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7,195
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7,195 results for “SET”
Climate, snow, and soil moisture data set for the Tuolumne and Merced River watersheds, California, USA
Open the record for dataset details and reuse information.
RNA seq data set of chick, quail, and duck mandibular primordia at embryonic stage (HH) 37
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Experimental test of the combined effects of water availability and flowering time on pollinator visitation and seed set
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Data set for: Experimentally reduced feather microbial loads improve reproductive performance in captive Zebra Finches
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The WeAllWalk Data Set
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S-BLE: A participatory BLE sensory data set recorded from real-world bus travel events
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Urban Forest Effects Model (UFORE) to calculate forest structure and function from sample ground data. Two part set.
Within the City of Baltimore, 195 permanent 1/10 circular plots were established based on a stratified random sample among land uses in 1999. These plots were re-measured in 2004 and 2009 and will be re-measured again in 2014. On each plot, all trees (as defined as woody vegetation with a stem diameter at 4.5 ft (dbh) greater than one-inch) are recorded. For each tree, data are recorded on species, dbh, height, crown width, condition, crown competition, percent canopy missing and distance and direction to nearby residential buildings. These data are analyzed using the i-Tree model (www.itreetools.org) to assess ecosystem services and values. However, more importantly, these plots along with a comparable set of plots established in Syracuse, NY in 1999 are the first and most spatially comprehensive set of long-term urban forest monitoring data within cities globally. These data are being used to understand how urban forest structure and associated ecosystem services are changing through time in the City of Baltimore.
Evaluation of global forcing data sets for hydropower inflow simulation in Nepal
<p>We are thankful to the Department of Hydrology and Meteorology, Government of Nepal for providing observed hydrological and Meteorological data sets for this study. Some of the data presented here are subject of copyright so we request to contact author (corresponding author: bikasbhattarai@gmail.com) for further use for publication. </p>
Microsoft Academic data set for RPYS analysis
<p>Microsoft Academic data set for RPYS analysis, used in "Discovering seminal works with marker papers", see https://arxiv.org/abs/1901.07352</p> <p>Please see also:</p> <p>https://aka.ms/msracad</p> <p>Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June (Paul) Hsu, and Kuansan Wang.<br> 2015. An Overview of Microsoft Academic Service (MA) and Applications. In Proceedings of the 24th<br> International Conference on World Wide Web (WWW '15 Companion). ACM, New York, NY, USA,<br> 243-246. DOI=http://dx.doi.org/10.1145/2740908.2742839</p>
Set of European CO2 and CO emission grids representing emission uncertainties
<p>This dataset was prepared by TNO as a contribution to the H2020 project CHE and the H2020 project VERIFY. The basis is a high-resolution (~1x1 km) emission inventory providing CO<sub>2</sub> and CO (from fossil fuels and biofuels separately) over western Europe (2ºW - 19ºE, 47ºN - 56ºN). The reported emissions by European countries to UNFCCC (CO<sub>2</sub>) and to EMEP/CEIP (CO) have been used and where needed gap-filled or replaced with emission data from the GAINS model. These country-level emissions are disaggregated in space using a consistent spatial distribution methodology, whereas large point sources are listed with their exact locations. This approach is similar to the one described by Kuenen et al., (ACP, 2014). Emissions are reported per GNFR sector, with an extra split for road transport.</p> <p>The emission grids that are part of this dataset are a variation on the base grid, representing the uncertainty in the emission data. Each grid is equally plausible. The grids have been created using a Monte Carlo approach. The uncertainties in the underlying data used to create the base grid (emissions: activity data and emission factors, spatial proxies) have been collected (either from country reports or based on expert judgement). Through the Monte Carlo simulation these uncertainties, taking into account error correlations between some sub-sectors, are combined to create ten new emission grids. The spread in emissions between these emission maps gives an indication of the uncertainty in the emissions.