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10,119 results for “IT Support”
Data Supporting: "Phytoplankton as Indicators of Global Warming?"
This data package provides monthly time series of measurements made in San Francisco Bay from 1975-2021 and analyzed in the article "Phytoplankton as Indicators of Global Warming?" submitted to Limnology and Oceanography Letters.
Sacramento Splittail Collections in 2022 in Support of Selenium Exposure Studies
Sacramento Splittail (Pogonichthys macrolepidotus) were collected in 2022 for studies investigating selenium exposure. A total of 18 adult individuals were collected from Suisun Bay and a total of 36 juvenile individuals were collected from various locations in the Sacramento-San Joaquin Delta. All of the collected individuals superficially appeared healthy and did not exhibit any spinal deformities.
Baseline Water Quality Monitoring to support restoration efforts in the Cache Slough Complex, 2013-2021.
The Cache Slough Complex is an ecologically significant area, with many restoration efforts of various kinds set to take place within its boundaries. These efforts which are tied to regulations set forth by the Biological Opinion, will have unknown impacts to in‐stream water quality. For this reason, the Department of Water Resources’ Water Quality Assessment (WQA) unit developed a monitoring program to establish baseline water quality measurements to help understand the environmental impact of these restoration projects and ensure pre‐restoration water quality data is available to compare against post-restoration water quality. To do so, data was collected at various sites that represent all major inputs and outflows within the Cache Slough Complex from September 2013 to December 2021.
Data to support manuscript: A Comparison of Two ROV Survey Methods Used to Estimate Fish Assemblages and Densities Around a California Oil Platform
This fish dataset was collected at platform Harmony, between Aug 25 and Aug 26, 2018. The ROV fish survey was done with two methods: industry (mimicking an inspection survey conducted for platform structural inspections) and biological method. Due to differences between the two methods in the positioning of the ROV vis a vis the jacket crossbeams, fish densities and diversities were different between the two methods at the platform bottom. This dataset provides fish counts and the relevant survey parameters (transect length and depth).
SBC LTER: REEF: Data to support "Niche Complementarity and Resistance to Grazing Promote the Invasion Success of Sargassum horneri in North America"
These data describe the results of surveys and manipulative experiments performed to investigate how niche complementarity, competition, and herbivory influence the success of the invasive seaweed Sargassum horneri. This data package includes five data tables and they are used to support the manuscript: Marks LM, Reed DC, Holbrook SJ (2020) Niche Complementarity and Resistance to Grazing Promote the Invasion Success of Sargassum horneri in North America. Diversity, 12(2)
SBC LTER: Beach: Data to support "Contribution of macroalgal wrack consumers to dissolved inorganic nitrogen concentrations in intertidal pore waters of sandy beaches"
These data describe measures of excretion from talitrid amphipods (Megalorchestia corniculata) fed giant kelp (Macrocystis pyrifera) blades and incubated in a series of mesocosms during May 2018. Data are contained in one table: time series of mesocosm porewater nutrient concentrations measured twice daily for approximately one week (two full trials included in this dataset). This dataset is to support the article: Lowman, H. E., Emery, K. A., Kubler-Dudgeon, L., Dugan, J. E., & Melack, J. M. (2019). Contribution of macroalgal wrack consumers to dissolved inorganic nitrogen concentrations in intertidal pore waters of sandy beaches. Estuarine, Coastal and Shelf Science. https://doi.org/10.1016/j.ecss.2019.02.004
Offline replay supports planning in human reinforcement learning
Open the record for dataset details and reuse information.
