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720 results for “Identification data”
Supplementary Data for "Identification of Neighborhood Hotspots via the Cumulative Hazard Index: Results from a Community-Partnered Low-cost Sensor Deployment"
<p>These are the underlying data sets needed to build the kriging maps and calculate dissemination block cumulative hazard indices described in the paper. There are three data sets:</p> <ol> <li><strong>"Sampling location names and coordinates.csv"</strong>: locations and IDs of the low-cost sensors and the regulatory monitoring stations used in this work.<strong> [NOTE: </strong>latitudes and longitudes for the sensor deployments have been intentionally rounded to protect the location of volunteer sensor hosts.]</li> <li><strong>"Dissemination Block Populations.csv"</strong>: These are the relevant dissemination blocks in the study domain and their associated populations. This information was originally extracted from: https://censusmapper.ca/#13/49.2430/-123.1252</li> <li><strong>"Daily average concentrations by site and pollutant.csv"</strong>: This contains the PM2.5, NO2 and O3 daily averages for the entire study period across all low-cost sensor sites and regulatory monitoring stations. Refer to "Sampling location names and coordinates.csv" to parse the labels in this data set.</li> </ol> <p>There is also a sample code in Python to construct the kriging maps provided in 2 formats. <strong>[NOTE: </strong>we have intentionally excluded uploading the exact data sets imported by this code; our original data contains exact locations of sensor host volunteers and thus cannot be shared.]</p> <ol> <li><strong>"Jain et al - GeoHealth - Kriging Script.ipynb"</strong>: A Jupyter notebook script to import the data, build kriging maps, calculate CHIs, and export the data.</li> <li><strong>" Jain et al - GeoHealth - Kriging Script.pdf"</strong>: A PDF export of the Jupyter notebook so that you can read the Python scripts even if you are not a Jupyter notebooks user.</li> </ol>
Blair et al. 2020: Machine learning identification of ground beetles (repackaging of occurrences published by the NEON Biorepository Data Portal)
Blair, J.; Weiser, M. D.; Kaspari, M.; Miller, M.; Siler, C.; Marshall, K. E. 2020. Robust and simplified machine learning identification of pitfall trap-collected ground beetles at the continental scale. Ecology and Evolution 10 (23): 13143-13153. https://doi.org/10.1002/ece3.6905 Additional NEON samples (not yet archived at the Biorepository) were used in this research: full list of occurrences used.
FIG. 3. — Navicordulia pascali n in The genus Navicordulia Machado & Costa, 1995 (Insecta, Odonata, Corduliidae s.str.): new species, identification key for males and data on ecology and distribution
FIG. 3. — Navicordulia pascali n. sp., holotype: A, S10 and anal appendages in dorsal view; B, part of S9, S10 and anal appendages in left lateral view. Scale bars: 1 mm.
FIG. 1. — Navicordulia pascali n in The genus Navicordulia Machado & Costa, 1995 (Insecta, Odonata, Corduliidae s.str.): new species, identification key for males and data on ecology and distribution
FIG. 1. — Navicordulia pascali n. sp., holotype: A, general habitus; B, head in dorsal view and part of thorax in right lateral view.Scale bars: A, 10 mm; B, 1 mm.
FIG. 6 in The genus Navicordulia Machado & Costa, 1995 (Insecta, Odonata, Corduliidae s.str.): new species, identification key for males and data on ecology and distribution
FIG. 6. — "Savane-roche" in the Barruol Mounts, locus typicus of Navicordulia pascali n. sp. Photo by Stéphane Brûlé.
