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533 results for “Quality control”
Extended data for the paper: "SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters"
<p>Extended data 1 to 4 for the software article:<br>SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters. </p> <p>The extended data is tables and a Figure output and input from/to SentemQC runs relevant for the SentemQC paper.</p>
Interagency Ecological Program: Water quality, fish, and zooplankton monitoring and modeling to support the 2018 Suisun Marsh Salinity Control Gates Summer Action
In summer 2018 we used a unique water control structure in the San Francisco Estuary (SFE) to direct a managed flow pulse into Suisun Marsh, one of the largest contiguous tidal marshes on the west coast of the United States. The action was designed to increase habitat suitability for the endangered Delta Smelt Hypomesus transpacificus, a small osmerid fish endemic to the upper SFE. The approach was to operate the Suisun Marsh Salinity Control Gates (SMSCG) in conjunction with increased Sacramento River tributary inflow to direct an estimated 160 x 10^6 m3 pulse of low salinity water into Suisun Marsh during August, a critical time period for juvenile Delta Smelt rearing. This dataset includes physical and biological monitoring data collected for the action. Datasets include Delta Smelt catch from the USFWS Enhanced Delta Smelt Monitoring program, zooplankton and Microcystis abundance from the Environmental Monitoring Program, historic Delta Smelt catch from the Summer Townet Survey, Delta Outflow from the Dayflow model, extent of Delta Smelt habitat from the UnTRIM Bay-Delta model, and water quality (Salinity, Temperature, Chlorophyll, and Turibidity) collected at continuous sondes at three locations. These data are associated with the manuscript "Evaluation of a large-scale flow manipulation to the upper San Francisco Estuary: Response of habitat conditions for an endangered native fish," by Dr. Ted Sommer, et al. 2020 PLOS One, in review.
Artificial Intelligence for Quality Control of manufacturing operations: Macro-mechanical milling in the Pilot Line GAMHE 5.0.
<p>Quality is defined as the extent to which a product conforms to the design specifications and how it complies with the requirements of component functionality. For some industries, such as automotive and aeronautical, the quality of their parts is very important given the high requirements to which they are subject. However, difficulties arise from the fact that a measure of quality can only be evaluated ‘‘out-of-process”, resulting in losses because there is no alternative to removing defective parts from the production line. Therefore, it is necessary to apply Artificial Intelligence-based kits/solutions that provide in-process estimation to predict quality from some measured variables. </p> <p>The main goal of these datasets is to monitor the final quality of the manufactured components or parts by estimating surface roughness from vibration signals and cutting parameters information using Artificial Intelligence-based solutions. Surface roughness is an essential feature in quality control defined by the deviation in the direction of the normal vector of a real surface from its ideal form. Because the roughness measurement is an offline and post process procedure, being able to estimate this value online brings a series of benefits in terms of time and cost reduction in manufacturing lines, energy efficiency, unnecessary wear of tools and machines, etc. Once a part has been detected with a surface quality below what is desired, a series of corrective measures can be applied for the following operations, such as: reducing the feed rate percentage, increasing the percentage of spindle speed or reducing the axial depth per pass, etc.</p>
Artificial Intelligence for quality control in manufacturing operations: Micro-mechanical milling in the Pilot Line GAMHE 5.0
<p>Quality is defined as the extent to which a product conforms to the design specifications and how it complies with the requirements of component functionality. For some industries, such as automotive and aeronautical, the quality of of manufactured parts is very important due to the high requirements. However, difficulties arise from the fact that a measure of quality can only be evaluated ‘‘out-of-process”, resulting in losses because there is no alternative to removing defective parts from the production line. Therefore, it is necessary to incorporate AI-based kits/solutions that provide in-process estimation to predict quality from some measured variables.