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Nonperturbative phase diagram of two-dimensional N=(2,2) super-Yang--Mills theory --- data release
<p>This HDF5 file collects data and analysis results for non-perturbative lattice field theory calculations investigating two-dimensional supersymmetric SU(N) Yang--Mills theory with four supercharges. See the README for further information.</p>
BBS phase 1 & phase 2 high quality E. coli bin assembled genomes
<p>1,402 <em>Escherichia coli</em> bin assembled genomes derived from the metagenome data collected as part of the <a href="https://www.ucl.ac.uk/global-health/research/a-z/baby-biome-study">BabyBiome study (BBS)</a> phase 1 & phase 2.</p> <p>The data in this upload was first published as part of "<em>Group 2 and 3 ABC-transporter dependant K-antigen loci contribute significantly to variation in the invasive potential of Escherichia coli"</em> (Gladstone et al. 2024, to be released).</p> <h2>Files</h2> <p>Assembly data:</p> <ul> <li>BBS_E_coli_BAGs.tar: Archive containing sequences of the 1,402 bin assembled genomes.</li> <li>BBS_E_coli_metadata.tsv: Table linking the sequence assemblies to the subject data.</li> </ul> <p>Capsule predictions:</p> <ul> <li>BBS_E_coli_Kaptive_output.csv: Capsule predictions for all sequence data.</li> <li>BBS_E_coli_deduplicated_sequences_IDs.txt: Filenames for assemblies that constitute the 873 deduplicated sequences analysed in Gladstone et al. 2024.</li> </ul> <p>Quality control data:</p> <ul> <li>BBS_E_coli_demix_check_scores.tsv: Output from demix_check for the sequence assemblies.</li> <li>BBS_E_coli_checkm_results.tsv: Output from checkm.</li> <li>BBS_E_coli_gunc_results.tsv: Output from gunc.</li> </ul> <h2>Methods</h2> <h3>Bin assembled genomes</h3> <p>Source data:</p> <ul> <li>BBS phase 1: <a href="https://doi.org/10.1038/s41586-019-1560-1">Shao et al. 2019</a></li> <li>BBS phase 2: <a href="https://doi.org/10.1038/s41564-024-01804-9">Shao et al. 2024</a></li> </ul> <p>The data was produced using the mSWEEP and mGEMS pipeline (<a href="https://doi.org/10.12688/wellcomeopenres.15639.2">Mäklin et al. 2020</a> & <a href="https://doi.org/10.1099/mgen.0.000691">Mäklin et al. 2021</a>) following the steps described in <a href="https://doi.org/10.1038/s41467-024-49591-5">Khawaja, Mäklin, Kallonen, et al. 2024</a>.</p> <h3>Quality control</h3> <p>The BAGs in this upload were filtered with demix_check (<a href="https://github.com/harry-thorpe/demix_check">https://github.com/harry-thorpe/demix_check</a>) and only those with a quality score 1 or 2 are included. For the capsule type annotations, contigs shorter than 5,000bp were removed but the short contigs are still present in the uploaded files). Further QC data is available from checkm (<a href="https://genome.cshlp.org/content/25/7/1043.short">Parks et al. 2015</a>) and gunc (<a href="https://link.springer.com/article/10.1186/s13059-021-02393-0">Orakov et al. 2022</a>) results.</p> <h3>Multilocus sequence typing</h3> <p>Sequence type (ST) was determined using fastmlst (<a href="https://journals.sagepub.com/doi/10.1177/11779322211059238">Guerrero-Araya et al. 2021</a>) with the `ecoli#1` database.</p> <h3>PopPUNK clustering</h3> <p>Sequence clusters (SC) correspond to the database available from <a href="https://zenodo.org/records/12528310">https://zenodo.org/records/12528310</a> and were created using PopPUNK (<a href="https://genome.cshlp.org/content/29/2/304.short">Lees et al. 2019</a>). Construction is described in <a href="https://doi.org/10.1038/s41467-024-49591-5">Khawaja, Mäklin, Kallonen, et al. 2024</a>.</p> <h3>Capsule type annotations</h3> <p>The capsule type annotations were created using Kaptive (<a href="https://doi.org/10.1099/mgen.0.000800">Lam et al. 2022</a>) with an <em>E. coli</em> specific database available from <a href="https://github.com/rgladstone/EC-K-typing">https://github.com/rgladstone/EC-K-typing</a> and described in Gladstone et al. 2024.</p>