Supporting data and code for: Electron-electron collision effects on ISR temperature measurements
<p>CSV files are processed simulation output from EPPIC. Read these files with eppic_csvread.m, which also reads in the Brownian collision spectra, neb_th631.mat.</p> <p>singleparticle5.m is the code used for single-particle simulations of ISR spectra</p> <p>eppic_input_deck.i is the input file used for collisional ISR runs with EPPIC, see manuscript for Github link of EPPIC.</p> <p>electrostatic_meteor_ablation_sim-master.zip contains an archive of the EPPIC code when it was run, see manuscript for a Github link for the current version of EPPIC</p>
Supporting data and code for: Millstone Hill ISR Measurements of Small Aspect Angle Spectra
<p>Main code is contained in Matlab .m files. Path dependencies may need to be fixed but there are no duplicate file names.</p> <p>acf_data_plot.m is used to plot the ACFs measured by Millstone</p> <p>acf_fitting.m is used to fit the Millstone ACFs to the Brownian theory. Calls isr_theory_fn.m to calculate Brownian ACF</p> <p>acf_fitting_plots.m plots the fitted and measured ACFs</p> <p>acf_fitting_plots_spcomp.m plots the ACFs from the single-particle simulations</p> <p>acf_fitting_plots_piccomp.m plots the ACFs from the PIC code</p> <p>sp_fit_to_brownian.m fits the single-particle simulations to Brownian theory</p> <p> </p> <p>The single particle simulations are produced by singleparticle5.m. The full ACFs are then calculated by running merge.m to combine the simulation results, gordeyev.m to calculate the electron Gordeyev integral, Ji_calc.m to calculate the ion Gordeyev integral, and finally isr_Jtoneb.m to calculate the spectra. The resulting spectra are included and labeled as 'neb_sp12_Te_Ti.mat' for the different electron and ion temperatures. The Brownian spectra for the same parameters are labeled as 'neb440_Te_Ti.mat'. The frequencies used in calculating the electron Gordeyev integrals may be needed for plotting and are labeled as 'wJe_12_Te_Ti.mat'.</p> <p> </p> <p>The data are labeled by the date, Nov_2013, Feb_2019, or Apr_2019. An appendix of 'avg' indicates the file averaged the ACFs across multiple range gates. The measured ACFs are all labeled 'acf_pos?_date_avg_fitted.h5', where pos? refers to the antenna pointing direction. The pointing directions and associated parameters are located in 'looker?_date_avg.csv'. The IRI model results for each experiment are in the files 'iri?_date_avg.m'.</p> <p> </p> <p>The spectra from PIC simulations are included as 'k*_T1150_N5e10_nz.mat', where * is an aspect angle index. The matching k* and aspect angles in degrees are {k4, 0.8}, {k6, 1.2}, {k11, 2}, {k15, 2.8}, {k20, 3.6}, {k24, 4.4}, {k28, 5}, {k29, 5.2}, {k34, 6}, {ky, 0}, {kx, 90}. The files 'neb_spPIC_fb_Te1145_Ti1145.mat' and 'neb440_Te1145_Ti1145.mat' are respectively the single-particle and Brownian spectra calculated with the same parameters as the PIC code. </p> <p>electrostatic_meteor_ablation_sim-master.zip contains an archive of the EPPIC code when it was run, see manuscript for a Github link for the current version of EPPIC</p>
SpaCy affixes: SpaCy support for affixes splitting for Freeling-like affixes rules and dictionaries.
<p>This library was born to split clitics from verbs so POS tagging works out-of-the-box with spaCy models.</p>
Data supporting "Leaf carbon and water status control stomatal and non-stomatal limitations of photosynthesis in trees."
<p>Data supporting our New Phytologist publication: "Leaf carbon and water status control stomatal and non-stomatal limitations of photosynthesis in trees"</p> <p> </p>
Diurnal cycle of the semi-direct effect from a persistent absorbing aerosol layer over marine stratocumulus in large-eddy simulations: supporting dataset