</p> <p>The main goal of these datasets is to enable monitoring of final quality of the manufactured components or parts by estimating surface roughness from vibration signals and cutting parameters information. Surface roughness is an essential feature in quality control defined by the deviation in the direction of the normal vector of a real surface from its ideal form. Because the roughness measurement is an offline and post process procedure, being able to estimate this value online brings a series of benefits in terms of time and cost reduction in manufacturing lines, energy efficiency, unnecessary wear of tools and machines, etc. Once a part has been detected with a surface quality below what is desired, a series of corrective measures can be applied for the following operations, such as: reducing the feed rate percentage, increasing the percentage of spindle speed or reducing the axial depth per pass, etc.</p> <p>Workstation 4 (WS4) of the GAMHE 5.0 pilot line is a Kern Evo high-precision machining centre, with a maximum spindle speed of 50 000 rpm and Blum laser system and is used to run micro-milling and micro-drilling operations. In this experimental dataset, five cutting parameters were considered in the processes: spindle speed, <em>n</em>; feed rate, <em>f</em>; and axial depth of cut, <em>a<sub>P</sub></em>. The radial depth of cut, <em>a<sub>e</sub></em>; was equal to the mill tool radius, <em>r</em>, in all of the slots.</p> <p>These experiments were micro-milling operations with 0.3 mm, 0.5 mm, 0.8 mm and 1 mm-diameter mills on a sintered tungsten-copper alloy (W78Cu22). The data collected for each micro milling operation was the rms and peak value of the vibrations in the three-machine axis. In addition, five cutting parameters were also collected: position in <em>X</em> of the last point of the sample, feed rate, spindle speed, tool radius and axial depth.</p>
ValRun: GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration
<p><strong>VaLRun: </strong></p> <p><strong>Raw data of "GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration"</strong></p> <p>(Excel-, pdf-, GraphPad-files, mp4 videos and a READ-ME text file)</p> <p>The introduction of new therapeutics requires validation of Good Manufacturing Practice (GMP)-grade manufacturing including suitable quality controls. This is challenging for Advanced Therapy Medicinal Products (ATMP) with personalized batches. We have developed a person-alized, cell-based gene therapy to treat age-related macular degeneration and established a vali-dation strategy of the GMP-grade manufacture for the ATMP; manufacturing and quality control were challenging due to a low cell number, batch-to-batch variability and short production duration. Instead of patient iris pigment epithelial cells, human donor tissue was used to produce the transfected cell product (“tIPE”). We implemented an extended validation of 104 tIPE productions. Procedure, operators and devices have been validated and qualified by determining cell number, viability, extracellular DNA, sterility, duration, temperature and volume. Transfected autologous cells were transplanted to rabbits verifying feasibility of the treatment. A container has been engineered to insure a safe transport from the production to the surgery site. Criteria for successful validation and qualification were based on tIPE’s Critical Quality Attributes and Process Parameters, its manufacture and release criteria. The validated process and qualified operators are essential to bring the ATMP into clinic and offer a general strategy for the transfer to other manufacture centers and personalized ATMPs.</p>
Interagency Ecological Program: Monitoring of water quality, phytoplankton, zooplankton, clams, and Delta Smelt to support the Summer-Fall Suisun Marsh Salinity Control Gates Action 2018-2024
The Suisun Marsh Salinity Control Gates (SMSCG) have the potential to increase low-salinity-zone habitat for endangered Delta Smelt (Hypomesus transpacificus, California Endangered Species Act listed as Endangered, Federal Endangered Species Act listed as Threatened), and to allow them to more frequently occupy Suisun Marsh, especially Montezuma Slough, one of their most important rearing habitats. Operation of the SMSCG in summer and fall to improve Delta Smelt habitat are called for in the Biological Opinion and Incidental Take permit for the Central Valley Project and State Water Project. To support the adaptive management of the action, the California Department of Water Resources and collaborating agencies monitored water quality, phytoplankton, zooplankton, clams, and fishes during the SMSCG management actions in 2018. Monitoring has continued during the summer and fall months of all subsequent years, including both those with and without actions. This data package includes data collected by the Interagency Ecological Program’s (IEP) long-term monitoring programs supplemented with targeted sample collection where existing surveys lacked spatial or temporal coverage. Monitoring during no-action years will be used as a baseline for comparison during action years. This data package will be updated annually.