</p> <p>The grid files (in .csv and .nc format) contain annual total emissions per grid cell for the year 2015. A separate file has been prepared for each ensemble member in the Monte Carlo simulation (indicated with M). The unit in the files is kg/yr.</p> <p>A detailed description of the Monte Carlo simulation is presented in:</p> <p>Super, I., Dellaert, S. N. C., Visschedijk, A. J. H., and Denier van der Gon, H. A. C.: Uncertainty analysis of a European high-resolution emission inventory of CO<sub>2</sub> and CO to support inverse modelling and network design, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-696, in review, 2019.</p> <p><strong>N.B. It is important to note that 10 maps are not sufficient to describe the sometimes complex uncertainty structures, for example in the case of lognormal uncertainty distributions. The interpretation of the uncertainty based on these 10 maps should therefore be done with care.</strong></p> <p><strong>NB. Despite efforts to prevent negative emissions to occur in the grid maps, some negative values are still present. In local studies this might cause some issues, and we recommend to set negative emissions to zero in those cases.</strong></p>
Internal Wave Bolus Data Set
<p>Image files for experiments of internal wave boluses. Stratification data for the experiments is also included.</p>
Data set: Grain Reynolds number scale effects in dry granular slides
<p>Scale series of velocity, flow depth and run out data for dry granular materials flowing down a slope with side walls. </p>
mOTUs 1.1 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63
<strong>Software: </strong>mOTUs<br><strong>SoftwareVersion: </strong>1.1<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> http://www.bork.embl.de/software/mOTUs1/<br><strong>DockerImage:</strong> stefanjanssen/docker_profiling_tools:motu<br><strong>IsBiobox:</strong> True<br><strong>BioboxYAMLFile:</strong> https://zenodo.org/record/3629567/files/biobox.yaml?download=1<br><strong>ReferenceDatabase:</strong> mOTU.v1.padded<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandsUsed:</strong> docker run \<br>--volume="/path/to/19122017_mousegut_scaffolds_yaml:/bbx/mnt/yaml:ro" \<br>--volume="/path/to/19122017_mousegut_scaffolds:/bbx/mnt/input:ro" \<br>--volume="/path/to/output:/bbx/mnt/output:rw" \<br>--volume="/path/to/output/metadata:/bbx/metadata:rw" \<br>--volume="/path/to/output/cache:/cache:rw" \<br>stefanjanssen/docker_profiling_tools:motu
FOCUS 0.31 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63
<strong>Software: </strong>FOCUS<br><strong>SoftwareVersion: </strong>0.31<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://github.com/metageni/FOCUS<br><strong>DockerImage:</strong> stefanjanssen/docker_profiling_tools:focus<br><strong>IsBiobox:</strong> True<br><strong>BioboxYAMLFile:</strong> https://zenodo.org/record/3629567/files/biobox.yaml?download=1<br><strong>ReferenceDatabase:</strong> k8_bacterial_and_draft<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandsUsed:</strong> docker run \<br>--volume="/path/to/19122017_mousegut_scaffolds_yaml:/bbx/mnt/yaml:ro" \<br>--volume="/path/to/19122017_mousegut_scaffolds:/bbx/mnt/input:ro" \<br>--volume="/path/to/output:/bbx/mnt/output:rw" \<br>--volume="/path/to/output/metadata:/bbx/metadata:rw" \<br>--volume="/path/to/output/cache:/cache:rw" \<br>stefanjanssen/docker_profiling_tools:focus
MetaPhlAn 2.9.21 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63
<strong>Software: </strong>MetaPhlAn<br><strong>SoftwareVersion: </strong>2.9.21<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://bitbucket.org/biobakery/metaphlan2<br><strong>DockerImage:</strong> cami/metaphlan:2.9.21<br><strong>IsBiobox:</strong> True<br><strong>BioboxYAMLFile:</strong> https://zenodo.org/record/3629567/files/biobox.yaml?download=1<br><strong>ReferenceDatabase:</strong> mpa_v29_CHOCOPhlAn_201901 <br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandsUsed:</strong> docker run \<br>--volume="/path/to/19122017_mousegut_scaffolds_yaml:/bbx/mnt/yaml:ro" \<br>--volume="/path/to/19122017_mousegut_scaffolds:/bbx/mnt/input:ro" \<br>--volume="/path/to/output:/bbx/mnt/output:rw" \<br>--volume="/path/to/output/metadata:/bbx/metadata:rw" \<br>--volume="/path/to/output/cache:/cache:rw" \<br>--volume="/path/to/reference_database:/exchange/db:rw" \<br>cami/metaphlan:2.9.21
MetaPhlAn 2.2.0 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63
