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159 results for “authentication”
Supporting Material to "Statistical Fault Attacks on Nonce-Based Authenticated Encryption Schemes"
<p>supporting_material_code/setX/ct_fault.txt: each line corresponds to one ciphertext received from the device under test while encrypting a plaintext. The bytes are separated by a comma. For every encryption a fault has been injected:</p> <p>set1: Laser fault injections targeting an AES co-processor on a smartcard microcontroller.</p> <p>set2: Clock tampering targeting an AES co-processor implemented on a general-purpose microcontroller.</p> <p>set3 & 4: Clock tampering targeting an AES software implementation (AVR crypto lib ASM) implemented on a general-purpose microcontroller (ATXmega256A3).</p> <p>supporting_material_code/main.cpp: program for key recovery which takes as input the faulty ciphertexts (ct_fault.txt)</p> <p> </p>
Three-tiered authentication of herbal Traditional Chinese Medicine ingredients used in women's health provides progressive qualitative and quantitative insight - part 2/4
<p>Traditional Chinese Medicine herbal products are increasingly used in Europe but prevalent authentication methods have significant gaps in detection. In this study, three authentication methods were tested in a tiered approach to improve accuracy on a collection of 51 TCM plant ingredients obtained on the European market. We show the relative performance of conventional barcoding, metabarcoding and standardized chromatographic profiling for TCM ingredients used in one of the most diagnosed disease patterns in women, endometriosis. DNA barcoding using marker ITS2 and chromatographic profiling are methods of choice reported by regulatory authorities and relevant national pharmacopeias. HPTLC was shown to be a valuable authentication tool, combined with metabarcoding, which gives an increased resolution on species diversity, despite dealing with highly processed herbal ingredients. Conventional DNA barcoding as a recommended method was shown to be an insufficient tool for authentication of these samples, while DNA metabarcoding yields an insight into biological contaminants. We conclude that a tiered identification strategy can provide progressive qualitative and quantitative insight in an integrative approach for quality control of processed herbal ingredients. </p>
Three-tiered authentication of herbal Traditional Chinese Medicine ingredients used in women's health provides progressive qualitative and quantitative insight - part 1/4
<p>Traditional Chinese Medicine herbal products are increasingly used in Europe but prevalent authentication methods have significant gaps in detection. In this study, three authentication methods were tested in a tiered approach to improve accuracy on a collection of 51 TCM plant ingredients obtained on the European market. We show the relative performance of conventional barcoding, metabarcoding and standardized chromatographic profiling for TCM ingredients used in one of the most diagnosed disease patterns in women, endometriosis. DNA barcoding using marker ITS2 and chromatographic profiling are methods of choice reported by regulatory authorities and relevant national pharmacopeias. HPTLC was shown to be a valuable authentication tool, combined with metabarcoding, which gives an increased resolution on species diversity, despite dealing with highly processed herbal ingredients. Conventional DNA barcoding as a recommended method was shown to be an insufficient tool for authentication of these samples, while DNA metabarcoding yields an insight into biological contaminants. We conclude that a tiered identification strategy can provide progressive qualitative and quantitative insight in an integrative approach for quality control of processed herbal ingredients. </p>
Three-tiered authentication of herbal Traditional Chinese Medicine ingredients used in women's health provides progressive qualitative and quantitative insight - part 3/4
<p>Traditional Chinese Medicine herbal products are increasingly used in Europe but prevalent authentication methods have significant gaps in detection. In this study, three authentication methods were tested in a tiered approach to improve accuracy on a collection of 51 TCM plant ingredients obtained on the European market. We show the relative performance of conventional barcoding, metabarcoding and standardized chromatographic profiling for TCM ingredients used in one of the most diagnosed disease patterns in women, endometriosis. DNA barcoding using marker ITS2 and chromatographic profiling are methods of choice reported by regulatory authorities and relevant national pharmacopeias. HPTLC was shown to be a valuable authentication tool, combined with metabarcoding, which gives an increased resolution on species diversity, despite dealing with highly processed herbal ingredients. Conventional DNA barcoding as a recommended method was shown to be an insufficient tool for authentication of these samples, while DNA metabarcoding yields an insight into biological contaminants. We conclude that a tiered identification strategy can provide progressive qualitative and quantitative insight in an integrative approach for quality control of processed herbal ingredients.</p>
