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764 results for “Reproducibility”
Data from: The cacao pathogen Moniliophthora roreri (Marasmiaceae) possesses biallelic A and B mating loci but reproduces clonally
The cacao pathogen Moniliophthora roreri belongs to the mushroom-forming family Marasmiaceae, but it has never been observed to produce a fruiting body, which calls to question its capacity for sexual reproduction. In this study, we identified potential A (HD1 and HD2) and B (pheromone precursors and pheromone receptors) mating genes in M. roreri. A PCR-based method was subsequently devised to determine the mating type for a set of 47 isolates from across the geographic range of the fungus. We developed and generated an 11-marker microsatellite set and conducted association and linkage disequilibrium (standardized index of association, IAs) analyses. We also performed an ancestral reconstruction analysis to show that the ancestor of M. roreri is predicted to be heterothallic and tetrapolar, which together with sliding window analyses support that the A and B mating loci are likely unlinked and follow a tetrapolar organization within the genome. The A locus is composed of a pair of HD1 and HD2 genes, whereas the B locus consists of a paired pheromone precursor, Mr_Ph4, and receptor, STE3_Mr4. Two A and B alleles but only two mating types were identified. Association analyses divided isolates into two well-defined genetically distinct groups that correlate with their mating type; IAs values show high linkage disequilibrium as is expected in clonal reproduction. Interestingly, both mating types were found in South American isolates but only one mating type was found in Central American isolates, supporting a prior hypothesis of clonal dissemination throughout Central America after a single or very few introductions of the fungus from South America.
Data from: Evaluating population receptive field estimation frameworks in terms of robustness and reproducibility
Within vision research retinotopic mapping and the more general receptive field estimation approach constitute not only an active field of research in itself but also underlie a plethora of interesting applications. This necessitates not only good estimation of population receptive fields (pRFs) but also that these receptive fields are consistent across time rather than dynamically changing. It is therefore of interest to maximize the accuracy with which population receptive fields can be estimated in a functional magnetic resonance imaging (fMRI) setting. This, in turn, requires an adequate estimation framework providing the data for population receptive field mapping. More specifically, adequate decisions with regard to stimulus choice and mode of presentation need to be made. Additionally, it needs to be evaluated whether the stimulation protocol should entail mean luminance periods and whether it is advantageous to average the blood oxygenation level dependent (BOLD) signal across stimulus cycles or not. By systematically studying the effects of these decisions on pRF estimates in an empirical as well as simulation setting we come to the conclusion that a bar stimulus presented at random positions and interspersed with mean luminance periods is generally most favorable. Finally, using this optimal estimation framework we furthermore tested the assumption of temporal consistency of population receptive fields. We show that the estimation of pRFs from two temporally separated sessions leads to highly similar pRF parameters.
Data from: Combining micro-volume isotope analysis and numerical simulation to reproduce fish migration history
1. Tracking the movement of migratory fish is of great importance for efficient conservation, although this has been technically difficult to achieve in small fish to which artificial tags cannot be attached. 2. We show that migration history can be reproduced by combining high-resolution otolith stable oxygen isotope ratio (δ18O) analysis and numerical simulation. 3. High-precision micro-milling and micro-volume carbonate analysing systems had the remarkable capability of extracting the otolith δ18O profiles with 10–30 days resolution. Furthermore, reasonable movements were reproduced by searching the routes consistent with the otolith δ18O profile, using an individual-based model with random swimming behaviour. 4. This method will be a valuable alternative to tagging and electronic loggers for revealing migration routes in early life stages, thereby providing crucial information to understand population structures and the environmental cause of recruitment variabilities, and to validate and improve fish movement models.
Data from: Thelomma ocellatum, a range extension to the Yukon Territory and case study in the use of molecular data to recognize asexually reproducing crustose lichens
An unusual sterile, asexually reproducing crustose lichen was encountered during fieldwork in the Yukon Territory of Canada. Genus and family level placement of the taxon were precluded by a lack of both sexual characters and any non-sexual characters that would have suggested an unambiguous generic affiliation. Examination of mtSSU and nrITS sequence data of the taxon revealed it to be a member of the Caliciaceae, with a sister relationship to the genus Tholurna. Subsequent molecular phylogenetic analyses of mtSSU sequence data suggested conspecificity with Thelomma ocellatum, one of the few calicioid lichens that reproduce asexually via lichenized diaspores. Comparison of the material from the Yukon with reference specimens and descriptions of T. ocellatum confirmed the identification of these populations, which extend the known distribution of T. ocellatum considerably northward in North America.
