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404 results for “Reference Data”
Data from: A global reference database in FAOSTAT of cropland nutrient budgets and nutrient use efficiency: nitrogen, phosphorus and potassium, 1961–2020
<p class="MsoNormal">Agricultural nutrient budgets help to identify an excess or an insufficiency in use of fertilizers and other nutrient sources. Nutrient budgets allow for indicators such as the nutrient balance (surplus or deficit) and nutrient use efficiency to be estimated. This can help in the monitoring of agricultural productivity and sustainability globally. The present dataset is a global database of country-level budget estimates for nitrogen (N), phosphorus (P) and potassium (K) in cropland. The database is disseminated in FAOSTAT and provides a global reference, synthesizing and continuously updating the state-of-the-art on this topic. The database covers the period 961 to 2020 for 205 countries and territories, as well as regional and global aggregates. Results indicate the wide range in nutrient use and use efficiencies across regions, nutrients, and time. This dataset introduces improvements over previous work in relation to key nutrient coefficients affecting nutrient budgets and use efficiency estimates. This is especially for nutrient removal in crop products, manure nutrient content, atmospheric deposition and crop biological N fixation rates. </p>
Data and code for 'Pseudogenes act as a neutral reference for detecting selection in prokaryotic pangenomes'
<p>This repository contains the code and files for reproducing the analyses and results reported in 'Pseudogenes act as a neutral reference for detecting selection in prokaryotic pangenomes' by Gavin M. Douglas and B. Jesse Shapiro (<a href="https://doi.org/10.1038/s41559-023-02268-6">https://doi.org/10.1038/s41559-023-02268-6</a>).</p> <p>File organization and descriptions:</p> <ul> <li><strong>code/</strong> - Contains GitHub repository releases of code used in manuscript (the other folders contain datafiles only). This code is provided here as well as on GitHub to ensure long-term access. <ul> <li><strong>handy_pop_gen-1.1.0/</strong> - release v1.1.0 of the convenience repository (used for specific data processing and analysis steps referred to in the manuscript).</li> <li><strong>pangenome_pseudogene_null-1.1.0/ </strong>- Main code repository for manuscript.</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>broad_pangenome_analysis/</strong> <ul> <li><strong>element_info/element_counts.tsv.gz</strong> - Counts of (filtered) pseudogenes and intact genes called per genome accession.</li> <li><strong>element_info/gene_sizes.tsv.gz</strong> - Gene sizes in base-pairs.</li> <li><strong>element_info/pseudogene_sizes.tsv.gz</strong> - Filtered pseudogene sizes in base-pairs.</li> <li><strong>element_info/element_percent_coverage/*tsv.gz</strong> - Tables containing the percent genome coverage of genes and pseudogenes, by accession and averaged over accessions per species separately.</li> <li><strong>example_Mycoplasmopsis_bovis_panaroo_output.csv.gz</strong> - Panaroo output table for <em>Mycoplasmopsis bovis</em>, which was used for an example. Corresponds to the <em>gene_presence_absence.csv</em> file in the raw Panaroo output.</li> <li><strong>focal_and_non.focal_full_to_short.tsv.gz</strong> - Mapfile of full to short (and unique) species ids used in analysis. Primarily to include species ids in cluster names without making them unnecessarily long.</li> <li><strong>genome_info/accessions.tsv.gz</strong> - Genome accessions used for broad pangenome analysis (note that not all genome accessions could be downloaded [and were ignored], which is indicated in the "could_download" column).</li> <li><strong>genome_info/genome_sizes.tsv.gz</strong> - Sizes of all genomes used for the broad pangenome analysis.</li> <li><strong>metrics_additional_subsamples.tsv.gz</strong> - Contains columns also found in the <em>pangenome_and_related_metrics.tsv.gz</em> file below, but based on genome subsamplings of 3 and 20, rather than 9.</li> <li><strong>model_output/pangenome_linear_models.rds</strong> - R Data Serialization files containing the output of R linear model objects (generated by lm and provided as an R list object). There are separate elements in the list for the mean number of genes, genomic fluidity, percentage singletons (si), and si/sp.</li> <li><strong>model_output/linear_model_coef.tsv.gz</strong> - Coefficient summary table for all linear models.</li> <li><strong>pangenome_and_related_metrics.tsv.gz</strong> - Metrics used for broad pangenome analysis across 670 prokaryotic species. Note that this table was filtered down to 668 species after excluding those with < 9 genomes.