Data relating to Clyne et al. Quality, scope and reporting standards of randomised controlled trials in Irish Health Research: an observational study
<p>Data relating to the study reported in the paper "Quality, scope and reporting standards of randomised controlled trials in Irish Health Research: an observational study".</p>
Airborne Radar Quality Control with Machine Learning
<p>This repository contains the radar data collected by ELDORA required to train and test the random forest model discussed in "Airborne radar quality control with machine learning" by Alexander DesRosiers and Michael M. Bell at the Colorado State University Department of Atmospheric Science. The model used in the manuscript is also contained in a '.pkl' file. Upon publication, a link to the paper will be provided here. Finer points of the methodology were discussed in the manuscript and the python script (make_radarQC_rf_model.py) is commented to guide users through the process of creating the model.</p>
Quality controlled observations of hourly incoming shortwave radiation data at the surface for solar resource mapping in Norway (2016-2020).
<p>Observed hourly incoming shortwave radiation data at the surface of Norway for the years 2016-2020 along with quality control flags, visualization plots and a descriptive report. The data has been collected, visually inspected and quality controlled within the SunPoint project (SUn in Norway - POtential and INTegration of the solar energy resource, Norwegian Research Council project 320750). The main data source is frost.met.no but some gaps were filled with data directly obtained by the station holders.</p> <p>There are three NetDCF files for 47 stations selected after quality control:</p> <ul> <li>rsds_1hr_selection_v5_2016-2020.nc: Raw data</li> <li>rsds_flagged_1hr_selection_v5_2016-2020.nc: Raw data with flags</li> <li>rsds_cleaned_1hr_selection_v5_2016-2020.nc: Filtered data (i.e. all flagged data has been removed)</li> </ul> <p>and one NetCDF file for all available stations (106 stations)</p> <ul> <li>rsds_1hr_frost_and_more_2016-2020.nc</li> </ul> <p>Version 3.5 of the McClear clear-sky model is used for flagging which reduces the bias to ground measurements compared to earlier versions. </p> <p>The visualization and automated quality control routines are available in the Scripts.zip file (python).</p>
Data used in the Final Draft Scientific Basis Report Supplement in Support of Proposed Voluntary Agreements for the Sacramento River, Delta, and Tributaries Update to the San Francisco Bay/Sacramento-San Joaquin Delta Water Quality Control Plan
This dataset includes modeled data describing the potential benefits of the Voluntary Agreements (VAs) from the Final Draft Scientific Basis Report Supplement in Support of Proposed Voluntary Agreements for the Sacramento River, Delta, and Tributaries Update to the San Francisco Bay/Sacramento-San Joaquin Delta Water Quality Control Plan.