PhasAGE Training School 2 - Phase separations and transitions by viral proteins: from viral factories to interference with host cell functions- LECTURE
<p>The Training School 2 “Biomolecular condensates in cell function, aging and disease” is the <strong>second</strong> edition of a series of PhasAGE training activities.</p> <p> </p> <p>The main goal of this training school is to raise awareness and provide expertise on fundamental aspects of phase separation and formation of <strong>biomolecular condensates</strong>, specifically covering the importance of this process to cellular biology and its contribution to the aging process and age-related diseases.</p>
Exoplanet imaging data challenge, phase 2
<p><strong>Datasets for the second phase of the Exoplanet Imaging Data Challenge</strong> (<a href="https://exoplanet-imaging-challenge.github.io/">https://exoplanet-imaging-challenge.github.io</a>). </p> <p>The second phase of the Exoplanet Imaging Data Challenge is focused on the characterisation of exoplanet signals in high-contrast imaging data. The participants must perform two tasks: provide (1) astrometry of the detected signals, and (2) spectrophotometry of the detected signals.</p> <p>For this phase, we therefore provide with <strong>8</strong> high-contrast data sets, taken with two integral field spectrographs: SPHERE-IFS installed at the Very Large Telescope (VLT, Chile) and GPI installed at the Gemini-South telescope (Chile). Each data set consists of the following files (in <em>.fits</em> format):</p> <p>(a) <em>image_cube_instxx.fits</em>: the coronagraphic multispectral image cube, acquired in pupil-stabilized mode;<br> (b) <em>parallactic_angles_instxx.fits</em>: the corresponding parallactic angle and airmass variation during the observation sequence;<br> (c) <em>wavelength_vect_instxx.fits</em>: the corresponding wavelength vector for each spectral channel;<br> (d) <em>psf_cube_instxx.fits</em>: the non-coronagraphic (point spread function) multispectral image of the target star;<br> (e) <em>first_guess_astrometry_instxx.fits</em>: a first guess position w.r.t the star of the (2 or 3) injected planetary signals.</p> <p>Each data set has 2 to 3 injected planetary signals in various locations.<br> Each data set has very different observing conditions, from very good to very bad.</p> <p><em>Additional information and ressources can be found on the <a href="https://exoplanet-imaging-challenge.github.io/">website</a> and dedicated <a href="https://github.com/exoplanet-imaging-challenge/phase2">Github repository.</a></em></p> <p><em>The results of the data challenge must be submitted directly by participants on the <a href="https://eval.ai/web/challenges/challenge-page/1717/overview">EvalAI platform</a>.</em></p>
INDICATORS TO EVALUATE THE LABOUR INSERTION OF PEOPLE WITH DISABILITIES IN CONVENTIONAL COMPANIES IN SPAIN: QUANTITIATIVE DATA SET OF DELPHI STUDY (PHASE 2 AND PHASE 3)
<p><span>The level of labor integration of people with disability (PwD) is notably lower than that of people without disabilities. In order to evaluate the success of the labor market integration of people with disabilities, it is necessary to establish a series of indicators that go beyond hiring rates. Hence, the objective of this study is to develop a list of indicators with their specified individual weight that will serve to evaluate the success of the labor market insertion of PwD in conventional companies. </span></p> <p><span>Methodology: </span></p> <p><span>The Delphi method was used. </span></p> <p><span>PHASE 1</span></p> <p><span>In Phase 1, an open-ended questionnaire was distributed to 48 human resources and disability experts. <span><br></span></span></p> <p><span>PHASE 2</span></p> <p><span>Based on the theoretical dimensions obtained, a list of 52 indicators was drawn up and the experts were asked to evaluate the importance of each item using a scale of 0 to 10 points. In addition, in this second questionnaire, they were encouraged to propose improvements in the final wording of the items, as well as in the relevance and denomination of the dimensions into which they had been grouped. No suggestions were received to modify the wording or to incorporate additional items.