</li> <li><strong>pangenome_and_related_metrics_filt.tsv.gz</strong> - Filtered table, as described above.</li> <li><strong>taxonomy.tsv.gz</strong> - Taxonomy for all species used for this analysis, taken from GTDB. Row names are species names.</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>indepth_10_species_analysis/</strong> <ul> <li><strong>cluster_breakdown_tables/</strong> - Folder containing tables providing breakdown of how clusters are distributed by element type, pangenome partition, and species. Provided for easy plotting.</li> <li><strong>cluster_COG_annot.tsv.gz</strong> - Mapping of cluster IDs to COG annotations.</li> <li><strong>cluster_filt_lengths_and_additional.tsv.gz</strong> - Metadata on clusters, most pertinently the length of the representative sequence in the cluster (which was used to filter out some clusters, below the cut-off which pseudogenes could not be called).</li> <li><strong>cluster_member_breakdown.tsv.gz </strong>- Table providing information on each element (called pseudogenes and intact genes) and provides information such as what cluster they are part of, what species and genome accession they are found in, etc.</li> <li><strong>cluster_types.rds</strong> - R Data Serialization file containing R list providing breakdown of all clusters into categories (intact/pseudogene/mixed, where mixed means containing both pseudogene and intact elements).</li> <li><strong>COG_enrichment_results/ultra.cloud-COG-gene-enrichments.tsv.gz</strong> - Output file with enrichment test summaries for COG IDs in significant COG categories, which was run for the ultra-cloud pangenome partition model only.</li> <li><strong>element_glmm_input.tsv.gz </strong>- Table containing all information used for fitting generalized linear mixed models.</li> <li><strong>focal_species.txt</strong> - Names of species used for the in-depth analysis.</li> <li><strong>genome_info/ </strong>- Folder containing the genome accessions (and the corresponding genome sizes) for all ten analyzed species.</li> <li><strong>glmm_output/</strong> - Folder containing R Data Serialization files containing output R objects after fitting generalized linear mixed models (only ultra-rare files are present, due to file size constraints).</li> <li><strong>per_genome_element.type_percent_coverages.rds</strong> - R Data Serialization file containing R list providing the percent coverage by intact genes vs pseudogenes per accession (nested by species)</li> </ul> </li> </ul>
Keccak reference data
<p>This dataset contains all data referring to the Keccak implementation used in the reference implementation of Elephant. Specifically, it contains: </p><ul><li>The source code after being processed by CIL </li><li>The CIL code after being manually processed further </li><li>The while+ code after being transformed from CIL </li><li>The while+ code, after being manually processed further </li><li>The annotated program resulting from the first pass derivation rules being applied on the while+ code </li><li>The annotated programs resulting from the second pass derivation rules being applied on the results of the first pass </li></ul><p>Note that the second pass derivation trees also contain a small amount of natural language, to explain what is being done, apart from simply applying the derivation rules.</p>
Data from: Memory-bound k-mer selection for large and evolutionary diverse reference libraries
Open the record for dataset details and reuse information.
Data from: Refinement of the Antarctic fur seal (Arctocephalus gazella) reference genome increases continuity and completeness
Open the record for dataset details and reuse information.
Data from: A global FAOSTAT reference database of cropland nutrient budgets and nutrient use efficiency (1961–2020): nitrogen, phosphorus and potassium
Open the record for dataset details and reuse information.
Data from: De novo reference genome of a Geomyid rodent, Botta’s pocket gopher (Thomomys bottae)
Open the record for dataset details and reuse information.
Spatial reference data for the Central Arizona-Phoenix area: change in elevation
This dataset has been created to support the research activities of Arizona State University researchers. The slope grid identifies the rate of maximum change in elevation value from each grid cell and is calculated in degrees. The data are derived from the 30 meter Digital Elevation Model created for the Central Arizona-Phoenix Long Term Ecological Research program (dem30_utm).
Coweeta Hydrologic Laboratory and Basin Reference GIS data
Coweeta Basin reference GIS data that were either commissioned or produced by the USDA Forest Service Coweeta Hydrologic Laboratory or the Coweeta LTER starting in the late 1980s.