<p>Relevant data for reproducing figures and tables from Herbert et al., 2020, Atmospheric Chemistry and Physics: "Diurnal cycle of the semi-direct effect from a persistent absorbing aerosol layer over marine stratocumulus in large-eddy simulations".</p> <p>The tar file contains the relevant netcdf files that contain simulation output from the MET Office large eddy model (LEM). Each netcdf file corresponds to a single model setup and experiment - details of which can be found within the published manuscript. Information concerning the LEM, and details on how to obtain access to the code, can be found at http://appconv.metoffice.com/LEM/index.html.</p> <p>The file 'relevant_information.pdf' contains two tables. The first provides the names of each netcdf file that was used to produce each figure in the manuscript. The second provides the metadata for the netcdf variables that were used to produce the figures and tables.</p> <p>The netcdf filename provides information on the experiment setup as follows:</p> <p>run-identifier-number _ length-of-run _ AOD-of-layer _ GAP-between-cloud-and-layer _ THICKNESS-of-layer _ 'bigarray' _ special-setups-for-sensitivity-experiments</p> <p>For example, r1360_8day_transient_aod02_0dz_250th_bigarray_nodrizz is run number r1360, simulating 8 days, with the aerosol layer properties AOD=0.2, cloud-to-aerosol gap of 0m, layer thickness of 250m, in the sensitivity setup where precipitation has been switched off.</p>
GWAS summary statistics imputation support data and integration with PrediXcan MASHR
<p># GWAS summary statistics imputation, integration with PrediXcan MASHR-M</p> <p> </p> <p>The file `sample_data.tar` contains all necessary files to perform imputation of GWAS summary statistics to the GTEx v8 QTL data set.</p> <p>It includes 1000 Genomes individuals' genotypes as reference panel.</p> <p>The `.tar` archive, upon uncompression, contains the following folder structure:</p> <p>```</p> <p>data<br> |-- coordinate_map<br> |-- gwas<br> |-- liftover<br> |-- models<br> | |-- eqtl<br> | | `-- mashr<br> | `-- sqtl<br> | `-- mashr<br> |-- reference_panel_1000G<br> `-- ucsc</p> <p>```</p> <p> </p> <p>`data/eur_ld.bed.gz` contains definitions of approximately independent LD-regions in hg38 (Berisa-Pickrell regions, lifted over)</p> <p>`data/gtex_v8_eur_filtered_maf0.01_monoallelic_variants.txt.gz` is a snp annotation file, listing all GTEx v8 variants with MAF>0.01 in europeans.</p> <p>`data/coordinate_map` contains precomputed mapping tables that MetaXcan tools can use to convert GWAS' genomic coordinates in GWAS between genome assemblies.</p> <p>`data/gwas` contains a sample GWAS file for the purposes of a tutorial (data obtained from Nikpay et al (Nat Gen 2016) https://www.ncbi.nlm.nih.gov/pubmed/26343387</p> <p>`data/liftover` contains Liftover chains to map coordinates between human genome assemblies (used by full harmonization tools)</p> <p>`data/models` contains PrediXcan MASHR-M models, and cross-tissue S-MultiXcan LD compilation, from eQTL and sQTL.</p> <p>`data/reference_panel_1000G` contains 1000G hg38 genotypes, in parquet format, to be used by imputation tools.</p> <p>`data/ucsc` contains genomic coordinates of rsids in hg17, hg18 and hg19. You can use these to add chromosome and start position information to a GWAS based on its rsids. (column `end` is not used)</p> <p> </p>
Supporting information for IUCr survey on raw data archival and reuse in chemical crystallography
<p>Supporting information i.e. questions, responses and raw data, relating to a study into raw data management and availability in small molecule crystallography conducted under the auspices of the International Union of Crystallography Committee on Data.</p> <p>It is now common to deposit structure factors when publishing, which means that the small molecule crystallography community caters very well for routine structures. However this is generally only the case if everything in a raw image is fully and/or properly accounted for and the model is correct or appropriate. So for example, in some cases raw data may no longer be required, while in others it may be necessary to validate or ‘do better’ in the future. Moreover there are increasing pressures from bodies e.g. funders to make the data relating to research outputs Findable, Accessible, Interoperable and Reusable (FAIR). In acknowledgement of this situation and in order to begin addressing it, IUCr Journals now facilitate access to and citation of large raw diffraction datasets in its articles. Therefore it is important for our community understand and define how we manage our raw data in this respect.</p> <p>As Members of the IUCr Committee on Data we see the need to conduct this survey about exploring raw data archival<br> practice and gathering opinions as to if/how raw data could/should be used if it were to be made more widely<br> available.</p>