Reproducibility and robustness of graph measures of the Associative-Semantic Network
<p>Matfiles and matlab scripts used to study the reproducibility and robustness of graph measures of the Associative-Semantic Network.</p>
Dataset 1. Contains all the variables necessary to reproduce the results of Liebrenz et al.
<p>File formats:</p> <p>.xls: Excel file with variable names in 1. row and variable labels in 2. row</p> <p>.xpt/.xpf: SAS XPORT data file (.xpt) and value labels (formats.xpf).</p> <p>Note that the following variables were renamed in the output file: sumcadhssb -> SUMCADHS, sumcwursk -> SUMCWURS, adhdnotest -> ADHDNOTE, subs_subnotob -> SUBS_SUB, and that the internally recorded dataset name was shortened to "Liebrenz" .dta: Stata 13 data file</p> <p> </p>
On Genomic Repeats and Reproducibility
<p>Supplementary Data Sets for Firtina et al (submitted)</p>
On Genomic Repeats and Reproducibility
<p>Supplementary Data Sets for Firtina et al (submitted)</p>
Reproducible in-silico omics analyses - Supplementary Figure 1 (DEPRECATED)
<p>Supplementary Figure 1. Kallisto Native Pipeline. The Kallisto native pipeline is written in bash and calls Kallisto to perform indexing of the transcriptome, RNA-seq pseudo-mapping and quantification and for Sleuth to perform differential expression analysis.</p>
Development of a flow chamber system for the reproducible in vitro analysis of biofilm formation on implant materials
<p>The data provided are the original data from the microscopic investigation of oral bacterial biofilms. Bacteria were stained with a life/dead staining. Viable cells are represented in red, non-viable cells are shown in green. The biofilms were gained by 50 stacks each with a CLSM. The biofilms were formed in a flow chamber system with a flow velocity of 100µL/min over 24-72 hours. The biofilms were grown on tintanium discs.</p>
Characterisation of core histone sequences and nuclear mobility using a reproducible research approach
<p>Dataset required to build the PhD thesis. Includes the public sequences that would otherwise be downloaded anew.</p>
Reproducible in-silico omics analyses - Supplementary Figure 1
<p>Supplementary Figure 1. Kallisto Native Pipeline. The Kallisto native pipeline is written in BASH and calls Kallisto to perform indexing of the transcriptome, RNA-seq pseudo-mapping and quantification and for Sleuth to perform differential expression analysis.</p>
Dataset to reproduce the figures in "Revisiting AMOC Transport Estimates from Observations and Models" in Geophysical Research Letters
<p>Reference level assumptions used to calculate the Atlantic meridional overturning circulation transports at the RAPID and MOVE observing arrays are revisited in an eddying ocean model. Observational transport calculation methods are complemented by several alternative approaches. At RAPID, the model transports from the observational method and the model truth (based on the actual model velocities) agree well in their mean and variability. There are substantial differences among the transport estimates obtained with various methods at the MOVE site. These differences result from relatively large and time-varying reference velocities at depth in the model, not supporting a level-of-no-motion. The methods that account for these reference velocities properly at MOVE produce transports that are in good agreement with the model truth. In contrast with the observational estimates, the model transport trends at MOVE and RAPID largely agree with each other on pentadal to multi-decadal time scales. The datasets listed here are output fields from the Meridional ovErTurning ciRculation diagnostIC (METRIC) package which enables consistent calculations of AMOC estimates at the MOVE and RAPID sections from observations and models. The METRIC package is available on GitHub at https://github.com/NCAR/metric. Citation for the code: Castruccio F. S., 2021: NCAR/metric: metric v0.1. doi/10.5281/zenodo.4708277 Citation for the method: Danabasoglu et al. (2021). Revisiting AMOC Transport Estimates from Observations and Models. Geophysical Research Letters.</p>
Data bundle for egon-data: A transparent and reproducible data processing pipeline for energy system modeling