</span></p> <p><span>PHASE 3</span></p> <p><span>Once the scores of all the participants had been collected, a third questionnaire was sent out with the aim of achieving a statistical consensus within the group of experts. In this questionnaire, each panel member was informed of their degree of agreement or disagreement in relation to the group as a whole, without revealing the identity of the other participants. In other words, each participant was provided with information on the average rating of the group and their own initial rating (from Phase 2) of each of the 52 indicators, offering them the option to modify their response if they considered it appropriate. If they chose to change their assessment, they were asked to justify their reasons.</span></p> <p><span><span>To assess the possible convergence of opinion, the change in the responses received in the third phase with respect to the second phase was analyzed. We examined whether there had been variations in the scores given by the experts in the second phase once the group's mean ratings had been received. For this purpose, the “proportion of experts” statistic was used to verify that the average value of the responses in this third phase was within a range from [-0.5 to +0.5], compared to the average value of the scores in the second phase. In the case of non-convergence of opinion, this methodology allows for as many rounds as necessary until convergence is achieved. </span></span></p> <p><span><span>THIS DATA SET COLLECT THE ANSWERS OF THE EXPERTS OF PHASE 2 AND PHASE 3.</span></span></p>
GGCMI Phase 2 masks and growing season input data
<p>Growing season data for crops as supplied to modelers in the GGCMI Phase 2 experiment (Franke et al. 2020). Other than for wheat, which is split in spring wheat and winter wheat in Phase 2, the growing season input data is the same as in Phase 1 (Elliott et al. 2015).</p> <p>A boolean mask on what regions can be excluded from the simulations, modeling all crops and irrigation systems everywhere otherwise.</p> <p>A mask assigning harvested wheat areas to winter or spring wheat.</p> <p> </p> <p>References:</p> <p>Franke J, Müller C, Elliott J, Ruane AC, Jagermeyr J, Balkovic J, Ciais P, Dury M, Falloon P, Folberth C, Francois L, Hank T, Hoffmann M, Izaurralde RC, Jacquemin I, Jones C, Khabarov N, Koch M, Li M, Liu W, Olin S, Phillips M, Pugh TAM, Reddy A, Wang X, Williams K, Zabel F, and Moyer E. 2020, The GGCMI Phase II experiment: global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0), Geosci. Model Dev. Discuss., 2019, 1-30, doi: <a href="http://dx.doi.org/10.5194/gmd-2019-237">10.5194/gmd-2019-237</a></p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:<a href="http://dx.doi.org/10.5194/gmd-8-261-2015">10.5194/gmd-8-261-2015</a>.</p>
Generalized Approximate Message Passing Practical 2D Phase Transition Simulations Dataset 2
<p>This deposition contains the results from a simulation of phase transitions for various practical 2D and 3D problem suites when using the Generalised Approximate Message Passing (GAMP) reconstruction algorithm.</p> <p>The deposition consists of:</p> <ol> <li>Five HDF5 databases containing the results from the phase transition simulations (<em>gamp_practical_2d_phase_transitions_ID_[0-4]_of_5.hdf5</em>).</li> <li>The Python script which was used to create the databases (<em>gamp_practical_2d_phase_transitions.py</em>).</li> <li>A Python module with tools needed to run the simulations (<em>gamp_pt_tools.py</em>).</li> <li>MD5 and SHA256 checksums of the databases and Python scripts (<em>gamp_practical_2d_phase_transitions.MD5SUMS / gamp_practical_2d_phase_transitions.SHA256SUMS</em>).</li> </ol> <p>The HDF5 databases are licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python scripts are licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) .</p> <p><strong>The files are provided as-is with no warranty as detailed in the above mentioned licenses.</strong></p>
