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13,405 results for “targets”
Non-Targeted Screening of Organic Compounds in Environmental and Biological Matrices Related to Children's Environmental Exposure in South Florida, 2022-2024
This dataset provides a comprehensive list of chemicals relevant to children’s exposure from both dietary and non-dietary sources, across five environmental and biological matrices: drinking water (n = 206), food (n = 203), urine (n = 183), soil (n = 178), and household dust (n = 164). Samples were collected between May 2022 and June 2024 in Miami-Dade and Broward counties, Florida. A non-targeted screening approach using high-resolution mass spectrometry (HRMS) coupled with liquid chromatography was employed for analysis, with matrix-specific preparation methods: online solid-phase extraction (SPE) for water and urine, QuEChERS for food, and accelerated solvent extraction (ASE) for soil and dust. Analyses were conducted in full-scan mode under both positive and negative electrospray ionization to maximize compound detection coverage. Compound identification was performed using Compound Discoverer software, incorporating spectral and structural databases such as mzCloud, ChemSpider, ClassyFire, and MassList. Annotations were based on exact mass, mass error threshold (<5ppm), predicted molecular formula, retention time alignment, isotopic pattern fit, MS/MS spectral similarity, and match confidence levels derived from integrated spectral libraries and database scoring algorithms. Quality assurance was maintained through the use of quality control (QC) samples across all matrices and analytical batches. The integration of non-targeted analysis, matrix-optimized extraction, and rigorous QA/QC practices makes this dataset a valuable resource for environmental exposomics, chemical risk assessment, and evidence-based public health policy development.
PsPM-RRM1-2: SCR, ECG, respiration and eye tracker measurements in response to electric stimulation or visual targets
<p>This dataset includes skin conductance response (SCR), electrocardiogram (ECG), respiration and eye tracker (including pupillometry) measurements for each of 29 healthy unmedicated participants (7 males and 22 females aged 23.5 +/- 3.6 years) in response to 10 discomforting electric stimulations to the forearm (RRM1) or 10 visual targets in a visual detection task (RRM2). The sample partly overlaps with data set <a href="https://doi.org/10.5281/zenodo.1292568">PsPM-FR</a>. Some participants did not take part in RRM1 or RRM2 such that there are 25 recordings for RRM1 and 26 recordings for RRM2. Electric shock stimuli are 0.2 ms wide square current pulse repeated at 500 Hz for 500 ms and individually adjusted amplitude just below the pain threshold. Visual stimuli are red crosses (+) embedded in a white digit stream; each stimulus is presented during 200 ms and separated by a 800 ms blank interval. ITI is selected randomly on each trial from 40 s, 45 s or 50 s. A baseline period with distractors but no targets concludes experiment RRM2. (This is in contrast to the methods description in Bach et al. (2016), according to which the baseline period was randomly either in the beginning or at the end of the experiment. This discrepancy was caused by an error in the code that controlled the experiment presentation.)</p>
Updated DEVOTES indicator catalogue of MSFD indicator systems targeting descriptors D1, D2, D4, and D6
<p>This is version 8 of the Catalogue of Indicators of the FP7 project DEVOTES (grant number 308392) that aims at supporting the implementation of the EU MSFD. This catalogue of indicators is an inventory of existing methods. The metadata have been updated and extended since deiverable D3-1 of the DEVOTES project. You can learn more about the DEVOTES project at: http://www.devotes-project.eu</p> <p>All data are provided without a guarantee of correctness or completeness. We are aware of some errors in the database content and are continuously working on correcting these and on supplementing the content with new metadata.</p> <p>The data can be best viewed with the free DEVOTool software, available at http://www.devotes-project.eu/devotool. By using the DEVOTool software, you accept the license conditions as outlined in section 6 of the software manual distributed together with DEVOTool. </p>