<p><strong>egon-data</strong> provides a transparent and reproducible open data based data processing pipeline for generating data models suitable for energy system modeling. The data is customized for the requirements of the research project <strong>eGon</strong>. The research project aims to develop tools for an open and cross-sectoral planning of transmission and distribution grids. For further information please visit the eGon <a href="https://ego-n.org/">project website</a> or its <a href="https://github.com/openego/eGon-data">Github repository.</a></p> <p>egon-data retrieves and processes data from several different external input sources. As not all data dependencies can be downloaded automatically from external sources we provide a data bundle to be downloaded by egon-data.</p> <p>The following data sets are part of the available data bundle:</p> <ol> <li> <p><strong>climate_zones_germany</strong></p> <ul> <li> <p>Climate zones in Germany</p> </li> <li> <p>source: Own representation based on DWD TRY climate zones</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>cutouts</strong></p> <ul> <li> <p>Weather data from Europe in 2011. Source: ERA5</p> </li> </ul> </li> <li> <p><strong>demand_regio_backup</strong></p> <ul> <li> <p>Electricity and heat demands</p> </li> </ul> </li> <li> <p><strong>emobility</strong></p> <ul> <li> <p>Data on eMobility mit_trip_data:<br>motorized individual travel - individual trips of electric vehicles (EV) generated with a modified version of simBEV v0.1.3 (https://github.com/rl-institut/simbev/tree/1f87c716d14ccc4a658b8d2b01fd12b88a4334d5). simBEV generates driving profiles for BEVs and PHEVs based upon MID data (BMVI) per RegioStaR7 region type (BBSR).</p> </li> <li> <p>Reiner Lemoine Institut, June 2022</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>entsoe</strong></p> <ul> <li> <p> </p> </li> </ul> </li> <li> <p><strong>gas_data</strong></p> <ul> <li> <p>CH4 infrastructure</p> </li> <li> <p>Biogas demand</p> </li> <li> <p>CH4 demand</p> </li> <li> <p>Source: SciGRID_gas</p> </li> </ul> </li> <li> <p><strong>geothermal_potential</strong></p> <ul> <li> <p>Spatial distribution of deep geothermal potentials in Germany</p> </li> <li> <p>source: <a href="https://doi.org/10.3390/en11020332">Assessment and Public Reporting of Geothermal Resources in Germany: Review and Outlook</a></p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>household_electricity_demand_profiles</strong></p> <ul> <li> <p>Annual profiles in hourly resolution of electricity demand of private households for different household types (singles, couples, other) with varying number of elderly and children.<br>The profiles were created using a bottom-up load profile generator by Fraunhofer IEE developed in the Bachelor's thesis "Auswirkungen verschiedener Haushaltslastprofile auf PV-Batterie-Systeme" by Jonas Haack, Fachhochschule Flensburg, December 2012.<br>The columns are named as follows: "<HH_TYPE_PREFIX>a<PROFILE_ID>", e.g. P2a0000 is the first profile of a couple's household with 2 children. See publication below for the list of prefixes. Values are given in Wh.<br>A related conference paper can be obtained here: http://publica.fraunhofer.de/documents/N-374761.html</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>household_heat_demand_profiles</strong></p> <ul> <li> <p>Sample heat time series including hot water and space heating for single- and multi-familiy houses. The profiles were created using the loadprofile generator by Fraunhofer IEE developed in the Master's thesis "Synthesis of a heat and electrical load profile for single and multi-family houses used for subsequent performance tests of a multi-component energy system", Simon Ruben Drauz, RWTH Aachen University, March 2016</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>hydrogen_network</strong></p> <ul> <li> <p>Planned H2 infrastructure</p> </li> <li> <p>Forecast H2 demand</p> </li> <li> <p>Source: fnb-gas</p> </li> </ul> </li> <li> <p><strong>hydrogen_storage_potential_saltstructures</strong></p> <ul> <li> <p>The data are taken from figure 7.1 in Donadei, S., et al., (2020), p. 7-5..</p> </li> <li> <p>Source: Flach lagernde Salze, (c) BGR Hannover, 2021.<br>Datenquelle: InSpEE-Salzstrukturen, (c) BGR, Hannover, 2015. &<br>Donadei, S., Horváth, B., Horváth, P.-L., Keppliner, J., Schneider, G.