedal detection from polyphonic piano music, we built a dataset consisting of ten well-known passages of Chopin's music. Ground-truth annotations of this dataset represent sustain-pedal on/off states at every 0.1 second. This annotation was based on the sustain-pedal movement tracked by a dedicated measurement system.</p> <p>Music scores of the ten passages were saved in PDF files. They were performed by a pianist using a Yamaha baby grand piano situated in the studios at Queen Mary University of London. The audio were recorded at 44.1 kHz and 24 bits using the spaced-pair stereo microphone technique. A pair of Earthworks QTC40 omnidirectional condenser microphones was positioned about 50 cm above the strings. </p> <p>We have developed a transfer learning method such that the on/off state of the sustain pedal can be detected at every 0.1 second. The ground-truth annotation and our detection results were saved in <em>transfer-learning-y_segment.npz</em>, which can be loaded using <em>numpy.load </em>in Python. The key for passage name, ground-truth annotation and our detection results is <em>filename_record</em>, <em>y_true</em> and <em>y_pred</em>, respectively.</p>
Trace-Share Dataset for Evaluation of Trace Meaning Preservation
<p>The dataset contains all data used during the evaluation of trace meaning preservation. Archives are protected by password "<strong>trace-share</strong>" to avoid false detection by antivirus software.</p> <p>For more information, see the project repository at <strong><a href="https://github.com/Trace-Share">https://github.com/Trace-Share</a></strong>.</p> <p> </p> <p><strong>Selected Attack Traces</strong></p> <p>The following list contains trace datasets used for evaluation. Each attack was chosen to have not only a different meaning but also different statistical properties.</p> <ul> <li><strong>dos_http_flood</strong> — the capture of GET and POST requests sent to one server by one attacker (HTTP~traffic);</li> <li><strong>ftp_bruteforce</strong> — short and unsuccessful attempt to guess a user’s password for FTP service (FTP traffic);</li> <li><strong>ponyloader_botnet</strong> — Pony Loader botnet used for stealing of credentials from 3 target devices reporting to single IP with a large number of intermediate addresses (DNS and HTTP traffic);</li> <li><strong>scan</strong> — the capture of nmap tool that scans given subnet using ICMP echo and TCP SYN requests (consist of ARP, ICMP, and TCP traffic);</li> <li><strong>wannacry_ransomware</strong> — the capture of Wanacry ransomware that spreads in a domain with three workstations, a domain controller, and a file-sharing server (SMB and SMBv2 traffic).</li> </ul> <p> </p> <p><strong>Background Traffic Data</strong></p> <p>Publicly available dataset <a href="https://www.unb.ca/cic/datasets/ids-2018.html">CSE-CIC-IDS-2018</a> was used as a background traffic data. The evaluation uses data from the day Thursday-01-03-2018 containing a sufficient proportion of regular traffic without any statistically significant attacks. Only traffic aimed at victim machines (range 172.31.69.0/24) is used to reduce less significant traffic.</p> <p> </p> <p><strong>Evaluation Results and Dataset Structure</strong></p> <ul> <li>Traces variants (<a href="https://zenodo.org/record/3547528/files/traces.zip"><em>traces.zip</em></a>) <ul> <li>./traces-original/ — trace PCAP files and crawled details in YAML format;</li> <li>./traces-normalized — normalized PCAP files and details in YAML format;</li> <li>./traces-adjusted — adjusted PCAP files using various timestamp generation settings, combination configuration in YAML format, and lables provided by ID2T in XML format.</li> </ul> </li> <li>Extracted alerts (<a href="https://zenodo.org/record/3547528/files/alerts.zip"><em>alerts.zip</em></a>) <ul> <li>./alerts-original/ — extracted Suricata alerts, Suricata log, and full Suricata output for all original trace files;</li> <li>./alerts-normalized/ — extracted Suricata alerts, Suricata log, and full Suricata output for all normalized trace files;</li> <li>./alerts-adjusted/ — extracted Suricata alerts, Suricata log, and full Suricata output for all adjusted trace files.</li> </ul> </li> <li>Evaluation results <ul> <li>*.csv files in the root directory — data contains extracted alert signatures and their count per each trace variant.</li> </ul> </li> </ul> <p> </p>
Fig. 4 Usnea parafloridana K in Evaluation of traditionally circumscribed species in the lichen-forming genus Usnea, section Usnea (Parmeliaceae, Ascomycota) using a six-locus dataset
Fig. 4 Usnea parafloridana K. Mark, Will-Wolf & Randlane sp. nov. – view of general habit (a, b), soralia with isidiomorphs (c), fibrils (d), soralia (e), and branch anatomy (f). Scale bars 7 mm (a, b), 1.5 mm (c), 2 mm (d), 0.4 mm (e), and 0.3 mm (f). Photographed specimens WW14807 (holotype; a, c, e), WW14858 (b, d), and WW14857 (f)
ScienceDex guides
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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.