S3 | NORMANCT15 | NORMAN Collaborative Trial Targets and Suspects
<p>This is the collection associated with list S3 NORMANCT15 on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S3 | NORMANCT15 | <strong>NORMAN Collaborative Trial Targets and Suspects</strong></p> <p>Schymanski <em>et al</em>. 2015.<br>DOI: <a href="http://link.springer.com/article/10.1007/s00216-015-8681-7">10.1007/s00216-015-8681-7</a></p> <p>Upload 22/3/2020: added merged InChIKey file for PubChem data extraction. 27/6/2025: added merged CSV</p>
Dataset / Code: Targeted protein degradation in mycobacteria uncovers antibacterial effects and potentiates antibiotic efficacy
<p><strong>Targeted protein degradation in mycobacteria uncovers antibacterial effects and potentiates antibiotic efficacy</strong></p> <p><strong> </strong></p> <p>Harim I. Won<sup>1,#</sup>, Samuel Zinga<sup>1,#</sup>, Olga Kandror<sup>1</sup>, Tatos Akopian<sup>1</sup>, Ian D. Wolf<sup>1</sup>, Jessica T.P. Schweber<sup>1</sup>, Ernst W. Schmid<sup>2</sup>, Michael C. Chao<sup>1</sup>, Maya Waldor<sup>1</sup>, Eric J. Rubin<sup>1,*</sup>, Junhao Zhu<sup>1,3,*</sup></p> <p><strong> </strong></p> <p><sup>1</sup>Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA.</p> <p><sup>2</sup>Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Blavatnik Institute, Boston, Massachusetts 02115, USA.</p> <p><sup>3</sup>CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.</p> <p><sup>#</sup>These authors contributed equally to this work.</p> <p>*Corresponding authors: <a href="mailto:zhujh@im.ac.cn">zhujh@im.ac.cn</a> (J.Z.), <a href="mailto:erubin@hsph.harvard.edu">erubin@hsph.harvard.edu</a> (E. J. R.)</p> <p><strong> </strong></p> <p><strong>Abstract</strong></p> <p>Proteolysis-targeting chimeras (PROTACs) represent a new therapeutic modality involving selectively directing disease-causing proteins for degradation through proteolytic systems. Our ability to exploit targeted protein degradation (TPD) for antibiotic development remains nascent due to our limited understanding of which bacterial proteins are amenable to a TPD strategy. Here, we use a genetic system to model chemically-induced proximity and degradation to screen essential proteins in <em>Mycobacterium smegmatis </em>(<em>Msm</em>)<em>, </em>a model for the human pathogen <em>M. tuberculosis </em>(<em>Mtb</em>). By integrating experimental screening of 72 protein candidates and machine learning, we find that drug-induced proximity to the bacterial ClpC1P1P2 proteolytic complex leads to the degradation of many endogenous proteins, especially those with disordered termini. Additionally, TPD of essential <em>Msm </em>proteins inhibits bacterial growth and potentiates the effects of existing antimicrobial compounds. Together, our results provide biological principles to select and evaluate attractive targets for future <em>Mtb</em> PROTAC development, as both standalone antibiotics and potentiators of existing antibiotic efficacy.</p> <p> </p>
S21 | UATHTARGETS | University of Athens Target List
<p>This is the collection associated with list S21 UATHTARGETS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S21 | UATHTARGETS | <strong>University of Athens Target List </strong></p> <p>Update 22/3/2020: added InChIKey file. Update 8/2/2022: new files from Dec 2021 with new compounds, NORMAN ID, classification and comments (provided by Maristina Nika). (v0.2.1 - attempted fix of CSV headers; v0.2.2 many small fixes flagged via PubChem deposit)</p> <p>Additional grant acknowledgement: Aristeia-Excellence: Transformation products of emerging pollutants in the aquatic environment (TREMEPOL project), 2012-2015, European Social Fund-Ministry of Education, <a href="http://tremepol.chem.uoa.gr/">http://tremepol.chem.uoa.gr/</a></p>
Extended Kalman filters for close-range navigation to noncooperative targets
