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1,237 results for “AC”
Catalysis of Ac-DEVD-AMC by procaspase-3 (dataset formatted for analysis by interferENZY)
<p><strong>Main description</strong></p> <p>This dataset depicts the catalysis of the fluorogenic substrate Ac-DEVD-AMC to the fluorescent substrate AMC by recombinant procaspase-3 obtained in yeast cell extracts, for fixed concentration of enzyme and variation of concentration of initial substrate. It was originally documented in <em>Biophysical Chemistry 252 (2019) 106193</em> (<a href="https://doi.org/10.1016/j.bpc.2019.106193">https://doi.org/10.1016/j.bpc.2019.106193</a>), and then used as a study case for the webserver interferENZY (a web-based tool for enzymatic assay validation and standardized kinetic analysis; visit <a href="https://interferenzy.i3s.up.pt">https://interferenzy.i3s.up.pt</a> for more information). To this end, it was converted to the format here presented: tab-separated *.txt input required for interferENZY analysis.</p> <p> </p> <p><strong>Dataset organization</strong></p> <p>Line 1: Tab-separated initial concentrations of substrate Ac-DEVD-AMC in micromolar (µM) concentration</p> <p>Line 2: Concentration of protein in yeast extract (0.123 mg/mL)</p> <p>Line 3: Units of time</p> <p>Line 4: Units of concentration for substrate values and measurements</p> <p>Line 5: Dataset name</p> <p>Line 6 and downwards: Tab-separated column-pairs of the progress curves (time,Product) corresponding to the indicated values of initial concentrations of substrate in line 1</p> <p> </p> <p><strong>Contact information:</strong></p> <p>Maria Filipa Pinto (mfpinto@i3s.up.pt)<br> Pedro M. Martins (pmartins@ibmc.up.pt)</p> <p>i3S – Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, 4200-135 Porto, Portugal. Telephone number: +351 226 074 900</p>
Chlorophyll a concentration, particulate organique carbon, and particle mean size index [gamma; 0.2 - 20 µm] measured using an hyperspectral spectrophotometer [ACS, Wetlabs] during the Tara Pacific Expedition 2016-2018
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples (Gorsky et al. 2019, Planes et al. 2019, Flores et al. 2020). The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis. Here we provide the continuous dataset originating from the hyperspectral and multispectral spectrophotometers [ACS] instruments acquiring continuously during the full course of the campaign. Surface seawater was pumped continuously through a hull inlet located 1.5 m under the waterline using a membrane pump (10 LPM; Shurflo), circulated through a vortex debubbler, a flow meter, and distributed to a number of flow-through instruments. An [ACS] spectrophotometer (WETLabs) measured hyper-spectral (4 nm resolution) attenuation and absorption in the visible and near infrared except between Panama and Tahiti where an AC-9 multispectral spectrophotometer (WETLabs) was used instead. The flow was automatically directed through a 0.2 µm filter for 10 minutes every hour before being circulated through the spectrophotometer to eliminate the impact of biofouling and instrument drift and estimate particulate absorption [ap] and attenuation [cp] (Slade et al. 2010). Chlorophyll a content was estimated from particulate absorption line height at 676 nm (Boss et al. 2001). The particulate organic carbon concentration [poc] was estimated using an empirical relation (Gardner et al. 2006) between measured [poc] and measured [cp]. An indicator for size distribution of particles between 0.2 and ~20 µm [gamma] was calculated from [cp] (Boss et al 2001). The data was processed with custom software for underway optical data (InLineAnalysis software available on GitHub). The detailed information regarding the data processing is given in the processing report attached with the data and in Lombard et al. (In prep.). These results are preliminary: no matchup with in-situ chlorophyll from HPLC or [poc] measurements were performed.</p>
Raw data for manuscript A. Dey et. al., ACS Nano 2023, 17, 16, 16080–16088.