<strong>Software: </strong>MetaPhlAn<br><strong>SoftwareVersion: </strong>2.2.0<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://bitbucket.org/biobakery/metaphlan2<br><strong>DockerImage:</strong> stefanjanssen/docker_profiling_tools:metaphlan2<br><strong>IsBiobox:</strong> True<br><strong>BioboxYAMLFile:</strong> https://zenodo.org/record/3629567/files/biobox.yaml?download=1<br><strong>ReferenceDatabase:</strong> mpa_v20_m200<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandsUsed:</strong> docker run \<br>--volume="/path/to/19122017_mousegut_scaffolds_yaml:/bbx/mnt/yaml:ro" \<br>--volume="/path/to/19122017_mousegut_scaffolds:/bbx/mnt/input:ro" \<br>--volume="/path/to/output:/bbx/mnt/output:rw" \<br>--volume="/path/to/output/metadata:/bbx/metadata:rw" \<br>--volume="/path/to/output/cache:/cache:rw" \<br>stefanjanssen/docker_profiling_tools:metaphlan2
CAMIARKQuikr 1.0.0 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63
<strong>Software: </strong>CAMIARKQuikr<br><strong>SoftwareVersion: </strong>1.0.0<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://doi.org/10.5281/zenodo.1730572<br><strong>DockerImage:</strong> stefanjanssen/docker_profiling_tools:quickr<br><strong>IsBiobox:</strong> True<br><strong>BioboxYAMLFile:</strong> https://zenodo.org/record/3629567/files/biobox.yaml?download=1<br><strong>ReferenceDatabase:</strong> https://doi.org/10.5281/zenodo.1730572<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandsUsed:</strong> docker run \<br>--volume="/path/to/19122017_mousegut_scaffolds_yaml:/bbx/mnt/yaml:ro" \<br>--volume="/path/to/19122017_mousegut_scaffolds:/bbx/mnt/input:ro" \<br>--volume="/path/to/output:/bbx/mnt/output:rw" \<br>--volume="/path/to/output/metadata:/bbx/metadata:rw" \<br>--volume="/path/to/output/cache:/cache:rw" \<br>stefanjanssen/docker_profiling_tools:quickr
DAS Tool 1.1.2 genome binning of the CAMI 2 Mouse Gut Toy data set, samples 0-63, gold standard pooled assembly
Genome binning of the gold standard pooled assembly. Refinement of the binning output of MaxBin 2.2.7, MetaBAT 2.12.1, CONCOCT 1.0.0, and DAS Tool 1.1.2.<br><strong>Software: </strong>DAS Tool<br><strong>SoftwareVersion: </strong>1.1.2<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://github.com/cmks/DAS_Tool<br><strong>DockerImage:</strong> cami/das_tool:1.1.2<br><strong>IsBiobox:</strong> No<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandUsed:</strong> DAS_Tool -i binning_concoct1.0.0,binning_maxbin2.2.7,binning_metabat2.12.1 -c anonymous_gsa_pooled.fasta -o output --search_engine diamond
CONCOCT 1.0.0 genome binning of the CAMI 2 Mouse Gut Toy data set, samples 0-63, gold standard pooled assembly
Genome binning of the gold standard pooled assembly <br><strong>Software: </strong>CONCOCT<br><strong>SoftwareVersion: </strong>1.0.0<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://github.com/BinPro/CONCOCT<br><strong>DockerImage:</strong> quay.io/biocontainers/concoct:1.0.0--py37h88e4a8a_5<br><strong>IsBiobox:</strong> No<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandUsed:</strong> for i in {0..63}; do bowtie2 -q --threads 30 --fr -x anonymous_gsa_pooled.fasta --interleaved sample_${i}/anonymous_reads.fq -S anonymous_reads_sample_${i}.sam ; done<br>for i in {0..63}; do samtools view -b sample_${i}.sam -o anonymous_reads_sample_${i}.bam & done<br>for i in {0..63}; do samtools sort anonymous_reads_sample_${i}.bam -o anonymous_reads_sample_${i}.sorted.bam ; done<br>for i in {0..63}; do samtools index anonymous_reads_sample_${i}.sorted.bam ; done<br>cut_up_fasta.py anonymous_gsa_pooled.fasta -c 10000 -o 0 --merge_last -b contigs_10K.bed > contigs_10K.fa<br>concoct_coverage_table.py contigs_10K.bed /host/benchmarking/fmeyer/output/bowtie2/mouse_gut/sorted_bam/anonymous_reads_sample_*.sorted.bam > coverage_table.tsv<br>concoct --composition_file contigs_10K.fa --coverage_file coverage_table.tsv -b<br>merge_cutup_clustering.py clustering_gt1000.csv > clustering_merged.csv
MEGAN 6.15.2 taxonomic binning of the CAMI 2 Mouse Gut Toy data set, gold standard pooled assembly
<p>Taxonomic binning of the gold standard pooled assembly<br> <strong>Software: </strong>MEGAN<br> <strong>SoftwareVersion: </strong>6.15.2<br> <strong>DataURL: </strong> https://data.cami-challenge.org/participate<br> <strong>SoftwareURL:</strong> https://github.com/husonlab/megan-ce<br> <strong>ReferenceDatabase:</strong> NCBI nr 2019-04-17<br> <strong>Taxonomy:</strong> NCBI 2019-04-17<br> <strong>ShortReadsUsed:</strong> False<br> <strong>LongReadsUsed:</strong> False<br> <strong>CommandUsed:</strong> diamond makedb --in nr.faa.gz --db nr_diamond0.9.24 --taxonmap prot.accession2taxid.gz --taxonnodes nodes.dmp<br> diamond blastx -d nr_diamond0.9.24 -q anonymous_gsa_pooled.fasta -a result -t tmp --index-chunks 1<br> megan/tools/daa2rma -i result.daa -o 19122017_mousegut_scaffolds.rma --acc2taxa megan/prot_acc2tax-Nov2018X1.abin<br> #final bins extracted with MEGAN's graphical interface tool</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.