<p>The data sets provided here are associated to the paper “Extended Kalman filters for close-range navigation to noncooperative targets” available at this <a name="_Hlk36545040"></a><a href="https://doi.org/10.1016/j.asr.2023.10.038"><span>link</span></a>. These allow recreating the simulations discussed in Sections 5.2 – for the results plotted in Figure 7 – 5.3 (Figures 9-10), and 5.4 (Figures 11-12).</p> <p>That paper presents a set of dynamic filters for estimating the relative roto-translational state and the main parameters of a noncooperative target from an observing chaser satellite during close proximity operations. The proposed different options address a wide range of design possibilities for the architecture of the relative navigation system. All filters are derived from a common, general, core shaped as a dynamic multiplicative extended Kalman filter using dual quaternions. This allows exploiting the advantages of handling the pose (i.e., attitude and position) in a multiplicative fashion, while improving the accuracy in the estimation of the angular and linear relative velocities, as well as enabling the estimation of some meaningful parameters of the target spacecraft (e.g., the ratios of the moments of inertia, position and orientation of the principal axes frame). Moreover, by adopting relative kinematics and dynamics equations in dual quaternions, the inherent coupling of the six degrees-of-freedom motion is addressed with no approximations.</p> <p>All filters take as observations only the noisy pose measurements from an electro-optical device. For each proposed formulation, numerical simulations are carried out to show the behaviour of the filter within a scenario representative of close-range target inspection at conclusion of the mid-range rendezvous.</p>
Dataset for paper "Target selection for Near-Earth Asteroids in-orbit sample collection missions"
<p>This dataset can be used to reproduce the results of the paper titled "Target selection for Near-Earth Asteroids in-orbit sample collection missions."</p> <p>The "results" folder contains the data to reproduce the maps and the rankings of the target asteroids.</p> <p>The "trajectories" folder contains the propagation of the sample trajectories used to obtain the grids.</p>
Patterns of Speciation in a Parapatric Pair of Saturnia Moths as Revealed by Target Capture
<p>This is the dataset for the manuscript entitled Patterns of Speciation in a Parapatric Pair of Saturnia Moths as Revealed by Target Capture. This study helps in the delimitation of a parapatric pair of two species of moths in a complex distribution considering their evolutionary history with the help of the Target Capture method.</p>
Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention
<p>Single cell RNA seq datasets used for analysis in the Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention</p>
Diffraction images used to solve the structures published in the article "Structure of human endo-α-1,2-mannosidase (MANEA), an antiviral host-glycosylation target"
<p>Raw diffraction images used for generating the structures published in the article "Structure of human endo-α-1,2-mannosidase (MANEA), an antiviral host-glycosylation target" (available <a href="https://doi.org/10.1073/pnas.2013620117">here</a>). Full single-crystal datasets, including images that were not used in the final analyses, are published. The software used for the processing of each dataset is listed in their respective PDB entries. Datasets 6ZJ1 and 6ZJ5 were cut anisotropically using STARANISO, other datasets were processed isotropically.</p> <p> </p> <p>If you find this useful, please contact me at <a href="mailto:lukasz.sobala@hirszfeld.pl">lukasz.sobala@hirszfeld.pl</a>, I am just interested in how these data are used!</p>
Data for SciKit-SurgeryFRED publication "Are fiducial registration error and target registration error correlated? SciKit-SurgeryFRED for teaching and research."
<p>This is data used in the publication;</p> <p><a href="https://www.spiedigitallibrary.org/profile/Steve.Thompson-90188">Stephen Thompson</a>, <a href="https://www.spiedigitallibrary.org/profile/Thomas.Dowrick-4289932">Tom Dowrick</a>, <a href="https://www.spiedigitallibrary.org/profile/Mian.Ahmad-4289934">Mian Ahmad</a>, <a href="https://www.spiedigitallibrary.org/profile/Jeremy.Opie-4314392">Jeremy Opie</a>, and <a href="https://www.spiedigitallibrary.org/profile/notfound?author=Matthew_Clarkson">Matthew J. Clarkson</a> "Are fiducial registration error and target registration error correlated? SciKit-SurgeryFRED for teaching and research", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 115980U (15 February 2021); <a href="https://doi.org/10.1117/12.2580159">https://doi.org/10.1117/12.2580159</a></p> <p>Data in summerSchoolGameLogs was collected using scikit-surgeryfred: v0.0.3 summer school 2020 (2020). DOI 10.5281/zenodo.3946090</p> <p>Data in in registration_results was collected using scikit-surgeryfred: v0.0.8 browser based user interface (2020). DOI 10.5281/ zenodo.4314971</p> <p>Each directory contains Python scripts to analyse the data as described in the above paper.</p> <p> </p>