Supporting data for "Towards an ultra-high-speed combustion pyrometer" paper
<p>Model and experimental data presented in "Towards an ultra-high-speed combustion pyrometer" paper for the <em>International Journal of Turbomachinery, Propulsion and Power </em>and at the 9th EVI-GTI International Gas Turbine Instrumentation Conference on 20-21/11/2019 in Graz (Austria).</p> <p>Each file contains data relative to a figure/table in the journal paper.</p>
Research data supporting "Atomic-Scale Patterning of Arsenic in Silicon by Scanning Tunneling Microscopy"
<p>Research data supporting the publication: Stock, T. J. Z, <em>et. al.</em>, <strong>2020</strong>, Atomic-Scale Patterning of Arsenic in Silicon by Scanning Tunneling Microscopy, <em>ACS Nano</em>, https://dx.doi.org/10.1021/acsnano.9b08943</p>
Datasets and Supporting Materials for the IPIN 2019 Competition Track 3 (Smartphone-based, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2019 Competition (Pisa, Italy).</p><p><strong>Contents:</strong></p><ol><li>IPIN2019_Call4Competition: Call for competition and main rules</li><li>IPIN2019_Track03_TechnicalAnnex: Technical annex describing the Track 3 of the competition.</li><li>01-Logfiles: This folder contains a subfolder with the 50 (40 + 10) training logfiles, a subfolder with the 9 validation logfiles, and a subfolder with the 1 blind evaluation logfile as provided to competitors.</li><li>02-Supplementary_Materials: This folder contains the Matlab/octave parser, the raster maps, the vector maps and the visualization of the training routes.</li><li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 99 evaluation points. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.</li></ol><p><strong>Please, cite the following works when using the datasets included in this package:</strong></p><ul><li>Jiménez, A. R.; Perez-Navarro, A.; Crivello, A.; Mendoza-Silva, G.; Ortiz, M.; Perul, J.; Seco, F. and Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2019 Competition Track 3 (Smartphone-based, off-site), Zenodo 2019. <a href="http://dx.doi.org/10.5281/zenodo.3606765">http://dx.doi.org/10.5281/zenodo.3606765</a></li><li>Potorti, F.; Park, S.; Palumbo, F.; Girolami, M.; Barsocchi, P.; Lee, S.; Torres-Sospedra, J.; Jimenez Ruiz, A. R.; Perez-Navarro, A.; Mendoza-Silva, G. M.; Seco, F.; Ortiz, M.; Perul, J.; Renaudin, V.; Kang, H.; Park, S. Y.; Lee, J. H.; Park, C. G.; Ha, J.; Han, J.; Park, C.; KIM, K.; Lee, Y.; GYE, S.; Lee, K.; Kim, E.; Choi, J.-S.; Choi, Y.-S.; Talwar, S.; Cho, S. Y.; Ben-Moshe, B.; Sansano, E.; Chidlovskii, B.; Kronenwett, N.; Prophet, S.; Landay, Y.; Marbel, R.; Peng, A.; Wu, B.; MA, C.; Poslad, S.; Selviah, D.; Wu, W.; Ma, Z.; Zhang, W.; Wei, D.; Yuan, H.; Jiang, J.-B.; Liu, J.-W.; Su, K.-W.; Leu, J.-S.; Nishiguchi, K.; Bousselham, W.; Uchiyama, H.; Thomas, D.; Shimada, A.; Taniguchi, R.-I.; Cortés, V.; Lungenstrass, T.; Ashraf, I.; Lee, C.; Usman Ali, M.; Im, Y.; Kim, G.; Eom, J.; Hur, S.; Park, Y.; Opiela, M.; Moreira, A.; Nicolau, M. J.; Pendão, C.; Silva, I.; Meneses, F.; Costa, A.; Trogh, J.; Plets, D.; Chien, Y.-R.; Chang, T.-Y.; Fang, S.-H.; Tsao, Y. The IPIN 2019 Indoor Localisation Competition - Description and Results IEEE Access Vol. 8, pp. 206674-206718, 2020. https://doi.org/10.1109/ACCESS.2020.3037221</li></ul><p><strong>Additional information can be found at:</strong></p><ul><li><a href="http://evaal.aaloa.org/2019/call-for-competitions">http://evaal.aaloa.org/2019/call-for-competitions</a></li></ul><p><strong>For any further questions about the database and this competition track, please contact: </strong></p><ul><li>Joaquín Torres (<a href="mailto:jtorres@uji.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">jtorres@uji.es</a>,<a href="mailto:torres@ubikgs.com?subject=Contact%20from%20Zenodo%203606765">torres@ubikgs.com</a>) UBIK Geospatial Solutions S.L., Spain<br>Institute of New Imaging Technologies, Universitat Jaume I, Spain.</li><li>Antonio R. Jiménez (<a href="mailto:antonio.jimenez@csic.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">antonio.jimenez@csic.es</a>) Center of Automation and Robotics (CAR)-CSIC/UPM, Spain.</li></ul>
Supporting model data for Paleogeographic controls on the evolution of Late Cretaceous ocean circulation by Ladant, J.-B., et al. in Climate of the Past, doi:10.5194/cp-2019-157.