<p><strong>Scan_00165.zip</strong>: This compressed file contains the raw X-ray data collected at beamline P06 at PETRA III, DESY, relevant for the manuscript. From this dataset that includes Bragg diffraction and X-ray fluorescence data from the indium Kα, gallium Kα, and arsenic Kα lines, all figures containing X-ray data in the manuscript were created. For viewing, use, e.g., <a href="https://ncnr.nist.gov/ncnrdata/view/nexus-hdf-viewer.html">https://ncnr.nist.gov/ncnrdata/view/nexus-hdf-viewer.html</a>.</p> <p><strong>W795_29a.tif</strong>: This data file is the raw SEM image from which the high magnification cut-out in the manuscript figure was taken. The SEM image was collected at an acceleration voltage of 15 kV using a trough-lens detector in secondary electron imaging mode. The field of view is 2.12 µm. For viewing, use any standard image viewer.</p> <p><strong>W795_InGaAsQDs.0_00023.spm</strong>: This data file is the raw AFM image from which after processing the AFM figures and AFM height information given in the manuscript were obtained. The image size is 200 nm × 200 nm, and was obtained at a scanning speed of 1 Hz with a resolution of 512 × 512 pixels. For viewing, install, e.g., <a href="http://gwyddion.net/download.php">http://gwyddion.net/download.php</a>.</p>
ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway)
<p>The ACS_Bayelva_class dataset contains 302 high-resolution binary snow cover images that were obtained by classifying orthrorectified photographs of a 1.77 km^2 area of interest in the Bayelva catchment. This latest version (2.0) of the dataset includes the orthorectified photographs that were used to classify the binary snow cover images. The catchment is close to Ny-Ålesund, the northernmost permanent civilian settlement in the world and a major hub for polar research, in the Norwegian high-Arctic Svalbard archipelago. The imagery has a (roughly) daily temporal resolution and a ground sampling distance (pixel spacing) of 0.5 m. The dataset spans 6 snowmelt seasons, covering the months May-August for the period 2012-2017. The orthophotos were obtained by processing oblique time-lapse photographs taken by a terrestrial automatic camera system (ACS) mounted at 562 m a.s.l. near the summit of Scheteligfjellet (719 m a.s.l.) a few kilometers west of Ny-Ålesund. The orthophotos were manually classified into binary snow cover images (0=no snow, 1=snow) by iteratively selecting a (visually) optimal threshold on the intensity in the blue-band for each image. More details are provided in the study of Aalstad et al. (2020) [a copy is available in this repository] where this dataset was created. The ACS was maintained by scientists from the group of Sebastian Westermann at the Section for Physical Geography and Hydrology in the Department of Geosciences at the University of Oslo, Oslo, Norway. </p>
Dataset associated to Picone, A. et al., ACS Appl. Nano Mater. 2021, 4, 12, 12993–13000
<p>Dataset associated to paper published under the SINFONIA project</p> <p>Picone, A. et al., ACS Appl. Nano Mater. 2021, 4, 12, 12993–13000</p>
Dataset supporting the paper "Molecular Approach for Engineering Interfacial Interactions in Magnetic/Topological Insulator Heterostructures. ACS Nano 14, 6285 (2020)"
<p>Dataset corresponding to theoretical calculations in the paper "Molecular Approach for Engineering Interfacial Interactions in Magnetic/Topological Insulator Heterostructures" ACS Nano 14, 6285 (2020), DOI: <a href="https://doi.org/10.1021/acsnano.0c02498">10.1021/acsnano.0c02498</a></p> <p>List of files:</p> <p>Several folders corresponding to the figures of the paper. They contain the following files:</p> <ul> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>)</li> <li>.agr files: grace files (<a href="https://plasma-gate.weizmann.ac.il/Grace/">https://plasma-gate.weizmann.ac.il/Grace/</a>).<br> </li> </ul>
