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11,174 results for “identifiers”
Multi-omic approach to identify phenotypic modifiers underlying cerebral demyelination in X-linked adrenoleukodystrophy
<p>These are the data tables used to produce results in the publication:</p> <p>"Multi-omic approach to identify phenotypic modifiers underlying cerebral demyelination in X-linked adrenoleukodystrophy."<br> Phillip A. Richmond & Frans van der Kloet et al.</p> <p>Submitting to Frontiers in Cellular and Developmental Biology, 2020, Peroxisomal Special Issue. </p> <p>These tables include normalized measurements from four omics technologies, with no identifying information included. For details on processing, see the manuscript or contact:</p> <p>prichmond (at) cmmt (dot) ubc (dot) ca. </p> <p>Description of Files</p> <ul> <li>Sample mapping <ul> <li>20180314_sib_pairs.xlsx <ul> <li>Excel sheet describing family numbering, etc. used as a mapping table within the sheets below. </li> </ul> </li> </ul> </li> <li>Methylation: <ul> <li>DMRs_5_Families_ALL_0.10DB_Dec2019.csv <ul> <li>Significant methylated regions with delta beta at least 10 percent when a single family is left out</li> </ul> </li> <li>ALD_Deconvoluted_Betas_Dec2019.csv <ul> <li>All fitted betas for every subject (single CpG)</li> </ul> </li> <li>ALD_Limma_Final_Dec2019_CHR.csv <ul> <li>All fitted effects using limma modeling per CpG </li> </ul> </li> </ul> </li> <li>RNA: <ul> <li>Count_data.txt <ul> <li>The raw count table summed at the gene level using featureCounts.</li> </ul> </li> <li>Pvalues_all_23_01_2019.csv <ul> <li>All pvalues and log fold changes for the genes included in the modeling process (also with family left out)</li> </ul> </li> <li>Tmm_norm_counts_5_2_2020.csv <ul> <li>Tmm normalized RNA count data</li> </ul> </li> </ul> </li> <li>Proteomic <ul> <li>Report_Precursor_Peptides.xls <ul> <li>The proteomic data as an excel spreadsheet</li> </ul> </li> </ul> </li> <li>Pvalues_prot_13_3_2019.xlsx <ul> <li>The pvalues and log fold changes (also with family left out)</li> </ul> </li> <li>Lipids: <ul> <li>Lipid_data.csv <ul> <li>The lipid data (metabolites with missings are removed)</li> </ul> </li> <li>Pvalues_lipids.csv <ul> <li>Pvalues for the lipid data (also with family left out)</li> </ul> </li> </ul> </li> </ul> <p><br> NOTE: For use of these data files for processing and reproducing results of the manuscript, please see https://github.com/Phillip-a-richmond/ALD_Modifier_Project. </p> <p> </p>
Bakraur बक्रौर, Bihār. Stūpa identified as Sujātā Maṭh, under excavation.
<p><a href="https://en.wikipedia.org/wiki/Bakraur">Bakraur</a> बक्रौर, Bihār. Stūpa identified as Sujātā Maṭh, under excavation. The stūpa, popularly if erroneously identified by the Burmese in the 20th century as the location where Sujātā offered rice milk to the Buddha, dates to the Pāla period and was excavated in 2006.</p>
Bakraur बक्रौर, Bihār. Stūpa identified as Sujātā Maṭh, under excavation.