-S., &<br>Zander-Schiebenhöfer, D. (2020). Teilprojekt Bewertungskriterien und<br>Potenzialabschätzung. BGR. Informationssystem Salz: Planungsgrundlagen,<br>Auswahlkriterien und Potenzialabschätzung für die Errichtung von Salzkavernen<br>zur Speicherung von Erneuerbaren Energien (Wasserstoff und Druckluft) –<br>Doppelsalinare und flach lagernde Salzschichten: InSpEE-DS. Sachbericht.<br>Hannover: BGR.</p> </li> <li> <p>License: The original data are licensed under the GeoNutzV, see <a href="https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf">https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf</a></p> </li> </ul> </li> <li> <p><strong>industrial_gas_demand</strong></p> </li> <li> <p><strong>industrial_sites</strong></p> <ul> <li> <p>Information about industrial sites with DSM-potential in Germany from a Master's thesis by Danielle Schmidt. The data set includes own information on the coordinates of every industrial site.</p> </li> <li> <p>source: Schmidt, Danielle. (2019). Supplementary material to the masters thesis: NUTS-3 Regionalization of Industrial Load Shifting Potential in Germany using a Time-Resolved Model [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3613767</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>mastr_geocoding</strong></p> </li> <li> <p><strong>nep2035_version2021</strong></p> <ul> <li> <p>Data extracted from the German grid development plan - power</p> </li> <li> <p>source: Netzentwicklungsplan Strom 2035 (2021), erster Entwurf | Übertragungsnetzbetreiber (M) CC-BY-4.0</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>pipeline_classification_gas</strong></p> <ul> <li> <p>Parameters for the classification of gas pipelines</p> </li> <li> <p>source: Single parameters extracted from <a href="https://www.econstor.eu/bitstream/10419/173388/1/1011162628.pdf">Electricity, Heat and Gas Sector Data for Modelling the German System</a></p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>pypsa_eur</strong></p> </li> <li> <p><strong>regions_dynamic_line_rating</strong></p> <ul> <li> <p>German regions suitable to model dynamic line rating</p> </li> <li> <p>source: Own representation based on <a href="https://www.transnetbw.de/files/pdf/netzentwicklung/netzplanungsgrundsaetze/UENB_PlGrS_Juli2020.pdf">Grundsätze für die Ausbauplanung des Deutschen Übertragungsnetze (2020)</a></p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>re_potential_areas</strong></p> <ul> <li> <p>Eligible areas for wind turbines and ground-mounted PV systems.</p> </li> <li> <p>Reiner Lemoine Institut, January 2022</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>wind_offshore_status2019</strong></p> <ul> <li> <p> </p> </li> </ul> </li> <li> <p><strong>WZ_definition</strong></p> <ul> <li> <p>Definitions of industrial and commercial branches</p> </li> <li> <p>source: <a href="https://www.destatis.de/static/DE/dokumente/klassifikation-wz-2008-3100100089004.pdf">Klassifikation der Wirtschaftszweige (WZ 2008)</a></p> </li> <li> <p>Extract from Terms of Use: © Statistisches Bundesamt, Wiesbaden 2008 Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellenangabe gestattet.</p> </li> </ul> </li> <li> <p><strong>zensus_households</strong><strong> </strong></p> <ul> <li> <p>Dataset describing the amount of people living by a certain types of family-types, age-classes,sex and size of household in Germany in state-resolution.</p> </li> <li> <p>source: Data retrieved from <a href="https://ergebnisse2011.zensus2022.de/datenbank/online">Zensus Datenbank</a> by performing these steps:</p> <ul> <li> <p>Search for: "1000A-2029"</p> </li> <li> <p>or choose topic: "Bevölkerung kompakt"</p> </li> <li> <p>Choose table code: "1000A-2029" with title "Personen: Alter (11 Altersklassen)/Geschlecht/Größe desprivaten Haushalts - Typ des privaten Haushalts (nach Familien/Lebensform)"</p> </li> <li> <p>Change setting "GEOLK1" to "Bundesländer (16)" higher resolution "Landkreise und kreisfreie Städte (412)" only accessible after registration.</p> </li> </ul> </li> <li> <p>Extract from Terms of Use: © Statistische Ämter des Bundes und der Länder 2021, Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellennachweis gestattet.</p> </li> </ul> </li> <li> <p><strong>zensus_population</strong></p> </li> <li> <p><strong>district_heating_shares_egon.csv</strong></p> </li> </ol>
Required data to regenerate simulated metagenomes and reproduce KO-identification results in these simulated metagenomes
Open the record for dataset details and reuse information.