Dataset supporting the paper "Single-Spin Sensing: A Molecule-on-Tip Approach. ACS Nano 18, 13829 (2024)"
<p>Dataset corresponding to theoretical calculations in the paper "Single-Spin Sensing: A Molecule-on-Tip Approach" ACS Nano 18, 13829 (2024) DOI: https://doi.org/10.1021/acsnano.4c02470</p> <p>Please cite as:</p> <p>Alex Fétida, Olivier Bengone, Michelangelo Romeo, Fabrice Scheurer, Roberto Robles, Nicolás Lorente, and Laurent Limot. Dataset supporting the paper "Single-Spin Sensing: A Molecule-on-Tip Approach. ACS Nano 18, 13829 (2024)" DOI: 10.5281/zenodo.13774118</p> <p>List of files:</p> <p>Several folders corresponding to the figures of the paper. They contain:</p> <p>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (https://jp-minerals.org/vesta/en/).</p> <p>.agr: grace files (https://plasma-gate.weizmann.ac.il/Grace/).</p> <p>Image files in png format.</p>
XRDs of Materials used in the Supplementary Information file of A. Lowe et al Exploring the Heat of Water Intrusion ... ACS Appl. Mater. Interfaces 2024, 16, 5286−5293
<p>Data plots were limited to 2theta range from 5 degrees to 50 degrees. CuKa</p>
Dataset supporting the paper "Large Orbital Moment of Two Coupled Spin‑Half Co Ions in a Complex on Gold. ACS Nano 17, 10608 (2023)"
<p>Dataset corresponding to theoretical calculations in the paper "Large Orbital Moment of Two Coupled Spin‑Half Co Ions in a Complex on Gold" ACS Nano 17, 10608 (2023), https://pubs.acs.org/doi/10.1021/acsnano.3c01595</p> <p>List of files:</p> <p>Several folders corresponding to the figures of the paper. They contain:<br> .siesta files: STM images in WsXM format (http://www.wsxm.eu/) simulated using STMpw (https://doi.org/10.5281/zenodo.3581159).<br> CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (https://jp-minerals.org/vesta/en/).<br> .agr: grace files (https://plasma-gate.weizmann.ac.il/Grace/).</p>
ESA SEOM-IAS – Measurement and ACS database O3 UV region
<p>The database contains measurements and absorption cross sections generated within the framework of the ESA project SEOM-IAS (Scientific Exploitation of Operational Missions - Improved Atmospheric Spectroscopy Databases), ESA/AO/1-7566/13/I-BG. Details on the project can be found at http://www.wdc.dlr.de/seom-ias/.</p> <p>The measurements were recorded at the German Aersopace Center (DLR) to provide a new absorption cross section database for ozone according to the needs of the TROPOMI instrument aboard the Sentinel 5-P satellite. The data are compiled in 2 zip files, one for the measurements (O3_UV_region_measurement_database_13112018.zip), one for the absorption cross sections (O3_UV_region_absorption_cross_section_database_13112018.zip) and a readme file (ESA_SEOM_IAS_spectra_O3UVRegion_readme.docx).</p> <p>The file “ACS_pTpoly_2nd+1st_order_BGcoefffit_constant_offset_V2.asc” in version II replaces the older version which contained an error in the wavelength axis. The readme file was updated, too.</p>
Numerical data for "Signatures of Kondo-Majorana interplay in ac response"
<p>Raw numerical data used to produce figures 2-8 in the article <em>Signatures of Kondo-Majorana interplay in ac response</em>, published as Phys. Rev. B <strong>109</strong>, 075432 (2024), DOI: 10.1103/PhysRevB.109.075432, and other data obtained within the same project.</p>
Electrochemical data shown in A. Fasano, A. Jacq-Bailly, J. Wozniak, V. Fourmond, and C. Léger, « Catalytic Bias and Redox-Driven Inactivation of the Group B FeFe Hydrogenase CpIII », ACS Catalysis (2024). doi: 10.1021/acscatal.4c01352