<p><a href="https://en.wikipedia.org/wiki/Bakraur">Bakraur</a> बक्रौर, Bihār. Stūpa identified as Sujātā Maṭh, under excavation. The stūpa, popularly if erroneously identified by the Burmese in the 20th century as the location where Sujātā offered rice milk to the Buddha, dates to the Pāla period and was excavated in 2006.</p>
Data for: Machine learning identifies robust matrisome markers and regulatory mechanisms in cancer
<p>The expression and regulation of matrisome genes - the ensemble of extracellular matrix, ECM, ECM-associated proteins and regulators as well as cytokines, chemokines and growth factors - is of paramount importance for the many biological processes and signals within the tumor microenvironment. The availability of large and diverse multi-omics data enables mapping and understanding the regulatory circuitry governing the tumor matrisome to an unprecedented level, though such a volume of information requires robust approaches to data analysis and integration. In this study, we show that combining Pan-Cancer expression data from The Cancer Genome Atlas (TCGA) with genomics, epigenomics and microenvironmental features from TCGA and other sources enables the identification of “landmark” matrisome genes and machine learning-based reconstruction of their regulatory networks in 74 clinical and molecular subtypes of human cancers and approx. 6700 patients. These results, enriched for prognostic genes and cross-validated markers at the protein level, unravel the role of genetic and epigenetic programs in governing the tumor matrisome and allow the prioritization of tumor-specific matrisome genes (and their regulators) for the development of novel therapeutic approaches.</p>
GitHub Java Corpus - Function Identifiers
<p>This dataset contains function identifiers extracted from the GitHub Java Corpus (<a href="http://groups.inf.ed.ac.uk/cup/javaGithub/">http://groups.inf.ed.ac.uk/cup/javaGithub/</a>).</p> <p>Each line corresponds to a method declaration. A line contains the name of the method declaration followed by the function identifiers (i.e., function calls) contained within the method body. </p> <p>The file embeddings_train.json can be used to train a word/sentence embedding model using the code in the Github repository (link below).</p> <p>The corpus was used for the experiments in the paper <strong>Combining Code Embedding with Static Analysis for Function-Call Completion</strong>.</p> <p>Github repository to replicate the experiments: https://github.com/mweyssow/cse-saner</p>
A2aR Oligomeric assemblies identified from MD simulations using in-vivo mimetic biomembranes
<p>GPCR oligomerisation is known to play an important role in the receptor signalling. However, due to the technical challenges, the structural information of GPCR oligomerisation is still very limited, which hinders our understanding of GPCR signalling in a fuller picture. In this deposit, we provide the structural coordinates of various oligomeric assemblies of Adenosine A2a receptor that were sampled from unbiased MD simulations.</p> <p>For more information regarding the MD simulation setup and definitions of the various calculated values, please check out our paper on <a href="https://www.biorxiv.org/content/10.1101/2020.06.24.168260v2">BioRxiv</a> (doi: https://doi.org/10.1101/2020.06.24.168260)</p> <p>** Simulation setup **<br> 9 copies of A2aR were randomly inserted into an <em>in-vivo </em>mimetic biomembrane (of size of 45nm x 45nm) to build the initial configuration of the simulations. 10 such systems were set up for A2aR in the inactive state, 10 for the active state and 10 for the active in complex with the mini Gs state. These systems were represented by MARTINI 2 coarse-grained models and were simulated for 50 micro-seconds. The use of MARTINI coarse-grained force field would freeze the receptor conformation in the initial configuration, thus decoupled the oligomerization from such process as ligand-induced conformational change. The more efficient sampling of coarse-grained force field therefore allowed us to explore fully the protein-protein associations in the oligomerisation process. Protein-protein associations were identified when any atoms from two protomers were getting closer than 0.75 nm. The oligomerisation process was monitored and the sampled various oligomeric assemblies were identified for calculation of oligomer residence time. </p> <p><br> ** Coordinate file explained **<br> These pdb files contain the A2aR oligomer structures in atomistic models. The coarse-grained oligomeric structures were converted back to atomistic models using CHARMM 36 force field. The identified oligomeric structures from each oligomeric order were clustered. 