Data used in "Fast metabolite identification with Input Output Kernel Regression"
<p>This repository contains the data used in [1] to evaluate the performance for metabolite identification from tandem mass spectra. These data have been extracted and processed in [2]. We used a subset of 4138 MS/MS spectra extracted from the GNPS public spectral library (https://gnps.ucsd.edu/ProteoSAFe/libraries.jsp) for training and evaluation. For searching, we used molecular structures from PubChem as candidate sets.</p> <p>Please mention and cite GNPS when using these data.</p> <p>The implementation of the method proposed in [1] is available on: https://version.aalto.fi/gitlab/kepaco/Fast-metabolite-identification-with-IOKR</p> <p><strong>Files description:</strong></p> <ul> <li><em>spectra.txt</em>: informations about the MS/MS spectra (GNPS identifier, compound name and INCHI identifier)</li> <li><em>data_GNPS.mat</em>: contains the molecular fingerprints, molecular formula and InCHI corresponding to the MS/MS spectra</li> <li><em>cv_ind.txt</em>: indices of the cross-validation folds</li> <li><em>ind_eval.txt</em>: indices of the examples used for evaluation</li> <li><em>candidates</em>: fingerprints and INCHI for the different candidate sets</li> <li><em>input_kernels</em>: contains 24 input kernel matrices</li> </ul> <p><strong>References:</strong></p> <p>[1] Brouard, C., Shen, H., Dührkop, K., d'Alché-Buc, F., Böcker, S. and Rousu, J.: Fast metabolite identification with Input Output Kernel Regression. In the proceedings of ISMB 2016, Bioinformatics 32(12): i28-i36, 2016. DOI: https://doi.org/10.1093/bioinformatics/btw246</p> <p>[2] Dührkop, K., Shen, H., Meusel, M., Rousu, J. and Böcker, S.: Searching molecular structure databases with tandem mass spectra using CSI:FingerID. PNAS, 112(41), 12580-12585, 2015. doi:10.1073/pnas.1509788112</p> <p> </p> <p> </p>
T-DNAreader: fast and precise identification of T-DNA insertion sites using RNA-seq data
<p><em><span>Agrobacterium</span></em><span>-mediated plant transformation, which enables the delivery of DNA using transfer DNA (T-DNA) binary vectors, is an essential technique in plant research. T-DNAs randomly integrate into the host genome and multiple T-DNAs can integrate in a single transformation event, necessitating the development of efficient, reliable tools to identify the T-DNA insertion sites (TISs). Here, we developed T-DNAreader to identify TISs from RNA-sequencing (RNA-seq) data with high precision, sensitivity, and speed, outperforming existing tools. T-DNAreader detected previously unknown TISs in characterized mutant plants. Overall, T-DNAreader enables the simple identification of TISs within transcribed regions and standardizes the characterization of T-DNA-containing transgenic plants.</span></p>
Data from: harnessing the power of regional baselines for broad-scale genetic stock identification: a multistage, integrated, and cost-effective approach
<p>In mixed-stock fishery analyses, genetic stock identification (GSI) estimates the contribution of each population to a mixture and is typically conducted at a regional scale using genetic baselines specific to the stocks expected in that region. Often these regional baselines cannot be combined to produce broader geographical baselines due to non-overlapping populations and genetic markers. In cases where the mixture contains stocks spanning across a wide area, a broad-scale baseline is created, but often at the cost of resolution. Here, we introduce a new GSI method to harness the resolution capabilities of baselines developed for regional applications in the analysis of mixtures containing individuals from a broad geographic range. This method employs a multistage framework that allows disparate baselines to be used in a single integrated process that produces estimates along with the propagated errors from each stage. All individuals in the mixture sample are required to be genotyped for all genetic markers in the baselines used by this model, but the baselines do not require overlap in genetic markers or populations representing the broad-scale or regional baselines.</p> <p>We demonstrate our integrated multistage GSI model using a synthesized data set made up of Chinook salmon, <em>Oncorhynchus tshawytscha</em>, from the North Bering Sea of Alaska. The data set is designed to be run using R package, Ms.GSI, and it does not represent the composition of the real fishery. The results show an improved accuracy for estimates using an integrated multistage framework, compared to the conventional framework of using separate hierarchical steps. The integrated multistage framework allows GSI of a wide geographic area without first developing a large scale, high-resolution genetic baseline or dividing a mixture sample into smaller regions beforehand. This approach is more cost-effective than updating range-wide baselines with all regionally important markers.</p>
Data-Driven Identification and Analysis of Waiting Times in Business Processes: A Systematic Literature Review
<p>Supplementary Material for Systematic Literature Review titled "Data-Driven Identification and Analysis of Waiting Times in<br> Business Processes: A Systematic Literature Review"</p>
Identification data for discrimination between Apis mellifera pomonella from Kazakhstan and honey bees from other parts of the world
<p>The data in dw.xml file can be used for identification of <em>Apis mellifera pomonella</em> from Kazakhstan. This file should be open in IdentiFly software http://drawwing.org/identifly and used as classification data. The discrimination is based on 19 landmarks of a forewing. For more details see:</p> <p><br>Nawrocka A., Kandemir I., Fuchs S., Tofilski A. 2018. Computer software for identification of honey bee subspecies and evolutionary lineages. Apidologie 49: 172-184. https://doi.org/10.1007/s13592-017-0538-y</p> <p>Temirbayeva, K., Torekhanov A., Nuralieva U., Sheralieva Z., Tofilski A. 2023. In Search of Apis mellifera pomonella in Kazakhstan. Life 13:1860. https://doi.org/10.3390/life13091860</p>
Data for: Morphological species delimitation in the Western Pond Turtle (Actinemys): Can machine learning methods aid in cryptic species identification?