Three-tiered authentication of herbal Traditional Chinese Medicine ingredients used in women's health provides progressive qualitative and quantitative insight - part 4/4
<p>Traditional Chinese Medicine herbal products are increasingly used in Europe but prevalent authentication methods have significant gaps in detection. In this study, three authentication methods were tested in a tiered approach to improve accuracy on a collection of 51 TCM plant ingredients obtained on the European market. We show the relative performance of conventional barcoding, metabarcoding and standardized chromatographic profiling for TCM ingredients used in one of the most diagnosed disease patterns in women, endometriosis. DNA barcoding using marker ITS2 and chromatographic profiling are methods of choice reported by regulatory authorities and relevant national pharmacopeias. HPTLC was shown to be a valuable authentication tool, combined with metabarcoding, which gives an increased resolution on species diversity, despite dealing with highly processed herbal ingredients. Conventional DNA barcoding as a recommended method was shown to be an insufficient tool for authentication of these samples, while DNA metabarcoding yields an insight into biological contaminants. We conclude that a tiered identification strategy can provide progressive qualitative and quantitative insight in an integrative approach for quality control of processed herbal ingredients.</p>
User Study Data for "HapticLock: Eyes-Free Authentication for Mobile Devices"
<p>User study data from the HapticLock paper published in the Proceedings of the ACM ICMI 2021 conference.</p>
Dataset of 'How Everyday Counterfeit Behavior that Disrupts Self Authenticity May Lead to Corruption Tendencies'
<p>Dataset and JASP Output of 'How Everyday Counterfeit Behavior that Disrupts Self Authenticity May Lead to Corruption Tendencies' (Abraham et al., 2021).</p> <p> </p>
Authentic Medieval Chair (Free)
Found a image online of a authentic Medieval chair and recreated the look of the image. Created in Blender. Textures where created in Normalizator Chair was painted in substance painter. Chair is also the chair in our upcoming game. Engineering ages https://discord.gg/9samDkS Source: Objaverse 1.0 / Sketchfab
Using machine learning to distinguish between authentic and imitation Jackson Pollock poured paintings: Art images
<p>Jackson Pollock's abstract poured paintings are celebrated for their striking aesthetic qualities. They are also among the most financially valued and imitated artworks, making them vulnerable to high-profile controversies involving Pollock-like paintings of unknown origin. Given the increased employment of artificial intelligence applications across society, we investigate whether established machine learning techniques can be adopted by the art world to help detect imitation Pollocks. The low number of images compared to typical artificial intelligence projects presents a potential limitation for art-related applications. To address this limitation, we develop a machine learning strategy involving a novel image ingestion method which decomposes the images into sets of multi-scaled tiles. Leveraging the power of transfer learning, this approach distinguishes between authentic and imitation poured artworks with an accuracy of 98.9%. The machine also uses the multi-scaled tiles to generate novel visual aids and interpretational parameters which together facilitate comparisons between the machine's results and traditional investigations of Pollock's artistic style.</p>
Lab Study Dataset: FIDO2 Platform and Roaming Authentication on Smartphones
<p>This record contains the <strong>lab study dataset and evaluation R source code</strong> from the paper "FIDO2 the Rescue? Platform vs. Roaming Authentication on Smartphones" by Leon Würsching*, Florentin Putz* <em>(* = equal contribution)</em>, Steffen Haesler, and Matthias Hollick <em> </em>in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23).</p> <p>Our pseudonymous <strong>dataset</strong> contains 22 variables for each of our 87 participants in our between-groups lab study. The variables consist of usability and acceptance scores, the adoption likelihood for 11 account types, and 9 control variables including the level of privacy concerns and ATI.</p> <p>Our R Markdown <strong>source code</strong> includes the full reproducible code of our study. This code generates all statistical figures from our paper. The code can also be used to reproduce our quantitative results and tables.</p> <p>Please refer to the README.md file and our paper for further details about the dataset and the lab study.</p> <p> </p> <p>This work has been co-funded by the LOEWE initiative (Hesse, Germany) within the emergenCITY center and the Federal Ministry of Education and Research of Germany in the project Open6GHub (grant number: 16KISK014).</p>