mzrtsim: Raw Data Simulation for Reproducible Gas/Liquid Chromatography–Mass Spectrometry Based Non-targeted Metabolomics Data Analysis
<p>All the data for 'mzrtsim: Raw Data Simulation for Reproducible Gas/Liquid Chromatography–Mass Spectrometry Based Non-targeted Metabolomics Data Analysis'</p> <p>sim.zip is stimulated data for intensity cutoff 0.05. simxcms.csv is peak intensity profiles for their simulated peaks.</p> <p>sim3.zip are simulated data for normal/leading/tailing peaks with tailing factor of 1, 0.8, and 1.5, respectively.</p> <p>All the csv files begin with sim3 are extracted peaks list from the sim3.zip with corresponding data analysis software.</p> <p>csv.zip recorded the m/z, retention time, intensity, and compounds name for simulated compound for each condition (sim.zip and sim3.zip).</p> <p>sep1.mzML: simulation for 8 isomers with similar m/z while different retention times. 7 peaks are non baseline separation peaks. Peaks profile is saved in spe1.csv file.</p> <p>xcms.csv, mzmine.csv, openms.csv: peaks found in sep1.mzML by xcms, mzmine 4.5 and openms, respectively.</p> <p>R code: <a href="https://github.com/yufree/democode/blob/master/meta/simfin.R">https://github.com/yufree/democode/blob/master/meta/simfin.R</a></p> <p>Website of mzrtsim package: https://yufree.github.io/mzrtsim/</p>
Targeted and untargeted LC-MS copepodamide datasets for marine and freshwater copepods
<p>This repository contains the datasets, analysis code and output generated and used in the scientific article titled "Mass spectroscopy reveals compositional differences in copepodamides from limnic and marine copepods" published in Scientific Reports (https://doi.org/10.1038/s41598-024-53247-1)<em>.</em></p> <p>Detailed information about the datasets are available in the README.txt.</p> <p>The source dataset created from the sampling effort, with targeted liquid chromatography coupled mass spectrometry (LC-MS) data, taxonomic information of individual copepods, their length measurements, estimated biomass etc is available in <em><strong>Masterfile_targeted_data_final.xlsx</strong></em>.</p> <p>The source dataset for precursor LC-MS scan data is available in <em><strong>Precursor_data_Deisotoped.xlsx</strong></em>. </p> <p>The resulting analysis data frames (last sheet in each .xlsx file) are available as separate .csv files (<strong>Arnoldt_targeted_analysis_data.csv</strong> & <strong>Arnoldt_targeted_analysis_data.xlsx</strong>). These files are denoted "Supplementary Data. 2" and "Supplementary Data. 1" respectively in the main article. Data files <strong>Chromatography.csv</strong> and <strong>zooplankton_composition_bulk.csv</strong> are used to create chromatograph line plots (Figures 3a & 3b in the article) and one of the supplementary figures (S1), respectively.</p> <p>A R-markdown file (<strong>Arnoldt_R_Code</strong><em><strong>.Rmd</strong></em>) with the code to analyse and visualise all data, and its html-output file (<strong>Arnoldt_R_Code_Output</strong><em><strong>.html</strong></em>) are also available here. The markdown files uses the four csv-files described in the paragraph above to generate all analyses and figures. The output (.html) file is denoted "Supplementary Code" in the main article.</p>
S65 | UATHTARGETSGC | University of Athens GC-APCI-HRMS Target List
<p>This is the collection associated with list S65 UATHTARGETSGC - University of Athens GC-APCI-HRMS Target List on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>GC-APCI-HRMS target list of University of Athens. Provided by the research group of Prof. Nikolaos Thomaidis (<a href="http://trams.chem.uoa.gr/">http://trams.chem.uoa.gr/</a>) and hosted on the NORMAN Suspect List Exchange (<a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a>). DOI: 10.5281/zenodo.3753372.</p> <p>Updated Feb 2024 following feedback from Peter Oswald, EI.</p>