<p>As the discovery of cryptic species has increased in frequency, there has been interest in whether geometric morphometric data can detect fine-scale patterns of variation that can be used to morphologically diagnose such species. We used a combination of geometric morphometric data and an ensemble of five supervised machine learning methods to investigate whether plastron shape can differentiate two putative cryptic turtle species, <em>Actinemys marmorata</em> and <em>Actinemys pallida</em>. <em>Actinemys</em> has been the focus of considerable research due to its biogeographic distribution and conservation status. Despite this work, reliable morphological diagnoses for its two species are still lacking. We validated our approach on two datasets, one consisting of eight morphologically disparate emydid species, and the other consisting of two subspecies of <em>Trachemys</em> (<em>T. scripta scripta</em>, <em>T. scripta elegans</em>). The validation tests returned near-perfect classification rates, demonstrating that plastron shape is an effective means for distinguishing taxonomic groups of emydids via machine learning methods. By contrast, the same methods did not return high classification rates for a set of alternative phylogeographic and morphological binning schemes in <em>Actinemys</em>. All classification hypotheses performed poorly relative to the validation datasets and no single hypothesis was unequivocally supported for <em>Actinemys</em>. Two hypotheses had machine learning performance that was marginally better than our remaining hypotheses. In both cases, those hypotheses favored a two-species split between <em>A. marmorata</em> and <em>A. pallida</em> specimens, lending tentative morphological support to the hypothesis of two <em>Actinemys</em> species. However, the machine learning results also underscore that <em>Actinemys</em> as a whole have lower levels of plastral variation than other turtles within Emydidae, but the reason for this morphological conservatism is unclear.</p>
Accompanying data for the paper "Robustness of the Data-Driven Identification algorithm with incomplete input data"
<h2>Links</h2> <ul> <li>isSupplementTo <em>publication-article</em> <a href="https://doi.org/10.46298/jtcam.12590">https://doi.org/10.46298/jtcam.12590</a></li> <li>isNewVersionOf <em>dataset</em> <a href="../records/10090469">https://zenodo.org/records/10090469</a></li> </ul> <h2>Authors</h2> <ul> <li><strong>Leygue, Adrien</strong>, Ecole Centrale de Nantes, ORCID: <a href="https://orcid.org/0000-0003-0714-822X">0000-0003-0714-822X</a></li> </ul> <h2>Language</h2> <ul> <li>English</li> </ul> <h2>License</h2> <ul> <li>Creative Commons Attribution 4.0</li> </ul> <h2>Funding sources</h2> <ul> <li>This work was performed by using HPC resources of Centrale Nantes Supercomputing Center on the cluster Liger, granted and identified D1705030 by the High Performance Computing Institute(ICI).</li> </ul> <h2>Data structure and information</h2> <p>Synthetic data used in the case study (section 3) of the paper.</p> <p>The data in XDMF (Milou.xdmf ) + hdf5 (Milou.hdf5) format comprises:</p> <ol> <li>The 2D computational mesh with triangular linear elements</li> <li>The nodal Forces for all loading steps (nodal quantity)</li> <li>The displacement for all loading steps (nodal quantity)</li> <li>Cauchy stress fields for all loading steps (cell quantity)</li> </ol>
The Q-TOF proteomics data for the identification of mammalian L-fucose dehydrogenase (EC 1.1.1.122)
<p>The enclosed zip file contains data files (RAW format) from MS^E experiment. The experiment was performed with the use of Acquity nanoUPLC coupled with a Synapt G2 HDMS Q-TOF mass spectrometer (Waters) fitted with a nanospray source. It aimed at the identification of proteins present in the gel bands S1-S9 and the gel band Z1. The bands have come from SDS-PAGE and zymography analyses, respectively, of the most active enzyme fraction from the Reactive Red Agarose 120 purification step.</p>
Data, code, and supplementary information for Hyracoid locus identification