<p>The dataset is comprised of CCSM4 model variables required to reproduce the figures shown in the following manuscript:</p> <p>Ladant, J.-B., C. J. Poulsen, F. Fluteau, C. R. Tabor, K. G. MacLeod, E. E. Martin, S. J. Haynes and M. A. Rostami, Paleogeographic controls on the evolution of Late Cretaceous ocean circulation, Climate of the Past, doi:10.5194/cp-2019-157.</p>
Poseidon 2.0 - Decision Support Tool for Water Reuse (Microsoft Excel) and Handbook
<p>Poseidon 2.0 is a user-oriented, simple and fast Excel-Tool which aims to compare different wastewater treatment techniques based on their pollutant removal efficiencies, their costs and additional assessment criteria. Poseidon can be applied for pre-feasibility studies in order to assess possible water reuse options and can show decision makers and other stakeholders that implementable solutions are available to comply with local requirements. This upload consists in:</p> <ul> <li>Poseidon 2.0 Excel File that can be used with Microsoft Excel - XLSM</li> <li>Handbook presenting main features of the decision support tool - PDF</li> </ul> <p>This dataset is linked to following additional open access resources:</p> <ol> <li>: Oertlé E, Hugi C, Wintgens T, Karavitis C, Oertlé E, Hugi C, Wintgens T, Karavitis CA. 2019. <strong>Poseidon—Decision Support Tool for Water Reuse</strong>. Water. 11(1):153. doi:10.3390/w11010153. [accessed 2019 Jan 22]. <a href="http://www.mdpi.com/2073-4441/11/1/153">http://www.mdpi.com/2073-4441/11/1/153</a> .</li> <li>Externally hosted supplementary file 1, Oertlé, Emmanuel. (2018, December 5). <strong>Poseidon - Decision Support Tool for Water Reuse (Microsoft Excel) and Handbook (Version 1.1.1)</strong>. Zenodo. <a href="http://doi.org/10.5281/zenodo.3341573">http://doi.org/10.5281/zenodo.3341573</a></li> <li>Externally hosted supplementary file 2, Oertlé, Emmanuel. (2018). <strong>Wastewater Treatment Unit Processes Datasets: Pollutant removal efficiencies, evaluation criteria and cost estimations (Version 1.0.0)</strong> [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.1247434">http://doi.org/10.5281/zenodo.1247434</a></li> <li>Externally hosted supplementary file 3, Oertlé, Emmanuel. (2018). <strong>Treatment Trains for Water Reclamation (Dataset) (Version 1.0.0)</strong> [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.1972627">http://doi.org/10.5281/zenodo.1972627</a></li> <li>Externally hosted supplementary file 4, Oertlé, Emmanuel. (2018). <strong>Water Quality Classes - Recommended Water Quality Based on Guideline and Typical Wastewater Qualities (Version 1.0.2)</strong> [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.3341570">http://doi.org/10.5281/zenodo.3341570</a> </li> </ol>
sif-downscaling-essd-supporting-data
<p>This repository contains supporting data associated to the following paper:</p> <p><strong>Duveiller G., Filipponi F., Walther S., Köhler P., Frankenberg C., Guanter L., and Cescatti A.</strong> 2020. A spatially downscaled sun-induced fluorescence global product for enhanced monitoring of vegetation productivity. <em>Earth System Science Data</em>. <a href="https://doi.org/10.5194/essd-2019-121">https://doi.org/10.5194/essd-2019-121</a></p> <p>The actual dataset that the paper describes, and which is considered the final result of the study, it available in its dedicated repository in the JRC Data Catalogue at <a href="https://data.jrc.ec.europa.eu/dataset/21935ffc-b797-4bee-94da-8fec85b3f9e1">https://data.jrc.ec.europa.eu/dataset/21935ffc-b797-4bee-94da-8fec85b3f9e1</a> (DOI:10.2905/21935FFC-B797-4BEE-94DA-8FEC85B3F9E1). Instead, the present repository contains associated data that falls in two categories:</p> <ol> <li>Post-processed data necessary to reproduce the figures in the paper using the code in the Github repository: <a href="https://github.com/GregDuveiller/sif-downscaling-essd/">https://github.com/GregDuveiller/sif-downscaling-essd/</a> (DOI:10.5281/zenodo.3753521).</li> <li>Pre-processed data that is normally availiable from other sources but which had to be especially tailored for the study. They are composed of OCO-2 and TROPOMI observations that are gridded and composited to the similar format to MODIS data used in the paper. See the paper for more details.</li> </ol> <p> </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.