mRNA expression data of genes related to mitochondrial quality control in hepatopancreas of the two marine bivalves, Mytilus edulis and Crassostrea gigas, during short-term hypoxia/reoxygenation stress
<p>Coastal environments commonly experience strong oxygen fluctuations. Resulting hypoxia/reoxygenation stress can negatively affect mitochondrial functions, since oxygen deficiency impairs ATP generation, whereas a surge of oxygen causes mitochondrial damage by oxidative stress mechanisms. Marine intertidal bivalves are adapted to fluctuating oxygen conditions, yet the underlying molecular mechanisms that sustain mitochondrial integrity and function during oxygen fluctuations are not yet well understood. We used targeted mRNA expression analysis to determine the potential involvement of the mitochondrial quality control mechanisms in responses to short-term hypoxia (24 h at <0.01% O<sub>2</sub>) and subsequent reoxygenation (1.5 h at 21% O<sub>2</sub>) in two hypoxia-tolerant marine bivalves, the Pacific oysters <em>Crassostrea gigas</em> and the blue mussels <em>Mytilus edulis</em>. To test these hypotheses, We focused on the transcript levels of the following marker genes: for mitochondrial fission and fusion - <em>mfn</em>2 (encoding mitofusin 2), <em>opa</em>1 (mitochondrial dynamin-like 120kDa protein), <em>dnm</em>1<em>l </em>(dynamin-1-like protein), <em>mff</em> (mitochondrial fission factor), <em>fis</em>1 (mitochondrial fission protein 1); for protein and DNA quality control - <em>tsfm</em> (encoding mitochondrial translation elongation factor Ts), <em>lonp</em>1 (mitochondrial Lon protease), <em>spg</em>7 (paraplegin), <em>oma</em>1 (mitochondrial metalloendopeptidase OMA1), <em>clpB</em> (mitochondrial caseinolytic matrix peptidase chaperone subunit B), <em>atp</em>23 (mitochondrial inner membrane protease ATP23), <em>twnk</em> (mitochondrial twinkle mtDNA helicase); and for mitophagy - <em>mieap</em> (encoding mitochondrial eating protein), <em>hyou</em>1 (hypoxia upregulated protein 1), <em>prkn</em> (parkin), <em>pink</em>1 (PTEN- induced kinase 1), and <em>pgam</em>5 (mitochondrial serine/threonine protein phosphatase PGAM5). The revealed species-specific differences in the expression of the mitochondrial quality control pathways shed light on the potentially important mechanisms of mitochondrial protection against H/R-induced damage that might contribute to hypoxia tolerance in marine bivalves. </p>
GECCO Industrial Challenge 2019 Dataset: A water quality dataset for the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition at the Genetic and Evolutionary Computation Conference 2019, Prague, Czech Republic.
<p>Dataset of the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 13th-17th 2019, Prague, Czech Republic</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>1. Original train dataset of water quality data provided to participants (identical to gecco2019_train_water_quality.csv)</p> <p>2. Call for Participation</p> <p>3. Rules and Description of the Challenge</p> <p>4. Resource Package provided to participants</p> <p>5. The complete dataset, consisting of train, test and validation merged together (gecco2019_all_water_quality.csv)</p> <p>6. The test dataset, which was used for creating the leaderboard on the server (gecco2019_test_water_quality.csv)</p> <p>7. The train dataset, which participants had available for training their models (gecco2019_train_water_quality.csv)</p> <p>8. The validation dataset, which was used for the end results for the challenge (gecco2019_valid_water_quality.csv)</p> <p> </p> <p>The challenge required the participants to submit a program for event detection. A training dataset was available to the participants (gecco2019_train_water_quality.csv). During the challenge the participants were able to upload a version of their program to out online platform, where this version was scored against the testing dataset (gecco2019_test_water_quality.csv), thus an intermediate leaderboard was available. To avoid overfitting against this dataset, at the end of the challenge, the end result was created from scoring with the validation dataset (gecco2019_valid_water_quality.csv). </p> <p>Train, Test, Validation dataset are from the same measuring station and are in chronological order. So the timestamps from the test dataset begin directly after the train timestamps, while the validation timestamps begin directly after the test timestamps. </p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, S. Moritz, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p> </p> <p>Internet of Things: Online Event Detection for Drinking Water Quality Control</p> <p> </p> <p>Description:</p> <p>For the 8th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2018 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.</p> <p><br> Competition Opens: End of January/Start of February 2019<br> Final Submission: 30 June 2019</p> <p>Official webpage:</p> <p><a href="https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php">https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php</a></p> <p> </p>