<p>Text file of all the electrochemical data shown in the following paper: A. Fasano, A. Jacq-Bailly, J. Wozniak, V. Fourmond, and C. Léger, « Catalytic Bias and Redox-Driven Inactivation of the Group B FeFe Hydrogenase CpIII », ACS Catalysis (2024). <a href="dx.doi.org/10.1021/acscatal.4c01352" target="_blank" rel="noopener">doi: 10.1021/acscatal.4c01352</a></p>
Dataset for publication "Chemical-Dealloying-Derived PtPdPb-Based Multimetallic Nanoparticles: Dimethyl Ether Electrocatalysis and Fuel Cell Application" in ACS Applied Materials & Interfaces 2023, 15, 49, 56930–56944
<p>Dataset for publication "Chemical-Dealloying-Derived PtPdPb-Based Multimetallic Nanoparticles: Dimethyl Ether Electrocatalysis and Fuel Cell Application" in ACS Applied Materials & Interfaces 2023, 15, 49, 56930–56944, https://doi.org/10.1021/acsami.3c11003</p> <p>Dataset contains XRD, SAXS, Particle size distribution, HRTEM, Electrochemical characterisation, Fuel cell current-voltage curve, DFT calculations, Elemental maps and electrode stability measurements made on pristine and chemically dealloyed carbon supported Pt2PdPb2 nanoparticles.</p>
Data for Spectral Induced Polarization of ZVI-AC-Sand Mixtures in Groundwater Remediation
<p>这是手稿“揭开地下水修复中 ZVI-AC-Sand 混合物的光谱诱导极化响应”的初始数据</p>
Data and results for the paper: "From Bugs to Benefits: Improving User Stories by Leveraging Crowd Knowledge with CrUISE-AC"
<div> <div>We provide the following files used in the study "From Bugs to Benefits: Improving User Stories by Leveraging Crowd Knowledge with CrUISE-AC".</div> <div>The paper has been accepted for presentation in the research track of the IEEE/ACM International Conference on Software Engineering (ICSE) 2025 and will be included in the conference proceedings.</div> <div>The preprint is available on <a href="http://arxiv.org/abs/2501.15181" target="_blank" rel="noopener">arXiv</a>.</div> <br> <div><strong>User stories e-commerce.xlsx</strong></div> <div>307 real-world user stories from 3 different eCommerce projects.</div> <br> <div><em>Project A</em> defines a complete set of requirements for a B2C focused onlineshop of a publishing house who aims do sell his own publications directly.</div> <div><em>Project B</em> contains a partial set of requirements for a B2C focused onlineshop of a bookseller.</div> <div><em>Project C</em> includes a subset of B2C and B2B requirements for an online bookstore, supplemented by an eProcurement module designed to provide information and automation for industrial customers.</div> <div>Most of the user stories come with additional acceptance criteria, written in unstructured natural language.</div> <br> <div>The user stories have been anonymized and the merchant's real names were replaced with neutral terms.</div> <br> <div>Columns</div> <div>- ID: a unique ID we assigned across all projects</div> <div>- Project: user story belongs to project A, B or C</div> <div>- Connextra: user story in connextra pattern</div> <div>- Acceptance Criteria: acceptance criteria that came with the user story</div> <br> <div><strong>User stories CMS.xlsx</strong></div> <div>34 CMS related user stories from a dataset that was originally created by</div> <div>*Lucassen, G., Dalpiaz, F., van der Werf, J.M.E., Brinkkemper, S.: Visualizing user</div> <div>story requirements at multiple granularity levels via semantic relatedness. In: Con-</div> <div>ceptual Modeling: 35th International Conference, ER 2016, Gifu, Japan, November</div> <div>14-17, 2016, Proceedings 35. pp. 463–478. Springer (2016)*</div> <br> <div>Columns</div> <div>- ID: a unique ID we assigned</div> <div>- Connextra: user story in connextra pattern</div> <br> <div><strong>Issues e-commerce.xlsx</strong></div> <div>54,396 issues, we harvested from seven different issue trackers between June 2011 and July 2024</div> <div>- magento2 (https://github.com/magento/magento2/issues)</div> <div>- nopCommerce (https://github.com/nopSolutions/nopCommerce/issues)</div> <div>- OpenCart (https://github.com/opencart/opencart/issues)</div> <div>- PrestaShop (https://github.com/PrestaShop/PrestaShop/issues)</div> <div>- Shopware5 (https://issues.shopware.com/?products=SW-5)</div> <div>- Shopware6 (https://issues.shopware.com/?products=SW-6)</div> <div>- WooCommerce (https://github.com/woocommerce/woocommerce/issues)</div> <br> <div>Columns</div> <div>- id: unique ID we have assigned</div> <div>- Issue Tracker: issue tracker this issue originates from</div> <div>- Title: title of the original issue</div> <div>- Body: body / description of the original issue</div> <div>- Preprocessed: result of preprocessing the issue as described in the paper</div> <div>- Sample: issue was part of our 3,500 sample issues we used to evaluate CrUISE-AC</div> <br> <div><strong>Issues CMS.xlsx</strong></div> <div>64,500 issues, we harvested from two different issue trackers between April 2002 and August 2024</div> <div>- Moodle (https://github.com/magento/magento2/issues)</div> <div>- Umbraco (https://github.com/nopSolutions/nopCommerce/issues)</div> <br> <div>Columns are the the same as for "Issues e-commerce.xlsx"</div> <br> <div><strong>trivia-trainingdata.csv</strong></div> Manually labelled dataset to train the trivia classifier. <div>The dataset contains 1916 phrases with an even distribution of 958 trivia and 958 non-trivia phrases.</div> <br> <div>- Label = 1: this sentence is trivia</div> <div>- Label = 0: this sentence is not considered trivia</div> <br> <div>Any source code was replaced by [CODE] to simplify the classification process. Source code in markdown could be identified easily as it is enclosed by a special character https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/creating-and-highlighting-code-blocks</div> <br><strong>Prompts</strong><br> <div><em>prompt_match.txt</em>: prompt we used across all LLMs to assess, if an issue potentially might affect a given user story</div> <em>prompt_generate.txt</em>: GPT4-turbo prompt to convert an issue text into gherkin-style acceptance criteria for a given user story<br> <div><em>prompt_evaluate.txt</em>: GPT4-turbo prompt to assess the usefulness of a newly generated acceptance criteria for a given user story</div> <br> <div><strong>Evaluation e-commerce.xlsx</strong></div> issue / user story pairs, generated acceptance criteria and result of manual evaluation.<br> <div> </div> <div>Columns</div> <div>- StoryID: unique ID of the user story (refer to User stories e-commerce.xlsx)</div> <div>- IssueID: unique ID of the issue (refer to Issues e-commerce.xlsx)</div> <div>- Issue: preprocessed issue text used as basis to generate the acceptance criterion</div> <div>- Connextra: user story in connextra pattern</div> <div>- Existing AC: acceptance criteria that originally came with the user story</div> <div>- AC: by CrUISE-AC generated acceptance criterion</div> <div>- AC_Explanation: explanation generated by CrUISE-AC why this AC adds new knowledge to the current user story</div> <div>- E1: evaluation result by expert 1 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E2: evaluation result by expert 2 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E3: evaluation result by expert 3 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E4: evaluation result by expert 4 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- 3/4 majority: did at least 3 experts assess this AC as relevant (1 = yes; 0 = no)</div> <br> <div><strong>Evaluation CMS.xlsx</strong></div> <div>- StoryID: unique ID of the user story (refer to User stories CMS.xlsx)</div> <div>- IssueID: unique ID of the issue (refer to Issues CMS.xlsx)</div> <div>- Issue: preprocessed issue text used as basis