Handling Dynamic Environment Changes for Behavior-Based User Authentication
<p><strong>Description:</strong></p> <p>This environment-independent user authentication dataset is from our MASS 2020 paper<strong>: <em>Towards Environment-independent Behavior-based User Authentication Using WiFi</em></strong>. This dataset contains the physiological characteristics captured by WiFi from 10 participants for 10 different activities. Each participant performs 20 rounds for each activity. The experiments are conducted in two different environments, the campus office, and the home apartment. The system performance is tested on the cross-environment scenarios (training in one environment and testing in another environment).</p> <p>Note: The MASS 2020 paper is based on our MobiHoc 2017 paper, <strong><em>Smart User Authentication through Actuation of Daily Activities Leveraging WiFi-enabled IoT</em></strong>. The MobiHoc 2017 work focused on user authentication using CSI extracted from human activity while the MASS 2020 work focused on the domain adaptation of user authentication using activity CSI.</p> <p>The dataset of our MobiHoc 2017 work is also published: <a href="https://zenodo.org/record/7750976#.ZBfTZ3bMKUk">https://zenodo.org/record/7750976#.ZBfTZ3bMKUk</a></p> <p> </p> <p><strong>Format: </strong>.dat format</p> <p><strong>Section 1: Device Configuration</strong></p> <ul> <li>Two commercial laptops, Dell E6430, as transmitter and receiver. Run with a Linux 14.04 operating system with 4.2.0 kernel. Equipped with 3 MINI PCI-E internal antennas. </li> <li>Intel 5300 network interface card (NIC) for CSI collection. The detail information regarding the CSI tool can be found at <a href="https://dhalperi.github.io/linux-80211n-csitool/faq.html">https://dhalperi.github.io/linux-80211n-csitool/faq.html</a>.</li> <li>WiFi packet transmission is set to 1000 pkts/s</li> </ul> <p><strong>Section 2: Data Format</strong></p> <p>We provide raw data received by the CSI tool. The data files are saved in the dat format. The details are shown in the following:</p> <ol> <li>10 participants are included in two different experiments.</li> <li>Each participant performed 20 rounds for each activity.</li> <li>The dataset file name is presented as "User_Day_Action_Location". The detailed information as: <ul> <li>User: The participants that CSI was collected from.</li> <li>Day: The date this data was collected. </li> <li>Action: The specific activity performed.</li> <li>Location: The specific location the experiment was conducted.</li> </ul> </li> </ol> <p><strong>Section 3: Experimental Setups</strong></p> <p>There are two experiment setups for our data collection. An image of the experimental setup and the illustration of activities from two different environments is included in the dataset. Each activity was performed in a designated location. In each activity location, the specific activity was conducted in 4 different proximate locations at least one foot away from each other. </p> <ol> <li>Residential Apartment <ul> <li>Environment: The experiments are conducted in a residential apartment with a size 33ft × 17ft.</li> <li>Participant: 10 users are students from Rutgers University (aged from 20 to 30).</li> <li>Activity: 7 activities were performed. <table> <caption>Detailed Activities Performed in Apartment</caption> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Activity</strong></td> </tr> <tr> <td> A→B</td> <td>Walking (trajectory 1)</td> </tr> <tr> <td> B→C</td> <td>Walking (trajectory 2)</td> </tr> <tr> <td> B</td> <td>Picking up a remote control</td> </tr> <tr> <td> C</td> <td>Sitting in a chair </td> </tr> <tr> <td> D</td> <td>Exercising</td> </tr> <tr> <td> E</td> <td>Operating on the oven</td> </tr> <tr> <td> F</td> <td>Using the stove</td> </tr> </tbody> </table> <p> </p> </li> </ul> </li> <li>Office <ul> <li>Environment: The experiments are conducted in an office with a size 21ft × 12ft.</li> <li>Participant: 5 users are students from Rutgers University (aged from 20 to 30).