<p>Serially homologous elements pose an identification problem in fragmentary records, particularly those of vertebrate fossils. Examples include individual vertebrae in the vertebral column and teeth in a tooth row. Until an isolated element can be accurately attributed to a specific position within its series, multiple lines of ecological and evolutionary research cannot be conducted. However, varying levels of differentiability between loci, and varying patterns of differentiation across clades, make it impossible to develop a single set of diagnostic traits for any particular set of serial homologs, particularly mammalian molars. Here, we test the utility of a set of classification criteria for distinguishing molar tooth positions of hyraxes (Mammalia, Afrotheria, Hyracoidea), which have been considered indistinguishable in previous taxonomic studies. As part of the test, we evaluate the degree to which between-locus variation is conservative in this taxon, which would strengthen the predictive power of proposed traits even in cases where species identity is unknown. Suitable tests for hypotheses of conservatism in categorical traits did not exist, to our knowledge, and we, therefore, explored the behavior of previously developed metrics, Borges et al.'s δ, to assess conservatism in contrast to the phylogenetic signal produced by Brownian motion. This metric shows some promise but the nature of resulting distributions makes tests difficult to interpret, indicating a line of potential future methods improvement. We used a linear morphometric characterization of shape to validate the candidate traits. In the case of hyracoid molars, relatively simple ratios of linear measurements have strong discriminatory power despite evolutionary variation in between-locus differences. Overall, new or understudied taxa are likely to have lower molar loci differentiable by their relative length and talonid vs. trigonid width.</p>
LC-MS/MS, PSM and BLG properties data from "Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteins"
<p>This dataset contains the data analyzed in:</p> <p>Rodriguez Palomo I, Nair B, Chang Y, Dartigues B, Dekker K, Mackie M, Evans M, Macleod R, Olsen JV, Collins MJ. (2023) "<em>Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteins"</em></p> <p>It contains the following data:</p> <ul> <li>raw_files.zip Thermo RAW files for the 0, 4 and 128 days samples</li> <li>benchmark_results.zip PSMs data from the analysis of the RAW files <ul> <li>Data from runs in Mascot, Fragpipe, pFind, Metamorpheus, MaxQuant and DeNovoGUI</li> <li>Parameters and workflow files for MaxQuant and Fragpipe</li> </ul> </li> <li>bovin_blg_prop.zip BLG properties files: amyloid formation, 3D structure and solvent accessibility</li> <li>benchmark_table.csv Table with runs settings for benchmarking</li> <li>parameters_table.xlsx Spreadsheet with software parameters, derived from files used to run each software</li> </ul> <p> </p>
Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) III: Introducing the KEN; Data for CH4
<p>We present all of the data across our SNR and abundance study for the molecule H2O for an exoEarth twin. The wavelength range is from 0.8-1.5 micron, with 25 evenly spaced 20%, 30%, and 40% bandpasses in this range. The SNR ranges from 3-20. We present the lower and upper wavelength per bandpass, the input CH4 value (abundance case), the retrieved CH4 value (presented as the log10(VMR)), the lower and upper limits of the 68% credible region (presented as the log10(VMR)), and the log-Bayes factor for CH4. For more information about how these were calculated, please see Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) III: Introducing the KEN, accepted and currently available on arXiv. </p> <p>To open this csv as a Pandas dataframe, use the following command:</p> <p>your_dataframe_name = pd.read_csv(f'zenodo_table.csv', dtype={'Input CH4': str})</p>
Data from: Identification of neural oscillations and epileptiform changes in human brain organoids.