Organic Matter, Geochemical, Visible Spectrocolorimetric Properties, Radiocesium Properties, and Grain Size of Potential Source Material, Target Sediment Core Layers and Laboratory Mixtures for Conducting Sediment Fingerprinting Approaches in the Mano Dam Reservoir (Hayama Lake) Catchment, Fukushima Prefecture, Japan
<p>The current dataset was compiled to study sediment fingerprintings practices, i.e tracer selection and contribution modelling. Organic matter, elemental geochemistry, visible difuse spectrocolorimetric properties, radiocesium properties, and grain size were analysed were analysed in potential source material that may supply sediment to coastal rivers, here the upper part of the Mano river, draining the main Fukushima radioactive pollution plume (Japan). Four potential soil source materials (<em>n</em> = 68) were considered: undecontaminated cropland (<em>n</em> = 24), as non-decontaminated soil before the application of local decontamination policies, remediated cropland (<em>n</em> = 10), as decontaminated soil after the application of local decontamination policies, forest soils (n = 24) and subsurface material originating from channel bank collapse or landslides (<em>n</em> = 10; referred to as subsoil). A sediment core was collected in the Mano Dam lake (Hayama lake) on the 6th June 2021 and was sectionned into 1-cm layers (<em>n</em> = 38). Laboratory mixtures (<em>n</em> = 27) were made to assess different contribution levels from the sources.</p> <p>The current dataset comprises four .csv files including data and metadata information and their respective descriptions of variables. The data set is composed of soil samples, sediment core layer and laboratory mixtures. Laboratory mixtures were prepared to provide a dataset to calibrate/validate un-mixing models implemented to address this research question and analysed in the same conditions and using the same equipment as the source/target material.</p> <p>Recommended encoding format: <strong>latin1</strong></p>
Simulated EIT circular targets with noise and blur
<p>Companion data for the paper "Post-processing electrical impedance tomography reconstructions with incomplete data using convolutional neural networks".</p> <p>See <a href="https://github.com/robert-abc/KTC2023-ABC1">https://github.com/robert-abc/KTC2023-ABC1</a> for more information. </p> <p>Files:</p> <ul> <li>CNN_input.mat: CNN input, the noisy data</li> <li>CNN_output.mat: CNN output, the clean data</li> <li><em>cnn_training.py: </em>Describes the CNN training using Keras</li> <li><em>CNN_training.ipynb: </em>Notebook after the CNN training</li> <li><em>ultimate_cnn1.h5: </em>CNN (keras) file after training</li> </ul> <p>We uploaded the files to Google Drive and executed the code using Google Colab.</p> <p>Before executing the code, one should change the current working directory to the folder where the files are.</p>
European Mean Target Achievement (MTA) Indicator
<p>The EU MTA indicator is one way of assessing the benefits of protected area expansions using information of covered species distributions. It can be interpreted as the average number of species or habitats that are adequately (indicated through a target) conserved by conservation areas (Natura 2000 and/or CDDA sites) within the European union.</p> <p>This repository contains the created MTA indicator values created from an intersection of the Natura2000 and CDDA databases with the sensitive Article 12 and 17 reporting data.</p> <p><strong>Filename explanation: </strong></p> <p>MTA_{target}_{scale}_{directive}_{biodiversity}_{version}.csv</p> <table> <tbody> <tr> <td>target</td> <td>Which target was used for calculations. Currently: 'loglinear'</td> </tr> <tr> <td>scale</td> <td>Which datasets from the reporting was used. Supported currently are EU and MS</td> </tr> <tr> <td>directive</td> <td>Subset either for 'All' species and habitats, or just 'Art12' or 'Art17' data</td> </tr> <tr> <td>biodiversity</td> <td>Subset either for 'All', 'species' or 'habitats' respectively</td> </tr> <tr> <td>version</td> <td>A unique version number for each upload identical with the Zotero version.</td> </tr> </tbody> </table> <p> </p> <p>For further information and a detailed factsheet can be found on the online dashboard here:<br><a title="Online dashboard" href="https://martin-jung.github.io/EUMTA/dashboard.html">https://martin-jung.github.io/EUMTA/dashboard.html</a> </p> <p>---</p> <p>* The MTA indicator calculation and the creation of this dashboard contributes to WP7 of the NaturaConnect project. The information here is provided free of charge and the project takes no responsibility for errors or misuse.</p>