</li> <li>Activity: 3 activities were performed. <table> <caption>Detailed Activities Performed in Office</caption> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Activity</strong></td> </tr> <tr> <td> G</td> <td>Sitting in a seat</td> </tr> <tr> <td> H</td> <td>Stretching the body</td> </tr> <tr> <td> I</td> <td>Typing on a keyboard</td> </tr> </tbody> </table> </li> </ul> </li> </ol> <p> </p> <p><strong>Section 4: Data Description</strong></p> <p>We separate our raw data into different folders based on different environment types. In each environment type, data are further distributed in terms of date. Each file includes all data from three internal antennas. All data files are in .dat format. We also provide Matlab scripts for CSI analysis and visualization. The following variables can be revealed from the codes:</p> <ol> <li>CSI: This is the Channel State Information (CSI) received from one receiver antenna. It describes the signal propagation from the transmitter to the receiver, and it is very sensitive to the impact of environmental changes. Each data reveals CSI from 30 subcarriers. </li> <li>Relative Phase: Relative Phase is a measurement to describe the degree of synchronization between data received from different antennas. It can be used to determine the phase offset for further signal preprocessing.</li> <li>Time: This is the time interval in which the data file contains. It measures time by the number of seconds. It can be used to determine how long the signal has been received.</li> </ol> <p><strong>Section 5: Codes</strong></p> <ul> <li>analysis_spectrogram.m: load a .dat file and extract all data by Data description(I.e, CSI, and Relative Phase).</li> </ul> <p><strong>Section 6: Citations</strong></p> <p>If your paper is related to our works, please cite our papers as follows.</p> <p><a href="https://ieeexplore.ieee.org/document/9356038">https://ieeexplore.ieee.org/document/9356038</a></p> <p>C. Shi, J. Liu, N. Borodinov, B. Leao and Y. Chen, "Towards Environment-independent Behavior-based User Authentication Using WiFi," <em>2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)</em>, Delhi, India, 2020, pp. 666-674, doi: 10.1109/MASS50613.2020.00086</p> <p><strong>Bibtex:</strong></p> <p>@INPROCEEDINGS{9356038,<br> author={Shi, Cong and Liu, Jian and Borodinov, Nick and Leao, Bruno and Chen, Yingying},<br> booktitle={2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)}, <br> title={Towards Environment-independent Behavior-based User Authentication Using WiFi}, <br> year={2020},<br> volume={},<br> number={},<br> pages={666-674},<br> doi={10.1109/MASS50613.2020.00086}}<br> </p> <p>The current version of the dataset is shrunk due to its size. If you wish to acquire the full version or you have any questions regarding the dataset, contact us by email: cl1361@scarletmail.rutgers.edu. </p>
Complementary authentication of Chinese herbal products to treat endometriosis using DNA metabarcoding and HPTLC shows a high level of variability
<p>Traditional Chinese Medicine (TCM) is popular for the treatment of endometriosis, a complex gynecological disease that affects 10% of women globally. The growing market for TCMs has yielded a significant incentive for product adulteration, and although emerging technologies show promise to improve their quality control, many challenges remain. We tested the authenticity of two traditional Chinese herbal formulae used in women’s healthcare for the treatment of endometriosis, known as <em>Gui Zhi Fu Ling Wan</em> (FL) and <em>Ge Xia Zhu Yu Tang</em> (GX). Dual-locus DNA metabarcoding analysis coupled with high-performance thin-layer chromatography (HPTLC) were used to authenticate 19 FL and six GX commercial herbal products, as well as three ad hoc prepared artificial mixtures. HPTLC was able to detect most of the expected ingredients via comparative component analysis. DNA metabarcoding was able to detect an unexpected species diversity in the products, including 38 unexpected taxa. Chromatography has resolution for all species indirectly through identification of marker compounds for the different species ingredients. Metabarcoding on the other hand yields an overview of species diversity in each sample, but interpretation of the results can be challenging. Detected species might not be present in quantities that matter, and without validated quantification some detected species can be hard to interpret. Comparative analysis of the two analytical approaches also reveals that DNA for species might be absent or too fragmented to amplify as the relevant chemical marker compounds can be detected but no amplicons assigned to the same species. Our study emphasizes that integrating DNA metabarcoding with phytochemical analysis brings valuable data for comprehensive authentication of Traditional Chinese Medicines ensuring their quality and safe use.</p>
Using machine learning to distinguish between authentic and imitation Jackson Pollock poured paintings: Art images
Open the record for dataset details and reuse information.