Namibian reference ochres description and geochemical data
<p>The datasets present the macroscopic description and ICP-OES and ICP-MS geochemical data of Namibian reference ochres. </p>
Data From: TERRA-REF, An open reference data set from high resolution genomics, phenomics, and imaging sensors
<p>The ARPA-E funded TERRA-REF project is generating open-access reference datasets for the study of plant sensing, genomics, and phenomics. Sensor data were generated by a field scanner sensing platform that captures color, thermal, hyperspectral, and active flourescence imagery as well as three dimensional structure and associated environmental measurements. This dataset is provided alongside data collected using traditional field methods in order to support calibration and validation of algorithms used to extract plot level phenotypes from these datasets.</p> <p>Data were collected at the University of Arizona Maricopa Agricultural Center in Maricopa, Arizona. <br> This site hosts a large field scanner with fifteen sensors, many of which are capable of capturing mm-scale images and point clouds at daily to weekly intervals.</p> <p>These data are intended to be re-used, and are accessible as a combination of files and databases linked by spatial, temporal, and genomic information. In addition to providing open access data, the entire computational pipeline is open source, and we enable users to access high-performance computing environments.</p> <p>The study has evaluated a sorghum diversity panel, biparental cross populations, and elite lines and hybrids from structured sorghum breeding populations. <br> In addition, a durum wheat diversity panel was grown and evaluated over three winter seasons.<br> The initial release includes derived data from from two seasons in which the sorghum diversity panel was evaluated.<br> Future releases will include data from additional seasons and locations.</p> <p>The TERRA-REF reference dataset can be used to characterize phenotype-to-genotype associations, on a genomic scale, that will enable knowledge-driven breeding and the development of higher-yielding cultivars of sorghum and wheat. <br> The data is also being used to develop new algorithms for machine learning, image analysis, genomics, and optical sensor engineering.</p>
Data set with reference scenarios
<p>Data set with reference scenarios. As it is not possible to include the entire dataset in this report, we only include two Tables on final energy demand. Table 1 shows the Final Energy Demand projections per industrial subsector and EU28 country in the Reference Scenario and Table 2 the Final Energy Demand projections per industrial subsector and EU28 country in the Frozen Efficiency Scenario. The full dataset, including physical production (in ktonnes) and fuel and electricity demand (in TJ) per industrial sub-sector, per fuel type and per EU 28 country is available upon request to the project coordinator.</p>
Data set on Energy Efficiency potentials on top of reference scenarios
<p>As it is not possible to include the entire dataset in this report, we only include the Energy Efficiency potentials for Austria. The full dataset, showing the savings potentials for all EU27 (and UK) countries, is available upon request to the project coordinator.</p>
Neurogenomic divergence during speciation by reinforcement of mating behaviors in chorus frogs (Pseudacris) – De novo reference transcriptome raw data, contigs and gene annotations
<p>RNA-Seq raw data used in the assembly and annotation of a reference transcriptome for the Upland Chorus Frog, <em>Pseudacris feriarum</em>. Raw data were obtained by sequencing of four tissue types: Brain, eyes, testis, and somatic. Assembled contigs (Trinity) and gene annotations (Trinotate) are also provided.</p>
Data from: Hare pseudo-reference genome from: the genomic impact of historical hybridization with massive mitochondrial DNA introgression
<p><b>Background:</b> The extent to which selection determines interspecific patterns of genetic exchanges enlightens the role of adaptation in evolution and speciation. Often reported extensive interspecific introgression could be selection-driven, but also result from demographic processes, especially in cases of invasive species replacements, which can promote introgression at their front. Because invasion and selective sweeps similarly mold variation, population genetics evidence for selection can only be gathered in an explicit demographic framework. The Iberian hare, <i>Lepus granatensis</i>, displays in its northern range extensive mitochondrial DNA introgression from <i>L. timidus</i>, an arctic/boreal species that it replaced locally after the last glacial maximum. We use whole-genome sequencing to infer geographic and genomic patterns of nuclear introgression and fit a neutral model of species replacement with hybridization, allowing us to evaluate how selection influenced introgression genome-wide, including for mtDNA.</p> <p><b>Results:</b> Although the average nuclear and mtDNA introgression patterns are strongly contrasted, they fit a single neutral model of post-glacial invasive replacement of <i>timidus</i> by <i>granatensis</i>. Outliers of elevated introgression include several genes related to immunity, spermatogenesis, and mitochondrial metabolism. Introgression is reduced on the X-chromosome and in low recombining regions.</p> <p><b>Conclusion:</b> General nuclear and mtDNA patterns of introgression can be explained by purely demographic processes. Hybrid incompatibilities and interplay between selection and recombination locally modulate levels of nuclear introgression. Selection promoted introgression of some genes involved in conflicts, either interspecific (parasites) or possibly cytonuclear. In the latter case, nuclear introgression could mitigate the potential negative effects of alien mtDNA on mitochondrial metabolism and male-specific traits.</p>
Data from: Diversity and evolution of the transposable element repertoire in arthropods with particular reference to insects
Background: Transposable elements (TEs) are a major component of metazoan genomes and are associated with a variety of mechanisms that shape genome architecture and evolution. Despite the ever-growing number of insect genomes sequenced to date, our understanding of the diversity and evolution of insect TEs remains poor. Results: Here, we present a standardized characterization and an order-level comparison of arthropod TE repertoires, encompassing 62 insect and 11 outgroup species. The insect TE repertoire contains TEs of almost every class previously described, and in some cases even TEs previously reported only from vertebrates and plants. Additionally, we identified a large fraction of unclassifiable TEs. We found high variation in TE content, ranging from less than 6 % in the antarctic midge (Diptera), the honey bee and the turnip sawfly (Hymenoptera) to more than 58 % in the malaria mosquito (Diptera) and the migratory locust (Orthoptera), and a possible relationship between the content and diversity of TEs and the genome size. Conclusion: While most insect orders exhibit a characteristic TE composition, we also observed intraordinal differences, e.g., in Diptera, Hymenoptera, and Hemiptera. Our findings shed light on common patterns and reveal lineage-specific differences in content and evolution of TEs in insects. We anticipate our study to provide the basis for future comparative research on the insect TE repertoire.