UF & UAB's Phase 2 Demonstration Study: Developing a Model to Support Transportation System Decisions considering the Experiences of Drivers of all Age Groups with Autonomous Vehicle Technology (Project A3)
<p>Enclosed you will find the data collected during our STRIDE Phase II research project (A3) and a data dictionary.</p>
Рис. 2. Размерная структура G. lacustris в ΛитораΛьной зоне озера АрахΛей: 1 — июнь; 2 — август; 3 — октябрь Fig. 2. G. lacustris population size structure in the Lake Arakhley littoral zone: 1 — June, 2 — August, 3 — October in The life cycle of Gmelinoides fasciatus (Stebbing, 1899) and Gammarus lacustris (Sars, 1863) amphipods in the lake Arakhley littoral during the extreme low-water phase of the hydrological cycle
Рис. 2. Размерная структура G. lacustris в ΛитораΛьной зоне озера АрахΛей: 1 — июнь; 2 — август; 3 — октябрь Fig. 2. G. lacustris population size structure in the Lake Arakhley littoral zone: 1 — June, 2 — August, 3 — October
Рис. 1. Размерная структура Gm. fasciatus в ΛитораΛьной зоне озера АрахΛей: 1 — в июне; 2 — в августе; 3 — в октябре; 4 — в Αекабре 2017 г. и июне 2018 г. Fig. 1. Gm. fasciatus population size structure in the Lake Arakhley littoral zone: 1 — June; 2 — August; 3 — October; 4 — December, 2017 and June, 2018 in The life cycle of Gmelinoides fasciatus (Stebbing, 1899) and Gammarus lacustris (Sars, 1863) amphipods in the lake Arakhley littoral during the extreme low-water phase of the hydrological cycle
Рис. 1. Размерная структура Gm. fasciatus в ΛитораΛьной зоне озера АрахΛей: 1 — в июне; 2 — в августе; 3 — в октябре; 4 — в Αекабре 2017 г. и июне 2018 г. Fig. 1. Gm. fasciatus population size structure in the Lake Arakhley littoral zone: 1 — June; 2 — August; 3 — October; 4 — December, 2017 and June, 2018
FIG. 2 in Two-phase medium - a new approach to microbiological culturing
FIG. 2. — Algal species growing on two-phase media with granite: A, Muriella decolor Vischer; B, Vischeria polyphem (Pitschmann) Kryvenda, Rybalka, Wolf & Friedl; C, Klebsormidium dissectum (F.Gay) H.Ettl & Gärtner. Scale bars: 10 µm.
Figure 2 in Morphological and Morphometrical Features in Dunaliella salina (Chlamydomonadales, Dunaliellaceae) During the Two-phase Cultivation Mode
Figure 2. Changes of D. salina morphometrical parameters: cell height and width (A, B), dividing and non-dividing cells volume (C, D) and percentage of cells with different volume (E, F) in "green" and "red" cultivation phase
Figure 2 in Root deformation affects mineral nutrition but not leaf gas exchange and growth of Genipa americana seedlings during the recovery phase after soil flooding
Figure 2. Concentrations of P in leaves for G. americana seedlings without or with root deformation (RD) after 28 days of soil drainage (recovery). N = 3. Means followed by the same letter are not significantly different according to Tukey's test (p <0.05). Capital letters represent comparisons water effects within root conditions and lower case letters represent comparisons of roots effects within water conditions.
Fig. 2. a in Lackey Moth (Malacosoma Neustria L., Lasiocampidae, Lepidoptera) Population During The Eruptive Phase
Fig. 2. a — destruction of 80% of the leaves at the SOM; б — leaf blades of Quercus mongolica destroyed by M. neustria, 2019. (photos by author)
Fig. 2 in A review of Sciurus Group studies on the red squirrel (Sciurus vulgaris): presence, population density and colour phases in Lombardy (Italy)
Fig. 2 - Fur colour polymorphism in Sciurus vulgaris. Collection of the Museum of Natural History of Milan MSNM. (Photo by Carlo Biancardi).