Dataset related to article "Phantom‑based analysis of variations in automatic exposure control across three mammography systems: implications for radiation dose and image quality in mammography, DBT, and CEM"
<p>The dataset comprises information from several DICOM tags extracted from digital mammography (DM), digital breast tomosynthesis (DBT), and contrast-enhanced mammography (CEM) images acquired in a phantom study aimed at characterizing the automatic exposure control (AEC) behavior of diverse mammography equipment. The final ten columns of the datasets encompass signal (mena pixel values, MPV) and noise (standard deviation, SD) measurements derived from phantom images. These measurements are used to compute several image quality metrics, including contrast, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), CNR relative difference in comparison to the 45 mm reference thickness, and a figure of merit (FOM) obtained by diving the squared CNR by the mean glandular dose (MGD).</p>
Dataset - FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control
<p>This dataset contains the data and model used in the paper</p> <blockquote> <p>Thomas Sanchez, Oscar Esteban, Yvan Gomez, Alexandre Pron, Mériam Koob, Vincent Dunet, Nadine Girard, Andras Jakab, Elisenda Eixarch, Guillaume Auzias, and Meritxell Bach Cuadra. "FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control." <a href="https://arxiv.org/abs/2311.04780"><em>arXiv preprint arXiv:2311.04780</em></a> (2023).</p> </blockquote> <p>If you found this dataset useful or used it in your research, please cite this reference.</p> <p>This dataset contains manual quality annotations and image quality metrics (IQMs) obtained from 1647 stacks of T2-weighted (T2w) slices of fetal brain magnetic resonance (MR) images collected from 233 subjects at four different institutions Lausanne University Hospital (CHUV) in Switzerland, BCNatal at Hospital Sant Joan de Déu in Barcelona (Spain), University Children's Hospital Zürich (KISPI) in Switzerland and La Timone University Hospital in Marseille, France. The data were acquired on scanners from different vendors (Siemens at CHUV, BCNatal and Marseille, General Electrics at KISPI), MR sequences (Half Fourier Single-shot Turbo spin-Echo –HASTE– for Siemens scanners and Single-Short Fast Spin Echo –SS-FSE– for GE scanners), magnetic field strengths (1.5 T and 3 T), image resolutions, fields of view, repetition times and echo times, with both neurotypical and pathological cases.</p> <p>These data and the derived IQMs were used to train and evaluate models for quality assessment and quality control of fetal brain MR images. The code to reproduce the experiments is available on <a href="https://github.com/Medical-Image-Analysis-Laboratory/fetal_brain_qc">GitHub.</a></p> <p>Each entry describe the information for a single stack of T2w slices. It contains information regarding which subject it belongs to, its manual quality rating, scanner-related information and 332 IQMs, starting at the `centroid` column in the file. Further description of the data is available in the materials and methods section of the <a href="https://arxiv.org/abs/2311.04780">paper</a>.</p> <p>The model is a 2D nnUNet [1] segmentation network trained on the super-resolution reconstructed data and manual segmentations available as part of the<a href="https://www.synapse.org/#!Synapse:syn25649159/wiki/610007"> Fetal Tissue Annotation Challenge</a> (FeTA).</p> <p>Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2023.</p> <p>[1] Isensee, Fabian, et al. "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation." <em>Nature methods</em> 18.2 (2021): 203-211.</p>
Long reads training material for 'Quality Control' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial for reads Quality Control.</p> <p>PacBio HiFi reads were provided by PacBio - GIAB sample HG002 (https://www.pacb.com/smrt-science/smrt-resources/datasets/) and was downsampled using seqtk (https://github.com/lh3/seqtk)</p> <p>Nanopore reads were provided by Tim Kahlke as part of "Long-Read, long reach Bioinformatics Tutorials" (https://timkahlke.github.io/LongRead_tutorials/) and was basecalled using Guppy v5.0.2 (dna_r9.4.1_450bps_sup.cfg).</p>
Data supplement to: Quality control of image sensors using gaseous tritium light sources
