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63 results for “Longitudinal dataset”
Datasets of the paper Monitoring cognitive and psychological alterations in COVID-19 patients: A longitudinal neuropsychological study by Diana et al. 2022
<p>Datasets of the paper "Monitoring cognitive and psychological alterations in COVID-19 patients: A longitudinal neuropsychological study" by Diana et al. 2022</p>
MalDevGenome: A Longitudinal and Large-Scale AndroidMalware Developer Dataset
<p>Dataset for the paper "MalDevGenome: A Longitudinal and Large-Scale AndroidMalware Developer Dataset".</p> <p>We have collected 1,411,830 apps from 10,708 different malware developer. These apps belongs to 1,035 different malware families. We provided the basic information, anti-virus detection result and sensitive behaviors of the apps, including app name, package name, apk file hashes, developer certificate hashes, permission, service, content provider, activity, broadcast receiver, library, hardcode string and the malware family name. And we will upload some representative malware developers’ application APK files later.</p> <p>As far, we uploaded 3 CSV file, including app basic informations, Virustotal's detection results and sensitive behaviors .</p> <p>(1)MalDevGenome_basic_info.csv</p> <p>App basic informations, including app name, package name, developer signature, apk file hashes, AV-Rank, malware family, etc.</p> <p>(2)MalDevGenome_virustotal_detection.csv</p> <p>Detailed VirusTotal's detection, including AV-Rank and details detection information of each anti-virus engine. These data are indexed according to Apk file MD5.</p> <p>(3)MalDevGenome_sensitive_behaviors.csv</p> <p>The sensitive behaviors of the samples, including permission, service, content provider, activity, broadcast receiver, library and hardcode string, etc. These data are indexed according to Apk file MD5.</p>
Long-CoDe: A Longitudinal Twitter Dataset of Depression in the COVID Era
<p># Long-CoDe: A Longitudinal Twitter Dataset of Depression in the COVID Era</p> <p>## Content of Long-CoDe</p> <p>Long-CoDe contains 15 million tweets from 597 depressed users and 804 control users. The temporal span of collected tweets is from Jan. 1st, 2019 to Dec. 31, 2022. We do not have the right to release text of the tweets due to privacy issues. We include user-ids, tweet-ids, date of posting, and label of the users in this dataset. We disguised user-ids with C00n and D00n to protect privacy and to represent their group labels. Tweets can be accessed with tweet-ids from the Twitter-APIs. The format of the data is like follows:</p> <p>| user-id | tweet-id | label |<br> | ------- | -------- | -------------- |<br> | C001| xxxxxxxx | 0 (Control) |<br> | D001| xxxxxxxx | 1 (Depression) |</p> <p>## Quality</p> <p>All users in both depressed and control groups posted steadily from 2019 to 2022. Control users were selected from 43 trending topics in 2020 evenly from Feb. to Dec. with broad interests.</p> <p>## Structure</p> <p>The Long-CoDe dataset is a single CSV file with tabular data. The format of the CSV file is as follows:</p> <p>| user_id | tweet_id | label<br> | ------- | -------- | -------------- |<br> | C001| xxxxxxxx | 0 (Control) |<br> | D001| xxxxxxxx | 1 (Depression) |</p> <p>## Potential uses of the dataset</p> <p>General analysis of tweets from the labeled depressed users, compared with the control.<br> Benchmark binary user-classification with contents of tweets or other extracted features.<br> Analyze the impact of Covid-19 on both depression and control groups in multiple phases of the pandemic.</p> <p>## Data Collection</p> <p>The data collection procedure contains four steps: (a) identifying depressed users, (b) selecting control users, (c) collecting tweets in 2020 and identifying regular users, and (d) collecting tweets from 2019 to 2022 for all selected users.</p> <p>### Identifying Depressed Users</p> <p>To identify targeted depressed users, we crawled tweets containing depression self-claims, such as ”I am/was/have been diagnosed with depression”, using regular expressions from Feb 2020 to Dec 2020,when Covid-19 had a significant influence on our society. The data was manually annotated, and keep the users who did a valid self-claim on depression diagnosis.</p> <p>### Identifying Control User</p> <p>In an attempt to create a diverse control user population, we gathered users that have shown interests in a wide array of trending topics, so that this would result in users with different backgrounds. From that, we randomly selected 6,200 unique users, and filtered out the users who ever made depression claims to give us a total of 5,929 control users.</p> <p>### Identifying Regular Users</p> <p>For each user from both groups, we crawled the tweets (without re-tweets) from Feb. to Dec. in 2020 and analyzed their posting behaviors. We keep the users who have stable posting behaviors. (posting between 75 and 205 tweets per month)</p> <p>### Collecting tweets from 2019 to 2022</p> <p>We crawled all tweets for each of the identified regular users for four years from Jan., 2019 to Dec., 2022.<br> </p>
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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.