Data associated with Cell Reports publication: Dura-Bernal, Griffith, et al. 2023, "Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics" (2/4)
<p>This dataset includes experimental data used to constrain and validate the model, and model simulation output data for the following Cell Reports publication: <a href="https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6">https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6</a></p><p>The source code for the associated A1 model and data analysis can be found here: <a href="https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data">https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data</a>.</p><p>All zip files should unzipped into a parent folder called /data inside the Github repository above.</p><p><strong>Important:</strong> Due to the Zenodo size limit, this dataset is split among 4 Zenodo uploads. This is upload <strong>2 out of 4</strong>. The other 3 uploads can be found at: </p><p>Upload 1/4: <a href="http://doi.org/10.5281/zenodo.10066993">http://doi.org/10.5281/zenodo.10066993</a> (https://zenodo.org/uploads/10066993)</p><p>Upload 3/4: <a href="http://doi.org/10.5281/zenodo.10071726">http://doi.org/10.5281/zenodo.10071726</a> (https://zenodo.org/uploads/10071726)</p><p>Upload 4/4: <a href="http://doi.org/10.5281/zenodo.10072277">http://doi.org/10.5281/zenodo.10072277</a> (https://zenodo.org/uploads/10072277)</p><p>For more information please contact: salvador.dura-bernal@downstate.edu </p>
Data associated with Cell Reports publication: Dura-Bernal, Griffith, et al. 2023, "Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics" (3/4)
<p>This dataset includes experimental data used to constrain and validate the model, and model simulation output data for the following Cell Reports publication: <a href="https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6">https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6</a></p><p>The source code for the associated A1 model and data analysis can be found here: <a href="https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data">https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data</a>.</p><p>All zip files should unzipped into a parent folder called /data inside the Github repository above.</p><p><strong>Important:</strong> Due to the Zenodo size limit, this dataset is split among 4 Zenodo uploads. This is upload <strong>3 out of 4</strong>. The other 3 uploads can be found at: </p><p>Upload 1/4: <a href="http://doi.org/10.5281/zenodo.10066993">http://doi.org/10.5281/zenodo.10066993</a> (https://zenodo.org/uploads/10066993)</p><p>Upload 2/4: <a href="http://doi.org/10.5281/zenodo.10069553">http://doi.org/10.5281/zenodo.10069553</a> (https://zenodo.org/uploads/10069553)</p><p>Upload 4/4: <a href="http://doi.org/10.5281/zenodo.10072277">http://doi.org/10.5281/zenodo.10072277</a> (https://zenodo.org/uploads/10072277)</p><p>For more information please contact: salvador.dura-bernal@downstate.edu </p>
Data associated with Cell Reports publication: Dura-Bernal, Griffith, et al. 2023, "Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics" (1/4)
<p>This dataset includes experimental data used to constrain and validate the model, and model simulation output data for the following Cell Reports publication: <a href="https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6">https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6</a></p><p>The source code for the associated A1 model and data analysis can be found here: <a href="https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data">https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data</a>.</p><p>All zip files should unzipped into a parent folder called /data inside the Github repository above.</p><p><strong>Important:</strong> Due to the Zenodo size limit, this dataset is split among 4 Zenodo uploads. This is upload <strong>1 out of 4</strong>. The other 3 uploads can be found at: </p><p>Upload 2/4: <a href="http://doi.org/10.5281/zenodo.10069553">http://doi.org/10.5281/zenodo.10069553</a> (https://zenodo.org/uploads/10069553)</p><p>Upload 3/4: <a href="http://doi.org/10.5281/zenodo.10071726">http://doi.org/10.5281/zenodo.10071726</a> (https://zenodo.org/uploads/10071726)</p><p>Upload 4/4: <a href="http://doi.org/10.5281/zenodo.10072277">http://doi.org/10.5281/zenodo.10072277</a> (https://zenodo.org/uploads/10072277)</p><p>For more information please contact: salvador.dura-bernal@downstate.edu </p>
Open and Reproducible Dataset of Bibliometric Indicators of Russia
<p>Version 1. Data collected on 03-04 November 2023.</p><p><i>by Ivan Sterligov, HSE University, </i><a href="mailto:ivan.sterligov@gmail.com"><i>ivan.sterligov@gmail.com</i></a></p><p>See readme.md for details</p><p>The dataset comes with python code that lets anyone collect and update all the data automatically, provided they set up a free access to Scopus API and conform to the rules of API usage set by Elsevier.</p><p>The dataset is a part of a broader academic project aimed at tracking dramatic changes in Russian publication output after 2022 and generally focuses on the period starting at 2018. This is an academic non-commercial research project and includes no Scopus metadata "as is", and no matadata on any particular paper, author or journal, only publication counts obtained using proprietary queries and journal lists constructed by its author. No citation metrics are used or published.</p><p>Dataset will be hopefully updated and new versions posted at Zotero. You can cite specific version DOI to be sure that you reference particular values. Also, the dataset as a whole has a common DOI which resolves to the newest version.</p>
CHBH 2D semi-LASER MRSI reproducibility dataset
<p>2D semi-LASER MRSI reproducibility dataset acquired from 8 healthy participants at the CHBH, Birmingham, UK. Product Siemens sequence was used with Prisma scanner on VE11C software release.</p><p>These data were used in the following publication:</p><p>Vella, O., Bagshaw, A. P. & Wilson, M. SLIPMAT: A pipeline for extracting tissue-specific spectral profiles from 1H MR spectroscopic imaging data. <i>Neuroimage</i> <strong>277</strong>, 120235 (2023).</p><p>Processing scripts:</p><p><a href="https://github.com/martin3141/slipmat_paper">https://github.com/martin3141/slipmat_paper</a></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.