10 structures were randomly taken from each cluster and stored as individual models in the pdb files with a naming format <strong><em>{Conf. State}_OS{Oligomeric Order}_cl{Cluster id}.pdb</em></strong>. The pdb files can be viewed by such visualization tools as PyMol, Chimera or JMol etc.</p> <p><br> ** Spreadsheet file explained **<br> The calculated properties, including residence time and geometry, of each identified oligomer were stored in the Excel shreadsheet (Oligomeric_Assembly_Distribution.xlsx). Each oligomeric order, i.e. oligomer order = 2,3,4,5, opens an individual spreadsheet page where the calculated data were grouped by oligomers' conformational states (i.e. Inactive, Active and Act + mini Gs) and then ranked by oligomers' residence time. The measurements for describing the oligomer geometry were shown in columns after "Cluster ID" and before "Count". For definitions of these measurements, please refer to our paper. Pictures of the oligomers viewed from the extracellular side and intracellular side were also provided in the the spreadsheet to assist visualisation.</p>
Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis - Image data
<p>The dataset contains raw imaging data from the work:</p> <p>"Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis"</p> <p>The dataset is organized as the following: the "FigureX_" or SupplementaryFigure_X" suffix in the filename refers to the figure in the paper in which the raw data is analyzed and/or visualized. The data is "raw", i.e. not processed. However, in many cases, maximum projections of the original 3D image stacks have been uploaded due to size limitations. The total size of the image stacks approaches 0.5TB. To access the full 3D image stacks please contact the corresponding author (Francesco Pampaloni, fpampalo@bio.uni-frankfurt.de).</p> <p><strong>Authors</strong></p> <p>Lotta Hof<sup>1</sup>*, Till Moreth<sup>1</sup>*, Michael Koch<sup>1</sup>, Tim Liebisch<sup>2</sup>, Marina Kurtz<sup>3</sup>, Julia Tarnick<sup>4</sup>, Susanna M. Lissek<sup>5</sup>, Monique M.A. Verstegen<sup>6</sup>, Luc J.W. van der Laan<sup>6</sup>, Meritxell Huch<sup>7</sup>, Franziska Matthäus<sup>2</sup>, Ernst H.K. Stelzer<sup>1</sup>, Francesco Pampaloni<sup>1§</sup></p> <p><sup>1</sup>Physical Biology Group, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>2</sup>Faculty of Biological Sciences, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>3</sup>Department of Physics, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>4</sup>Deanery of Biomedical Science, University of Edinburgh, Edinburgh, United Kingdom</p> <p><sup>5</sup>Experimental Medicine and Therapy Research, University of Regensburg, Regensburg, Germany</p> <p><sup>6</sup>Department of Surgery, Erasmus MC – University Medical Center, Rotterdam, The Netherlands</p> <p><sup>7</sup>The Wellcome Trust/CRUK Gurdon Institute, University of Cambridge, Cambridge, United Kingdom. Present address: Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany</p> <p>*contributed equally</p> <p><sup>§</sup>corresponding author: fpampalo@bio.uni-frankfurt.de</p> <p><strong>Abstract</strong></p> <p><em>Background</em></p> <p>Organoids are morphologically heterogeneous three-dimensional cell culture systems and serve as an ideal model for understanding the principles of collective cell behaviour in mammalian organs during development, homeostasis, regeneration and pathogenesis. To investigate the underlying cell organisation principles of organoids, we imaged hundreds of pancreas and cholangio carcinoma organoids in parallel using light sheet and bright field microscopy for up to seven days.</p> <p><em>Results</em></p> <p>We quantified organoid behaviour at single-cell (microscale), individual-organoid (mesoscale), and entire-culture (macroscale) levels. At single-cell resolution, we monitored formation, monolayer polarisation and degeneration, and identified diverse behaviours, including lumen expansion and decline (size oscillation), migration, rotation and multi-organoid fusion. Detailed individual organoid quantifications lead to a mechanical 3D agent-based model. A derived scaling law and simulations support the hypotheses that size oscillations depend on organoid properties and cell division dynamics, which is confirmed by bright field microscopy analysis of entire cultures.</p> <p><em>Conclusion</em></p> <p>Our multiscale analysis provides a systematic picture of the diversity of cell organisation in organoids by identifying and quantifying the core regulatory principles of organoid morphogenesis.</p>