Data from 666 earthquake/tsunami scenario simulations targeting Nankai subduction
<p><strong>Summary: </strong></p> <ul> <li>Data from 666 earthquake/tsunami scenario simulations targeting Nankai subduction</li> <li>Tsunami simulation solver: TUNAMI-N2</li> <li>Fault rupture model: Okada model(Okada, 1985)</li> <li>Each scenario data consists of 71 ASCII files containing the simulated wave sequences at the synthetic gauges </li> <li>Each single scenario is tagged as "Nankai-XYZE", where XYZW would be the number from <strong>0003</strong> to <strong>1470</strong>.</li> <li>Some of the synthetic gauges are located based on the real ocean gauge points (e.g., DONET2, NOPHAS) nearby Shikoku region, Japan.</li> </ul> <p> </p> <p><strong>Details of each file:</strong></p> <ul> <li><strong>wave_sequenses.tar.gz</strong>: A series of wave sequences (4-hour wave history-data, recorded every 5 seconds) at 71 synthetic gauges are provided in ascii type format. <br> Note that <strong>5.8GB additional storage would be required</strong> to fully unzip this archive file with the following command. <pre><code>tar xzvf wave_sequences.tar.gz</code></pre> <p>You can get all scenario data as follows:</p> <pre><code>wave_sequences ├ Nankai-0003 ├ Nankai-0004 ├… ├ Nankai-1467 └ Nankai-1470 </code></pre> <p>Each directory contains the 71 files, head with 'pntX_Y.asc',</p> <pre><code>Nankai-0003 ├ pnt1_01.asc ├ pnt1_02.asc ├... ├ pnt5_46.asc └ pnt5_47.asc</code></pre> <p>which contains the wave sequence data. The ASCII file, the time (minute) elapsed from the fault rupture is aligned in the left column, and the wave displacements \eta (meter) from the original surface location are in the right column.</p> </li> </ul> <ul> <li> <p><strong>quake_params.csv</strong>: The parameter used to generate 666 earthquake scenarios caused by the rupture of rectangular fault, by means of Okada model. (Okada 1985)</p> </li> </ul> <ul> <li><strong>synthetic_gauges.csv</strong>: The locations of 71 synthetic gauges. </li> </ul>
Dataset Comparison of MRI-based automated segmentation methods and functional neurosurgery targeting with direct visualization of the Ventro-intermediate thalamic nucleus at 7T
<p>Scientific Reports - Nature - DOI : 10.1038/s41598-018-37825-8</p> <p>##################################<br> "Comparison of MRI-based automated segmentation methods and functional neurosurgery targeting with direct visualization of the Ventro-intermediate thalamic nucleus at 7T"<br> ##################################</p> <p>E. Najdenovska*, C. Tuleasca*, J. Jorge, P. Maeder, J.P. Marques, T. Roine, D. Gallichan, J.-P. Thiran, M. Levivier, and M. Bach Cuadra</p> <p>*Equally contributed authors</p> <p><br> Copyright (c) - All rights reserved. University of Lausanne. 2018.</p> <p><br> To reproduce the analyses presented in the referred study, in this repository you could find the MR images acquired from nine young healthy subjects (YS1-YS5), four elderly healthy subject (ES1-ES4) and two drug-resistant tremor patients treated treated with Vim radiosurgery by Gamma Knife (P1 and P2).</p> <p>The provided dataset includes the following NifTI files:</p> <p>- MPRRAGE @3T<br> - DWI @3T (together with the corresponding bvals and bvecs)<br> - MP2RAGE @7T<br> - SWI @7T<br> - binary masks of the manual delineation of both left and right Vim respectively that were done on the SWI (as NifTI files as well).</p> <p>Additionally, for the young cohort (YS1-YS5) we include as well the images used for building the quadrilateral of Guiot:<br> - T2-w @3T<br> - T2 CISS @3T</p> <p>For the patients (P1 and P2), a follow-up MPRAGE (acquired at 3T) with Gadolinium enhancement is also provided.</p> <p>——————————————<br> Notes:<br> 1. For YS3 MP2RAGE at 7T is missing, instead MPRAGE at 3T was used</p> <p>2. The code performing the thalamic nuclei clustering could be found in Zenodo (DOI: 10.5281/zenodo.123768)</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.