ArXiv reference data from Paperscape
<p>This dataset contains reference data for the arXiv online repository of scientific papers (http://arxiv.org) as extracted by the Paperscape project (http://paperscape.org). It spans from the beginning of the arXiv (1991) to the end of 2013.</p> <p>The file format is CSV i.e. one line per entry, with 7 fields separated by semicolons:</p> <p>arxiv-id;comma-separated-arxiv-categories;num-found-refs;num-total-refs;comma-separated-refs;comma-separated-authors;title</p> <p>If you use this data please give credit to Damien P. George and Rob Knegjens, Paperscape, and link to the DOI:10.5281/zenodo.10052.</p>
SARWIND LG-Mod (v4.01) output data with reference to the Sentinel-1A (2014/12/07, Camargue), EW, GRD, MR, VV-polarized, descending orbit, SNAP pre-processed data (+ the additional SKYRON reference wind)
<p>A zipped folder containing SARWIND LG-Mod (v4.01) output data obtained by processing the Sentinel-1A (2014/12/07, Camargue), EW, GRD, MR, VV-polarized, descending orbit, SNAP pre-processed data (+ the additional SKYRON reference wind).</p> <p>Pre-processed data derived from the Sentinel-1 SAR image have been obtained by using the Sentinel Application Platform (SNAP), ver. 3.0.3, distributed by the European Space Agency (ESA). The reference wind components have been provided by the Numerical Weather Model (NWM) SKYRON.</p> <p>These Earth Observation (EO) data have been used as inputs to the SARWIND LGMod algorithm, ver. 4.01, developed by Rana Fabio Michele (2014-2016) for the sea surface wind field retrieval.</p> <p>The available Sentinel-1, Extra Wide Swath Mode, Ground Range, Multi-look, Detected, Medium Resolution, Vertical polarisation on transmit, Vertical polarisation on receive, Descending orbit, C-band SAR image, has been acquired on the 7th Dec 2014, 05:51:54.</p>
Sentinel-1A (2014/12/07, Camargue), EW, GRD, MR, VV-polarized, descending orbit, SNAP pre-processed data (+ additional SKYRON reference wind) for SARWIND LG-Mod (v4.01)
<p>A zipped folder containing pre-processed data from a Sentinel-1 SAR image by using the Sentinel Application Platform (SNAP), ver. 3.0.3, distributed by the European Space Agency (ESA). The reference wind components provided by the Numerical Weather Model (NWM) SKYRON is also added in the folder.</p> <p>These Earth Observation (EO) data can be used as inputs to the SARWIND LGMod algorithm, ver. 4.01, developed by Rana Fabio Michele (2014-2016), with the aim at retrieving the sea surface wind field by exploiting the single co-polarized Sentinel-1 image available.</p> <p>In particular, the latter is the Sentinel-1, Extra Wide Swath Mode, Ground Range, Multi-look, Detected, Medium Resolution, Vertical polarisation on transmit, Vertical polarisation on receive, Descending orbit, C-band SAR image, acquired on the 7th Dec 2014, 05:51:54</p>
Sentinel-1A (2014/12/31, Camargue), EW, GRD, MR, VV-polarized, descending orbit, SNAP pre-processed data (+ additional SKYRON reference wind) for SARWIND LG-Mod (v4.01)
<p>A zipped folder containing pre-processed data from a Sentinel-1 SAR image by using the Sentinel Application Platform (SNAP), ver. 3.0.3, distributed by the European Space Agency (ESA). The reference wind components provided by the Numerical Weather Model (NWM) SKYRON is also added in the folder.</p> <p>These Earth Observation (EO) data can be used as inputs to the SARWIND LGMod algorithm, ver. 4.01, developed by Rana Fabio Michele (2014-2016), with the aim at retrieving the sea surface wind field by exploiting the single co-polarized Sentinel-1 image available.</p> <p>In particular, the latter is the Sentinel-1, Extra Wide Swath Mode, Ground Range, Multi-look, Detected, Medium Resolution, Vertical polarisation on transmit, Vertical polarisation on receive, Descending orbit, C-band SAR image, acquired on the 31st Dec 2014, 05:51:53.</p>
ScienceDex guides
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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.