Gene co-ordinates, expression levels; SNP identifiers and functions for Drosophila melanogaster (Sussex LHM population)
<p>Data for SNP context information to add to GWAS results. Specifically, SNP functions, sex-bias in gene expression, official SNP idenfiers from NCBI dbSNP, and gene positions and names (from UCSC Genome Browswer). Most of the input files are on-line and their URLs are stated in the code (make_dmel_accessory_data.sh). Also includes code, logs, and exploratory graphs.</p>
Artificial Intelligence Identifies Individuals with Prediabetes from Single-Lead Electrocardiograms
<h2>Contents</h2> <ul> <li><strong>codes.zip</strong> <ul> <li>For ECG feature extraction (this will need original ECG signal data) <ul> <li>ecg_feature_extraction.sh</li> <li>ecg_feature_extraction.py</li> <li>feature_extractor.py</li> </ul> </li> <li>For training with hyperparameter optimization <ul> <li>train.sh</li> <li>train.py</li> </ul> </li> <li>For prediction of prediabetes/diabetes from ECG feature <ul> <li>test.sh</li> <li>test.py</li> </ul> </li> </ul> </li> <li><strong>raw_ecg_data.zip</strong>: 16,766 ECG records used in our analyses. Each record is a 5,000 x 12 matrix in a CSV file. (In this dataset, value 1 represents 4.88 µV.)</li> <li><strong>external_ecg_data.zip</strong>: 2,456 ECG records used in our external validation. Each record is a 5,000 x 12 matrix in a CSV file. (In this dataset, value 1 represents 1 µV.)</li> <li><strong>participant_characteristics.csv</strong>: Health check records of 16,766 participants where the information below are stored. <ul> <li>participant_id: IDs for participant. Some IDs are duplicated because the dataset contains multiple records from some of the participants.</li> <li>ecg_id: IDs for ECG records, all of which are unique</li> <li>age: The age of each participant at the time of the health checkup</li> <li>male_sex: If the participant is male, "True" is recorded</li> <li>smoking: if the participant smokes, "True" is recorded</li> <li>drinking: 1 for "rarely", 2 for "occasionally" and 3 for "regularly" is recorded according to the frequency of drinking</li> <li>height: participant's height in centimeters (cm)</li> <li>weight: participant's body weight in kilograms (kg)</li> <li>BMI: body mass index, calculated using the formula: weight (kg) / [height (m)]^2</li> <li>pulse_rate: pulse rate in pulse per minute (/min) </li> <li>sBP: systolic blood pressure in mmHg</li> <li>dBP: diastolic blood pressure in mmHg</li> <li>FPG: fasting plasma glucose levels measured in milligrams per deciliter (mg/dL)</li> <li>HbA1c: hemoglobin A1c levels in %</li> <li>dm_under_treatment: if the participant was undergoing treatment for known diabetes, "True" is recorded</li> <li>prediabetes_diabetes: classification label which is "True" if a participant meet either of the following criteria <ul> <li>FPG ≥ 110 mg/dL</li> <li>HbA1c ≥ 6.0%</li> <li>Undergoing treatment for diabetes</li> </ul> </li> <li>development_data: "True" in records used as development data in our study</li> </ul> </li> <li><strong>external_cohort_characteristics.csv</strong>: Health check records of 2,456 participants where the information below are stored. <ul> <li>ecg_id: IDs for ECG records, all of which are unique</li> <li>prediabetes_diabetes: classification label which is "True" if a participant meet either of the following criteria <ul> <li>FPG ≥ 110 mg/dL</li> <li>HbA1c ≥ 6.0%</li> <li>Undergoing treatment for diabetes</li> </ul> </li> <li>FPG: fasting plasma glucose levels measured in milligrams per deciliter (mg/dL)</li> <li>HbA1c: hemoglobin A1c levels in %</li> <li>dm_under_treatment: if the participant was undergoing treatment for known diabetes, "True" is recorded</li> </ul> </li> <li><strong>ecg_feature_data.zip</strong>: extracted ECG features (unprocessed), for 12-lead and 1-lead ECG <ul> <li>ecg_features_1-lead.csv [Single-lead (lead I) ECG]</li> <li>ecg_features_12-lead.csv [12-lead ECG]</li> <li>ecg_features_12-leads_external_cohort.csv [12-lead ECG of external cohort]</li> </ul> </li> <li><strong>feature_list.zip</strong>: List of ECG features used (to be used for ECG extraction for original data) <ul> <li>feature_list_269_12-lead.csv [269 features for 12-lead ECG analysis]</li> <li>feature_list_28_1-lead.csv   [28 features for single-lead (lead I) analysis]</li> </ul> </li> <li><strong>model_12-lead.zip, model_1-lead.zip</strong>: model trained with our 12-lead or single-lead (lead I) ECG data, and the classification thresholds, used for test<br> <ul> <li>model_fold_1.pkl - model_fold_10.pkl : model for each of 10-fold cross validation</li> <li>average_threshold.pkl : classification threshold, which is the average of 10-fold</li> </ul> </li> </ul> <p>Codes and data for demo are also available in (https://github.com/dkoga4116/diabetes_detector)</p>
Phlorest phylogeny derived from Kitchen et al. 2009 'Bayesian phylogenetic analysis of Semitic languages identifies an Early Bronze Age origin of Semitic in the Near East'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Kitchen A, Ehret C, Assefa S & Mulligan CJ. 2009. Bayesian phylogenetic analysis of Semitic languages identifies an Early Bronze Age origin of Semitic in the Near East. Proceedings of the Royal Society B: Biological Sciences, 270(1668), 2703-2710.</p> </blockquote>
Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries
<p>This dataset contains data collected during a study <a href="https://www.sciencedirect.com/science/article/pii/S0740624X23000989"><em><strong>"Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries"</strong></em></a> conducted by <em>Martin Lnenicka (University of Pardubice, Pardubice, Czech Republic), Anastasija Nikiforova (University of Tartu, Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Kosovska Mitrovica, Serbia), Daniel Rudmark (University of Gothenburg and RISE Research Institutes of Sweden, Gothenburg, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Caterina Santoro (KU Leuven, Leuven, Belgium), Cesar Casiano Flores (University of Twente, Twente, the Netherlands), Marijn Janssen (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain).</em></p> <p>It is being made public both to act as supplementary data for "<em>Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries</em>", Government Information Quarterly*, and in order for other researchers to use these data in their own work. </p> <p>***Methodology***</p> <p>The paper focuses on benchmarking of open data initiatives over the years and attempts to identify patterns observed among European countries that could lead to disparities in the development, growth, and sustainability of open data ecosystems. </p> <p>This study examines existing benchmarks, indices, and rankings of open (government) data initiatives to find the contexts by which these initiatives are shaped, both of which then outline a protocol to determine the patterns. The composite benchmarks-driven analytical protocol is used as an instrument to examine the understanding, effects, and expert opinions concerning the development patterns and current state of open data ecosystems implemented in eight European countries - Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. 3-round Delphi method is applied to identify, reach a consensus, and validate the observed development patterns and their effects that could lead to disparities and divides. Specifically, this study conducts a comparative analysis of different patterns of open (government) data initiatives and their effects in the eight selected countries using six open data benchmarks, two e-government reports (57 editions in total), and other relevant resources, covering the period of 2013–2022.</p> <p>***Description of the data in this data set***</p> <p>The file "OpenDataIndex_<em>2013_</em>2022" collects an overview of 27 editions of 6 open data indices - for all countries they cover, providing respective ranks and values for these countries. These indices are:</p> <p>1) Global Open Data Index (GODI) (4 editions)</p> <p>2) Open Data Maturity Report (ODMR) (8 editions)</p> <p>3) Open Data Inventory (ODIN) (6 editions)</p> <p>4) Open Data Barometer (ODB) (5 editions)</p> <p>5) Open, Useful and Re-usable data (OURdata) Index (3 editions)</p> <p>6) Open Government Development Index (OGDI) (2 editions)</p> <p>These data shapes the third context - open data indices and rankings. The second sheet of this file covers countries covered by this study, namely, Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. It serves the basis for Section 4.2 of the paper.