<p>In the article "Quality Control of Image Sensors using Gaseous Tritium Light Sources" (<a href="https://doi.org/10.1098/rsta.2021.0130)">https://doi.org/10.1098/rsta.2021.0130)</a> we propose a practical method for radiometrically calibrating cameras using widely available gaseous tritium light sources (<em>betalights</em>). This dataset includes all the recorded data along with the scripts necessary to reproduce the results and figures.</p>
MR Gradient System Long-Term Stability Investigation and Protocol Optimization for Quality Control using Gradient Impulse Response Function (GIRF)
<p>The dataset of the abstract "MR Gradient System Long-Term Stability Investigation and Protocol Optimization for Quality Control using Gradient Impulse Response Function (GIRF)" for ISMRM 2022, London, UK. The data processing code with instructions could be found <a href="https://github.com/BRAIN-TO/girfISMRM2022">here</a>.</p> <p> </p> <p>Meas1.zip and Meas2.zip contain the first and the second measurements of the raw T2* decay signal acquired with the phantom-based method. Note that the coil dimension has been averaged to save data volume for demonstration purposes. This will lead to a lower SNR of the calculated output gradient and GIRF.</p> <p> </p> <p>CalculatedGIRF.zip provides the author's pre-calculated GIRFs using the data without coil averaging. This data is used for all the postprocessing (e.g. SNR and stability analysis, etc.) in the published abstract with the source code provided in the same Github repository.</p> <p> </p>
Quality control traceability during the packing and release process of Ceratitis capitata for Sterile Insect Technique
<p><strong>Abstract</strong></p> <p>In programs applying the Sterile Insect Technique (SIT), the quality of insects deployed in the field determines the success in preventing, suppressing, containing, or eradicating the pest population. In the fruit fly emergence and release facility (ERF) of the Moscamed Program in Mexico, irradiated pupae of <em>Ceratitis capitata</em> are packed in Mexico-type towers, and key adult quality parameters, such as emergence, fliers, and survival, are determined throughout the packing, handling and release process. However, different methodologies are used to estimate the percentage of fliers in the different stages of the process, raising doubts of whether observed differences are due to the effect of each stage or to the methodology used. With this in mind, we developed an alternative called “Adult Flier device” (= AF-device) to evaluate the adult flier parameter following a critical evaluation path of five steps: 1) upon arrival at ERF, 2) post-packing, 3) post-holding, 4) post-chilling, and 5) post-release, where adult fliers and survival under stress were evaluated. We also compared the current methodologies for the estimation of "absolute fliers" available in different operating manuals. Our results suggest that the AF-device allows reliable traceability of sterile insect quality parameters throughout the packing and release process, since no significant differences were observed with the control treatments. In the chilling stage, the five methodologies tested were equivalent, but the AF-device was less time-consuming and required less manpower and biological material than the other methodological options. Our results demonstrate that the use of the AF-device can be a feasible, versatile, innovative, and efficient alternative to evaluate quality control parameters throughout the process of packing and releasing sterile insects, providing reliable results in a timely manner with less hand labor using minimal biological material.</p>
Training material for Genome assembly quality control (Galaxy Training Network tutorial)
<p>This Zenodo repository includes the required datasets for following the GTN: Genome assembly quality control.</p>
Dataset for 'A deep learning-based quality control loop of the extrusion-based bioprinting process'
<p>Zip file containing the dataset for the paper 'A deep learning-based quality control loop of the extrusion-based bioprinting process' (available at <a href="https://ijb.whioce.com/index.php/int-j-bioprinting/article/view/620">https://ijb.whioce.com/index.php/int-j-bioprinting/article/view/620</a>). The dataset is composed of:</p> <ul> <li>A series of video folders, each having a unique ID, and containing the processed video frames.</li> <li>A master.csv file containing all the metadata for each video.</li> </ul> <p>The authors acknowledge the supported by the European Union’s Horizon 2020 research and innovation program under the project GIOTTO: “Giotto: Active ageing and osteoporosis: The next challenge for smart nanobiomaterials and 3D technologies,” grant agreement no. 814410.</p>
ScienceDex guides
Understand access before you commit
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.