CCLid: A toolkit to authenticate the genotype and stability of cancer cell lines
<p>CCLid (Cancer Cell Line identification) is designed as a toolkit to address the lack of a publicly available resource for genotype-based cell line authentication. We developed this resource to allow for genotype-matching of any given cancer cell line to the 1,204 unique cell lines found in the CCLE dataset, with support to include additional SNP array datasets. Using the B-allele frequencies (BAFs) for all SNPs found in common between the input data and reference datasets, this tool will allow for a genotype matching operation that trains and uses a logistic model to calculate the probability of the best cell line matches. This is followed by a measure of genetic drift between isogenic lines by look for segments of the genome that have significantly different BAF values.</p> <p> </p> <p>This zenodo dataset contains the (sample x probeset) BAF matrix for the CCLE dataset, as well as supporting datasets to allow mapping of SNP probesets and genotype correction between SNP array technologies (i.e. Affymetrix SNP 6.0 and Illumina HumanOmni 2.5M). This also contains all the metadata for cell line identities in CCLE, GDSC, and gCSI as well their corresponding cellosaurus unique identifies.</p>
Publicly available medical text data with authentic quality
<p>This dataset is the public medical text record (progress notes) written in Japanese.</p> <p>Any researchers can use this dataset without privacy issues. </p> <p>CC BY-NC 4.0</p> <p>crowd.zip: 9,756 pseudo progress notes written by crowd workers</p> <p>crowd_evaluated.zip: 83 pseudo progress notes with authentic quality written by crowd workers</p> <p>MD.zip: 19 pseudo progress notes written by medical doctors</p> <p> </p> <p>Reference:</p> <p>Kagawa, R., Baba, Y., & Tsurushima, H. (2021, December). A practical and universal framework for generating publicly available medical notes of authentic quality via the power of crowds. In <em>2021 IEEE International Conference on Big Data (Big Data)</em> (pp. 3534-3543). IEEE.</p> <p><a href="http://hdl.handle.net/2241/0002002333">http://hdl.handle.net/2241/0002002333</a></p> <p>The supplemental files of the paper are here: <a href="https://github.com/rinabouk/HMData2021">https://github.com/rinabouk/HMData2021</a></p>
FIGURES 2–10 in Sphenocratus xinjiangensis Liang, sp. nov., the first authentic record of the dictyopharid subfamily Orgeriinae (Hemiptera: Fulgoroidea: Dictyopharidae) in China
FIGURES 2–10. Sphenocratus xinjiangensis Liang, sp. nov. (China: Xinjiang, IZCAS): 2. head (ventral view). 3. head and pronotum (lateral view). 4. left fore wing. 5. genitalia (lateral view). 6. pygofer and parameres (ventral view). 7. pygofer and anal tube (dorsal view). 8. aedeagus (ventral view). 10. aedeagus (lateral view). 11. aedeagus (dorsal view).
FIGURE 1 in Sphenocratus xinjiangensis Liang, sp. nov., the first authentic record of the dictyopharid subfamily Orgeriinae (Hemiptera: Fulgoroidea: Dictyopharidae) in China
FIGURE 1. Sphenocratus xinjiangensis Liang, sp. nov. (China: Xinjiang, IZCAS): male, dorsal habitus.