Figure 2. Correct Classified Instances for different data sets-Intelligent System for Diagnosis of a Three-Phase Separator
<p>The data mining models may be considered a superior</p> <p>technique that may be successful</p> <p>applied in diagnosis and may be develop in the futu</p> <p>re on the base of more training data to increase</p> <p>the accuracy of results.</p> <p>Industrial processes are dynamic processes with ran</p> <p>dom behavior and whose evolution over</p> <p>time cannot be predicted unless it is well known th</p> <p>e process model and use advanced predictive</p> <p>techniques. Consequently, design and implement an a</p> <p>utomated online monitoring and diagnosis</p> <p>three-phase separator remains a future direction of</p> <p>research conducted so far.</p> <p>Conceptually, this system should have permanent acc</p> <p>ess to data collected from field</p> <p>transducers, to be able to identify the type of fau</p> <p>lt occurred, to locate the fault and provide</p> <p>recommendations to remedy abnormal operating condit</p> <p>ion. Also, updating the database defects with</p> <p>new types of defects occurred and the adequate solu</p> <p>tions adopted for eliminating errors in the</p> <p>operating mode is an important feature to be consid</p> <p>ered during the design of the online diagnosis</p> <p>system. This is possible if the system would have s</p> <p>elf-learning capabilities. To acquire this "skill",</p> <p>the automatic online diagnosis system may contain a</p> <p>diagnosis module based on artificial neural</p> <p>networks.</p>
Data - Effect of electrolytes as adjuvants in GFP and LPS partitioning on aqueous two-phase systems: 2. Nonionic micellar systems
<p><strong>Overview</strong></p> <p>The production of recombinant biopharmaceuticals is highly dependent of a proper choice of the downstream processing stages. Particularly, the purification that must ensure that all the endotoxins (lipopolysaccharide - LPS) are efficiently removed from the final product. This dataset contains the raw data and statistical analysis for the research entitled - "Effect of electrolytes as adjuvants in GFP and LPS partitioning on aqueous two-phase systems: 2. Nonionic micellar systems". </p> <p><strong>Info</strong></p> <p>ANOVA_Turkey_Sub.R <- code for ANOVA analysis in R statistic 3.3.3 <br> glm.R <- code for GLM analysis in R statistic 3.3.3<br> K&REC_ORG_ANOVA.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) and recover (REC) for ANOVA analysis</p> <p>K_ORG_ANOVA.docx <- File with ANOVA result of partition coefficient (K) for GFP</p> <p>REC_ORG_ANOVA.docx <- File with ANOVA result of partition coefficient (REC) for GFP</p> <p>REM_LPS_ORG_ANOVA.csv <- File with raw values organized in a spreadsheet of LPS removal for ANOVA analysis</p> <p>REM_LPS_ORG_ANOVA.docx <- File with ANOVA result of removal of LPS</p> <p>Stability__ORG_ANOVA.csv <- File with raw values organized in a spreadsheet of GFP stability for ANOVA analysis</p> <p>Stability__ORG_ANOVA.docx <- File with ANOVA result of GFP stability</p> <p>K_ORG_glm_005.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in 0.05M salt assays </p> <p>K_ORG_glm_005.doc <- File with GLM analysis of GFP partition coefficient (K) in 0.05M salt assays </p> <p>K_ORG_glm_005_QQ.png <- Residual quantile plot of GLM analysis for partition coefficient (K) in 0.05M salt assays </p> <p>K_ORG_glm_025.