to generate the acceptance criterion</div> <div>- Connextra: user story in connextra pattern</div> <div>- AC: by CrUISE-AC generated acceptance criterion</div> <div>- AC_Explanation: explanation generated by CrUISE-AC why this AC adds new knowledge to the current user story</div> <div>- E1: evaluation result by expert 1 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E4: evaluation result by expert 4 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E5: evaluation result by expert 5 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- 2/3 majority: did at least 2 experts assess this AC as relevant (1 = yes; 0 = no)</div> </div>
Vibration assisted drilling (VAD) application to the manufactured maraging steel X3NiCoMoTi18-9-5 (1.2709) and aluminium AlSi10Mg (EN AC-43000) parts
<p>Repository containing data from vibration assisted drilling (VAD) experiments on steel and Aluminum 3D powderbed manufactured parts.</p> <p>Please refer to README.MD (or .PDF), which contains a brief description of the chosen materials, parts and tools An explanation of the data aquisition and processing methods, together with the used nomenclature is given as well.</p>
Рис. 2. UPGMA — фенограма генетических дистанций (Nei, 1972) между видами и популяциями двустворчатых моллюсков. П р и м е ч а н и е. Aa — A. anatina, Ac — A. cygnea, Pc — P. complanata, Sw — S. woodiana. Речные системы: 1 — Дунай, 2 — р. Тиса, 3 — Верхний Днестр, 4 — Нижний Днестр, 5 — р. Ингул, 6 — р. Западный Буг, 7 — р. Припять, 8 — р. Верхний Днепр, 9 — р. Рось, 10 — р. Псёл, 11 — р. Северский Донец, 12 — р. Салгир. in Genetic And Morphological Variability And Differentiation Of Freshwater Mussels (Bivavia, Unionidae, Anodontinae) In Ukraine
Рис. 2. UPGMA — фенограма генетических дистанций (Nei, 1972) между видами и популяциями двустворчатых моллюсков. П р и м е ч а н и е. Aa — A. anatina, Ac — A. cygnea, Pc — P. complanata, Sw — S. woodiana. Речные системы: 1 — Дунай, 2 — р. Тиса, 3 — Верхний Днестр, 4 — Нижний Днестр, 5 — р. Ингул, 6 — р. Западный Буг, 7 — р. Припять, 8 — р. Верхний Днепр, 9 — р. Рось, 10 — р. Псёл, 11 — р. Северский Донец, 12 — р. Салгир.
Dataset: AC Immune SA (ACIU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: AC Immune SA (ACIU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Figure 1 in Angiostrongylus cantonensis cathepsin B-like protease (Ac-cathB-1) is involved in host gut penetration
Figure 1. Selection and identification of the stably expressed cell line. (A) Schematic diagram of vector construction. Replacement of the GFP with a sequence containing 50 IgK SP and 30 myc-His sequence using an isocaudamer technique resulted in the creation of pBobi-IMHIP vector. Lentiviral vector pBobi-cathB1 was generated by insertion of Ac-cathB-1 coding sequence excluding SP into the pBobi-IMHIP vector by XbaI and BamHI restriction sites. (B) Lentiviral packaging of pBobi-cathB1 and pBobi-GFP. Under the fluorescence microscope, more than 90% of cells in the pBobi-GFP transfected group displayed green fluorescence. (C) Assessment of the establishment of 293T-cathB1. Anti-Myc IF staining was performed in the two cell lines. All cells in 293T-cathB1 showed red fluorescence representing 100% positive, while all cells in 293T-GFP showed no fluorescence under the red fluorescent filter due to the lack of myc-tag expression. (D) Tests for rAccathB-1 expression. Total RNA and protein samples were extracted from two groups of cells. Results of RT-PCR (top two panels) and western blot (lower two panels) showed that rAc-cathB-1 was highly expressed in 293T-cathB1 at both mRNA and protein levels. Actb, RT-PCR control and B-actin, loading control for western blot; WL, white light; and bar = 100 µm.
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.