Authentic human translations corpora EN-MNE, EN-THA
<p>Data for the research has been gathered from two authentic human translations corpora <a href="https://www.clarin.si/repository/xmlui/handle/11356/1176?show=full">EN-MNE</a>, and <a href="https://opus.nlpl.eu/">EN-THA</a>. Pairs of sentences were selected according to the length (100-150 characters) and processed according to the methodology explained within the research.</p>
Vincent van Gogh Authentication Dataset
<p><strong>Vincent van Gogh Authentication Dataset</strong></p><p><strong>Overview</strong></p><p>This dataset is a collection of artworks for research in art history, digital humanities, and computational forgery detection. The dataset is based on the **VGDB-2016** collection available <a href="https://figshare.com/articles/dataset/From_Impressionism_to_Expressionism_Automatically_Identifying_Van_Gogh_s_Paintings/3370627">here </a>and includes a diverse range of images related to Vincent van Gogh's oeuvre.</p><p><strong>The VGDB-2016 Dataset</strong></p><p>- Source: The artworks were primarily sourced from Wikimedia Commons.</p><p>- Composition: The dataset comprises 126 original artworks, and 212 artworks with similar chronology or artistic movement to van Gogh.</p><p>- Image Quality: Each artwork maintains a high-resolution standard with a density of at least 196.3 Pixels Per Image (PPI).</p><p>- Special Inclusions: Two artworks with debated attribution are included for testing purposes.</p><p><strong>The Contrast Set</strong></p><p>To aid in the study of art forgery and style analysis, we have included a carefully curated contrast set.</p><p>- <strong>Purpose:</strong> The contrast set features artworks that are not created by van Gogh but closely resemble his style. This is crucial for developing and testing algorithms for forgery detection.</p><p>- <strong>Components:</strong></p><p> - Similar: These are works by artists who shared a similar style or were part of the same artistic movement as van Gogh (already part of VGDB-2016).</p><p> - Forgeries: This category includes non-autograph copies, artworks explicitly made in the style of van Gogh, and known forgeries.</p><p> - Synthetic Fakes: Generated using advanced AI models, including Stable Diffusion 2.1 and StyleGAN3.</p><p>- <strong>Details of the Contrast Set:</strong></p><p> - Artworks by Similar Artists: 212 proxies.</p><p> - Forgeries: 17 imitations, of which:</p><p> - 9 by Otto Wacker (in folder Vincent Forgeries Wacker),</p><p> - 8 by John Myatt (NOT CREATIVE COMMONS).</p><p>- <strong>AI-Generated Artworks:</strong></p><p> - Stable Diffusion 30 images (in folder Vincent Stable Diffusion),</p><p> - GANs fine-tuned on van Gogh's style 30 images (in folder Vincent GAN finetune),</p><p> - Random GANs (Raw GANs) 30 images (in folder Vincent GAN random).</p><p>The metadata file ('van_gogh_forgeries.csv') is meant to extend the already existing metadata [<a href="https://figshare.com/articles/dataset/From_Impressionism_to_Expressionism_Automatically_Identifying_Van_Gogh_s_Paintings/3370627">vgdb_2016.csv</a>] with the information of the forgeries included.</p><p><strong>Usage Guidelines</strong></p><p>This dataset is intended for academic and research purposes. Users are encouraged to apply this dataset in studies related to art history, digital humanities, and the development of computational tools for art analysis and forgery detection.</p><p><strong>John Myatt Genuine Forgeries</strong></p><p>We use 8 artworks by John Myatt in our dataset. These images are property of the Genuine Fakes Ltd. company and were downloaded manually from <a href="https://www.genuine-fakes.com/about/">https://www.genuine-fakes.com/about/</a>. For reproducibility we indicate the names of the artworks used:</p><p>- John Myatt's version of Van Gogh's</p><p> - Self Portrait</p><p> - Starry Night with Snow and Distant Woodland</p><p> - Starry Night with Wheat Field and Cypress Trees</p><p> - Starry Night</p><p> - Country Road in Provence by Night</p><p> - A Pair of Old Shoes</p><p> - The Harvest</p><p> - Oleanders</p><p><strong>Acknowledgments</strong></p><p>We extend our gratitude to the contributors of VGDB-2016 and Wikimedia Commons for providing the foundational resources for this dataset.</p><p><strong>Citations</strong></p><p>Folego G, Gomes O, Rocha A. From Impressionism to Expressionism: Automatically Identifying Van Gogh's Paintings. In: 2016 IEEE International Conference on Image Processing (ICIP); 2016. p. 141–145</p>
Fig. 6 in Species authentication of Tor spp. (family Cyprinidae) in Indonesia based on osteocranium structure and biometric data
Fig. 6. The morphology of the neurocranii bone seen from the posterior view. A: T. tambroides; B: T. tambra; C: T. douronensis; and D: T. soro. BO: basioccipital bone; EPO: epotic bone; EXO: exoccipital bone; FM: magnum foramen bone; FOL: lateral occipital foramen bone; PPTR: pterotic processus bone; PTR: pterotic bone; SOC: supraoccipital bone. Scale bar: 0.5 cm.
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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.