</p> <p>Based on the analysis of selected countries, incl. the analysis of their specifics and performance over the years in the indices and benchmarks, covering 57 editions of OGD-oriented reports and indices and e-government-related reports (2013-2022) that shaped a protocol (see paper, Annex 1), 102 patterns that may lead to disparities and divides in the development and benchmarking of ODEs were identified, which after the assessment by expert panel were reduced to a final number of 94 patterns representing four contexts, from which the recommendations defined in the paper were obtained. These patterns are available in the file "OGDdevelopmentPatterns". The first sheet contains the list of patterns, while the second sheet - the list of patterns and their effect as assessed by expert panel.</p> <p>***Format of the file***<br>.xls, .csv (for the first spreadsheet only)</p> <p>***Licenses or restrictions***<br>CC-BY</p> <p> </p> <p>For more info, see README.txt<br> </p>
Properties of identified ship tracks
<p>The ship track database of is used, which used "day microphysics" images comprised of a composite of visible, near and thermal infrared channels were used to manually locate likely positions of ship tracks. Identification was assisted by examining the CDNC, calculated from the MYD06 level 2 cloud retrieval products from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. The CDNC is calculated based on the adiabatic assumption, using the cloud optical depth and cloud effective radius from the MODIS MYD06 level 2 product.</p><p>Ship locations were sourced from their automatic identification system (AIS) data, allowing an observed ship track to be linked to the generating ship. Local meteorology was gathered from the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis data, and ships mass emission rates were calculated using their specific fuel consumption and estimated drag. </p><p>Data labels:</p><ul><li>trackno: Ship track identifier (-)</li><li>sox: SO emission rate (kg s^-1)</li><li>cbh: Cloud base height (m)</li><li>blh: Boundary layer height (m)</li><li>cth: Cloud top height (m)</li><li>spd_res: Relative velocity between ship velocity and wind velocity (m s^-1)</li><li>nd_cln: Background cloud droplet number concentration (cm^-3)</li><li>nd_pol: Ship track cloud droplet number concentration (cm^-3)</li><li>cf_liq: Liquid cloud fraction (-)</li><li>lwp: Liquid water path (g m^-2)</li><li>t1000: Temperature at 1000 hPa (K)</li><li>LTS: Low tropospheric stability (K)</li><li>ctt: Cloud top temperature (K)</li><li>ctrc: Cloud top radiative cooling (W m^-2)</li><li>is_coupled: Flag for cloud coupling according to cloud base height indicator (cbh<1000 m)</li></ul>
Empirical data, qualitative codes, analysis: Schuur J.S. et al. Identifying levers of urban neighbourhood transformation. npj Urban Sustainability (2023)
<p>Please refer to the stand-alone "2023_SchuurJS_UrbanSustainabilityfinal.html" file where the analysis and results corresponding to the article titled: "Identifying levers of urban neighbourhood transformation using serious games" is presented. The underlying data sets and Rmarkdown script used for the analysis can be used to re-run the analysis. Ensure to read the "0_README.txt" file to build the appropriate folder structure to do so.</p>
Data inputs and results from AI-supported title and abstract screening "Lack of evidence regarding markers identifying acute heart failure in patients with COPD: an AI-supported systematic review"
<p>These comma-separated data files were used to conduct the AI supported screening of [Lack of Evidence Regarding Markers Identifying Acute Heart Failure in Patients with COPD: An AI-supported Systematic Review (working title)], following the methodology described in the publication (URL/doi to be uploaded).</p> <p>These files provide insight into the AI-supported screening process and the choices made by the human reviewer.</p>
RAYUELA - Open Data - Data collected through a serious game created to identify patterns and profiles of young potential victims/perpetrators of cybercrimes.