<p>Seurat object containing processed single-cell RNA sequencing data described in:</p> <p>Samarasinghe, R.A., Miranda, O.A., Buth, J.E. <em>et al.</em> Identification of neural oscillations and epileptiform changes in human brain organoids. <em>Nat Neurosci</em> <strong>24, </strong>1488–1500 (2021). <a href="https://doi.org/10.1038/s41593-021-00906-5">https://doi.org/10.1038/s41593-021-00906-5</a></p> <p>Additional raw and processed data can be accessed at the Gene Expression Omnibus under accession number <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE165577">GSE165577</a>. </p> <p> </p>
Dataset - Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model
<p>This dataset accompanies the manuscript titled "Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model", submitted by co-authors to the journal Global Change Biology (GCB) Bioenergy.</p> <p>Wouter Meijninger<sup>1</sup>, Berien Elbersen<sup>1</sup>, Michiel van Eupen<sup>1</sup>, Stephan Mantel<sup>2</sup>, Pilar Ciria Ciria<sup>3</sup>, Andrea Parenti<sup>4</sup>, Marina Sanz Gallego<sup>3</sup> and Paloma Perez Ortiz<sup>3</sup>, Marco Acciai<sup>4</sup>,and Andrea Monti<sup>4</sup><br> Institutes: 1) Wageningen University & Research, 2) ISRIC, 3) CIEMAT, 4) Bologna University,</p> <p><strong>Abstract (Manuscript)</strong></p> <p>In the context of increased pressures on land for food and non-food production it is relevant to understand better, which land resources have become unused and abandoned and where these lands are. Data on where these lands are and what their extend is are not collected in regular statistics. In this paper we present an approach to detect signs of abandonment in cropping land using radar coherence data. The methodology was tested in the Spanish regions of Albacete and Soria where agricultural land abandonment is a common process. The results show that land abandonment detection using radar coherence data works well for the region of Albacete in arable lands. The radar-based analysis is a relatively simple method to detect land abandonment in an early to longer-term state and can therefore be applied once developed and tested further in other regions to larger areas of the EU where land abandonment is serious and needs monitoring and policy response. The applicability of the method to Soria and Emilia Romagna (Italy) regions show that there are still challenges to overcome to make the method more widely applicable for detecting land abandonment in other environmental zones of Europe. Lack of reliable training and validation data, like LPIS data, in regions is one of the challenges in this respect.</p> <p><strong>Readme data files</strong></p> <p><em>Coherence_quarterly_statisitcs_2017_to_2020.zip</em></p> <p>Radar coherence quarterly statistics - Albacete (Spain)</p> <p>Radar coherence data is based on Sentinel-1B<br> Period: 2017 to 2020</p> <p>File naming (.tif files) per year (<em>YYYY</em>):</p> <ul> <li>Mean coherence: <em>mean_YYYY_1to4.tif</em></li> <li>Standard deviation coherence: <em>std_YYYY_1to4.tif</em></li> <li>Range coherence: <em>range_YYYY_1to4.tif</em></li> <li>Mean delta coherence: <em>mean_delta_YYYY_1to4.tif</em></li> <li>Standard deviation delta coherence: <em>std_delta_YYYY_1to4.tif</em></li> <li>Maximum delta coherence: <em>max_delta_YYYY_1to4.tif</em></li> </ul> <p>Each file consists of 4 bands:</p> <ul> <li>band 1: 1st quarter [Jan-Feb-March]</li> <li>band 2: 2nd quarter [April-May-June]</li> <li>band 3: 3rd quarter [July-Aug-Sept]</li> <li>band 4: 4th quarter [Oct-Nov-Dec]</li> </ul> <p>Statistics are based on radar coherence data, which is scaled between >0 and 1<br> No data: 0-values</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p><em>SIGPAC_data_Albacete_2018_to_2020.zip</em></p> <ul> <li>More than 5 year fallow (20m raster files)</li> <li>Land Use Land Cover LULC (20m raster files)</li> </ul> <p>More than 5 year fallow (according to SIGPAC)<br> Period: 2018 to 2020<br> File naming (ENVI files):</p> <ul> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2018_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2018_20m.hdr)</li> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2019_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2019_20m.hdr)</li> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2020_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2020_20m.hdr)</li> </ul> <p>Pixel values:<br> 0: Not fallow<br> 1: Fallow more than 5 years</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p>Land Use Land Cover LULC (according to SIGPAC)<br> Period: 2018 to 2020<br> File naming (ENVI files):</p> <ul> <li>LULC_SIGPAC_Albacete_2018_20m.dat (+ LULC_SIGPAC_Albacete_2018_20m.hdr)</li> <li>LULC_SIGPAC_Albacete_2019_20m.dat (+ LULC_SIGPAC_Albacete_2019_20m.hdr)</li> <li>LULC_SIGPAC_Albacete_2020_20m.dat (+ LULC_SIGPAC_Albacete_2020_20m.hdr)</li> </ul> <p>Pixel values:</p> <ul> <li>0 - Nan</li> <li>1 - Arable land</li> <li>2 - Vineyards</li> <li>3 - Olives</li> <li>4 - Fruits</li> <li>5 - Nuts</li> <li>6 - Citrus</li> <li>7 - Permanent grassland</li> <li>8 - Forest</li> <li>9 - Rest, small elements</li> <li>10 - Built-up areas</li> <li>11 - Water</li> <li>12 - Roads</li> <li>13 - Unproductive land</li> </ul> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p><em>Annual_unused_used_land_maps_Albacete_2017_to_2020.zip</em></p> <p>Derived annual unused/used land maps - Albacete (Spain), based on Random-Forest model<br> Period: 2017-2020<br> File naming (ENVI files):</p> <ul> <li>predict_RF_Albacete_2017_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2017_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2018_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2018_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2019_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2019_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2020_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2020_quarterly_stats_LU1_v181920.hdr)</li> </ul> <p>Pixel values:<br> 0 - Used (and/or Nan)<br> 1 - Unused</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p><em>Four_year_abandoned_land_Albacete_2017_to_2020.zip</em></p> <p>Four-year abandonment map is based on the 4 annual unused/used land maps<br> File naming (ENVI):</p> <ul> <li>Four_year_abandoned_land_Albacete_2017_to_2020.dat (+ Four_year_abandoned_land_Albacete_2017_to_2020.hdr)</li> </ul> <p>Pixel values:<br> 0 - (Nan)<br> 1 - Used (1 year unused in period 2017 - 2020)<br> 2 - Used (2 year unused in a row in period 2017 - 2020)<br> 3 - Abandoned (3 year unused in a row in period 2017 - 2020)<br> 4 - Abandoned (4 year unused in a row in period 2017 - 2020)</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p>
Data for: Nawrocka et al., Variable-temperature NMR spectroscopy for metabolite identification in biological materials, RSC Adv., 2021, 11, 35321, DOI: 10.1039/d1ra05626c
<p>Raw NMR spectrometer data (Varian/Agilent format) for the paper Nawrocka et al., Variable-temperature NMR spectroscopy for metabolite identification in biological materials, RSC Adv., 2021, 11, 35321, DOI: 10.1039/d1ra05626c</p> <p>Includes variable-temperature series for artificial and natural mixtures of metabolites and spectra of separate metabolites.</p>
Code and data sets for "MS²Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates"
<p>Code used to prepare data sets, train and evaluate new MS²PIP models, evaluate MS²Rescore for immunopeptidomics, and generate figures. See README.md for more information on how to use these files and reproduce the results reported in the manuscript titled "MS²Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates".</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.