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in 0.25M salt assays </p> <p>K_ORG_glm_025.doc <- File with GLM analysis of GFP partition coefficient (K) in 0.25M salt assays </p> <p>K_ORG_glm_025_QQ.png <- Residual quantile plot of GLM analysis for partition coefficient (K) in 0.25M salt assays </p> <p>REC_ORG_glm_005.csv <- File with raw values organized in a spreadsheet of GFP recover (REC) for GLM analysis in 0.05M salt assays</p> <p>REC_ORG_glm_005.doc <- File with GLM analysis of GFP recover (REC) in 0.05M salt assays </p> <p>REC_ORG_glm_005_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in 0.05M salt assays </p> <p>REC_ORG_glm_025.csv <- File with raw values organized in a spreadsheet of GFP recover (REC) for GLM analysis in 0.25M salt assays</p> <p>REC_ORG_glm_025.doc <- File with GLM analysis of GFP recover (REC) in 0.25M salt assays </p> <p>REC_ORG_glm_025_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in 0.25M salt assays </p> <p>REM_ORG_glm_005.csv <- File with raw values organized in a spreadsheet of LPS removal (REM) for GLM analysis in 0.05M salt assays</p> <p>REM_ORG_glm_005.doc <- File with GLM analysis of LPS removal (REM) in 0.05M salt assays </p> <p>REM_ORG_glm_005_QQ.png <- Residual quantile plot of GLM analysis of LPS removal (REM) in 0.25M salt assays</p> <p>REM_ORG_glm_025.csv <- File with raw values organized in a spreadsheet of LPS removal (REM) for GLM analysis in 0.25M salt assays</p> <p>REM_ORG_glm_025.doc <- File with GLM analysis of LPS removal (REM) in 0.25M salt assays </p> <p>REM_ORG_glm_025_QQ.png <- Residual quantile plot of GLM analysis of LPS removal (REM) in 0.25M salt assays</p> <p> </p> <p><strong>Annotation</strong></p> <p>12/12 - Concentration of 12% of each polymer PEG/NaPA</p> <p>16/16 - Concentration of 16% of each polymer PEG/NaPA</p> <p>P/N - PEG/NaPA</p> <p>10e4, 10e5, 10e6 - Concentration of LPS in scientific notation - 10000, 100000, 100000 EU/mL</p> <p>poly - Polymer</p> <p>salt - Salt concentration in the assay</p> <p>tsalt - Type of salt in the assay (NaCl, KNO3, KI and Li2SO4)</p> <p>lps - lipopolysaccharide</p> <p>K - GFP partition coefficient</p> <p>REM - LPS removal</p> <p>REC - GFP recover</p> <p>wo_salt - Assay without salt addition</p> <p><strong>Acknowledgements</strong></p> <p>The authors are grateful for financial support from FAPESP (São Paulo Research Foundation, Brazil) through the following projects: 2005/60159-7; 2007/51978-0; 2014/16424-7; and 2014/19793-3. The authors also acknowledge the support from CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brazil) through the process #0366/09-9 and CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil).</p> <p><strong>Consider citing our work. </strong></p> <p>1. Work in progress...</p>
Single crystal diffraction raw data for Fampridine hydrochloride Phase 2
<p>A set of diffraction images in Rigaku Oxford Diffraction (img) format, collected on a Rigaku FRE+ diffractometer, equipped with HF Varimax confocal mirrors and an AFC12 goniometer and HG Saturn 724+ detector diffractometer.</p> <p>The sample is Phase 2 of the Fampridine hydrochloride organic salt - a system that forms numerous complicated phases. The authors wish to make the raw data available so that those who wish to explore the modelling of these exceptionally complex systems may process the data themselves.</p>
Fig. 2 in A new omomyid primate from the earliest Eocene of southern England: First phase of microchoerine evolution
Fig. 2. Scanning electron micrographs of gold–palladium coated epoxy casts of lower molars of the omomyid primate Melaneremia schrevei sp. nov., central channel, upper shelly clays, Woolwich Formation, Sandilands cutting, Park Hill, Croydon. A. Right m1 (reversed), NHMUK.M85504. B. Right m2 (reversed), NHMUK.M85502. C. Left m2, NHMUK.M85503. Views are occlusal (A1, B1, C1), buccal (A3, B2, C2), and lingual (A2, B3, C3).
Liquid Phase Electron Microscopy of Bacterial Ultrastructure (1 of 2)
<p>Raw and processed LM, ADF-/BF-STEM, EDX and TEM images from study of <em>D.radiodurans</em> encapsulated in graphene liquid cells.</p>
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.