<p>The data of this dataset have been collected in the pilots carried out by the RAYUELA project in different countries of the European Union. The participants are minors and the game sessions have been carried out in schools and summer camps in a supervised way.</p>
Shapefiles showing the locations of long-term climate change refugia and hotspots identified in the FairSeas report "A Climate Resilient Path for Ireland's Marine Protected Areas Network"
<p>Shapefiles created for the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network", an addendum chapter to "Revitalising Our Seas report: Identifying<br>Areas of Interest for Marine Protected Area Designation in Irish Waters"</p> <p>These shapefiles summarise long-term patterns that emerge from the spatial-meta analysis of physical-biogeochemical and species distribution modelling data, providing an overview of the distribution of climate change refugia and climate change hotspots across Ireland's National Marine Planning Framework between 2026 - 2069, and across the two emissions scenarios considered in the report (RCP4.5 and RCP8.5). </p> <p>Filenames refer to the specific analysis each set of shapefiles belong to: Benthic habitats, benthic megafauna, pelagic habitats, pelagic megafauna and forage fish. Details of the modelling datasets used in each of these analyses, the meta-analysis method and shapefile creation can be found in Annex A1 in the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network".</p>
Identifying the interplay between protective measures and settings on the SARS-CoV-2 transmission using a Bayesian network [Dataset]
<p>data07B.csv: dataset for the study of the SARS-CoV-2 transmission.</p> <p>CPTNetica.txt: conditional probabilities tables of each variable given through Netica once the BN obtained in R code is loaded.</p> <p>code01.R: code to learn structure and parameters of the SARS-CoV-2 BN model.</p>
GWAS summary stats in "Genome-wide association meta-analysis identifies two novel loci associated with dental caries."
<p>Summary stats of the genome-wide meta-analysis for dental caries and periodontal diseases in our study (population A and B).</p> <p>Article "Genome-wide association meta-analysis identifies two novel loci associated with dental caries."</p> <p>https://doi.org/10.1186/s12903-024-04799-1<br><br></p>
GWAS Summary Statistics for Publication: Identifying novel genetic and phenotypic associations to genomic features by leveraging off-target reads in exome sequencing data
<p>This dataset contains summary statistics for genome-wide association studies (GWAS) conducted on genomic features derived from off-target reads in whole-exome sequencing (WES) data. The study utilized tools like Seeing Beyond the Target (SBT) and ImReP to construct novel phenotypic features from unmapped reads in ~50,000 participants in the UK Biobank. Features include mitochondrial DNA (mtDNA) copy number, ribosomal DNA (rDNA) copy number (5S, 18S, 28S), immune repertoire metrics (e.g., T-cell receptor alpha diversity), and microvial genome load (viral and fungal).</p> <p>Summary statistics can be used for replication studies, meta-analyses, or further exploration of these phenotypes.</p>
Survey answers to identify barriers and enablers to climate change adaptation solutions (as part of the Adaptation AGORA project)
<p><span>This dataset s the result of collaborative work for Deliverable 4.1 (WP4; T4.1) of the Adaptation AGORA project. This survey aimed to capture the key factors supporting or hindering adaptation practitioners experienced with engaging citizens and stakeholders in climate change adaptation initiatives. </span></p> <p><span>The survey targeted <span>European adaptation practitioners, i.e., all professionals in charge of implementing climate change adaptation initiatives, and more particularly, those involved in collaborative processes engaging stakeholders and citizens </span><span>at the local and/or regional scale.</span></span></p> <p><span><span>The survev protocol can be found here: Euro-Mediterranean Center for Climate Change, University of Geneva, Stockholm Environment Institute, Barcelona Supercomputing Center, & Agenzia per la Promozione della Ricerca Europea. (2024). Protocol to carry out surveys to identify barriers and enablers to climate change adaptation solutions. Zenodo. <a href="https://doi.org/10.5281/zenodo.13385305" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13385305</a></span></span></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.