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14,867 results for “determination”
Chlorophyll determined by extraction of samples taken approximately weekly from seawater intake starting at Palmer Station by station personnel including during winter-over period, 1991-2024.
Chlorophyll a (Chl a) is the principal photosynthetic pigment of phytoplankton, and is used as a proxy measurement for estimating phytoplankton biomass in water samples. Chl a concentrations reflect the distribution of active phytoplankton spatially and with depth in the water column and their changes over time. Chlorophyll a is determined weekly year-round at the laboratory seawater intake (SWI), from a depth of 6 meters. Concentrations are typically very low (< 1 µg Chl a per liter) in winter (April-October), and higher (1-30 µg/L) following the initiation of the annual spring-summer phytoplankton bloom in November - January.
Data set: Can ocean community production and respiration be determined by measuring high-frequency oxygen profiles from autonomous floats?
<p>Relevant autonomous float data for <a href="https://doi.org/10.5194/bg-17-4119-2020">Gordon et al. (2020)</a>. Following a similar structure to the Argo network's "synthetic" profile files, one file per float is produced with all relevant variables (temperature, salinity, chlorophyll, backscatter, dissolved oxygen) on a common depth and time grid. The Electro-Magnetic Autonomous Profiling Explorer (EM-APEX) floats were deployed in the northern Gulf of Mexico in May 2017 - see <a href="https://doi.org/10.1109/CWTM43797.2019.8955168">Shay et al. (2019)</a> for more information. </p> <p>The data published here contains a timestamp for each data point. Another version of this data which contains some additional variables is hosted on <a href="https://data.gulfresearchinitiative.org/data/R5.x275.281:0001">GRIIDC</a>, but does not contain a timestamp for each data point, but rather for each profile. </p>
Fracture Toughness of Off-Stoichiometric B2 NiAl as determined by micromechanical tests and atomistic simulations
<p>KQJ-Alconcentration-NiAl_experiment.csv : semicolon-separated ASCII file containing the fracture toughness (2nd column) and the contribution of the plastic deformation to the fracture toughness (3rd column) as function of Al concentration (1st column) for off-stoichiometric B2 NiAl as determined by micro mechanical tests on notched cantilever beams.</p> <p>KIc-Alconcentration-NiAl_static-simulations.dat : space-separated ASCII file containing the fracture toughness (K_Ic) of B2 NiAl (2nd column) for different Al concentrations (first column) as as determined by static atomistic calculations with the<br> # Potential by G. P. P. Pun, Y. Mishin, (Phil. Mag. 89 (34-36) (2009) 3245– 3267).</p> <p>NiAl_Pun_conc_0.40-0.65Ni_Esurf110_Cij.dat : space-separated ASCII file containing the energy of {110} surfaces (2nd column) and elastic constants (columns 3-5) of B2 NiAl for different Ni concentrations (f1st column) as determined by atomistic simulations using the the potential by G. P. P. Pun, Y. Mishin, (Phil. Mag. 89 (34-36) (2009) 3245– 3267)</p> <p>KIc-Alconcentration-NiAl_theory.dat : space-separated ASCII file containing the fracture toughness (K_Ic) of B2 NiAl (2nd column) for different Al concentrations (1st column) as calculated by the Griffith equation.</p> <p> </p>
Data from: Choosy beetles: how host trees and southern boreal forest naturalness may determine dead wood beetle communities
<p>See methods section of paper for detailed information on dataset and sources; briefly, these .csv files includes numbers of each beetle species captured at all sites used in the project, as well as information about each site and about each species.</p> <p> </p> <p>Data from:</p> <p><strong>Choosy beetles: how host trees and southern boreal forest naturalness may determine dead wood beetle communitie</strong><strong>s</strong></p> <p>Ryan C. Burner, Tone Birkemoe, Jörg G. Stephan, Lukas Drag, Jörg Muller, Otso Ovakainen, Mária Potterf, Olav Skarpaas, Tord Snall, Anne Sverdrup-Thygeson</p> <p>Forest Ecology and Management, 2021</p> <p> </p> <p>From abstract of paper:</p> <p>Wood-living beetles make up a large proportion of forest biodiversity, and contribute to important ecosystem services, including decomposition. Beetle communities in managed southern boreal forests are less species rich than in natural and near-natural forest stands. In addition, many beetle species rely primarily on specific tree species. Yet, the associations between individual beetle species, forest management category, and tree species are seldom quantified, even for red-listed beetles. We compiled a beetle capture dataset from flight intercept traps placed in Norway spruce (<em>Picea abies</em>), oak (<em>Quercus sp.</em>), and Eurasian aspen (<em>Populus tremulae</em>) trees in 413 sites in mature managed forest, near-natural forest, and clear-cuts in southeastern Norway. We used joint species distribution models to estimate the strength of associations for 368 saproxylic beetle species (including 20 vulnerable, endangered, or critical red-listed species) for each forest management category and tree species. Tree species on which traps were mounted had the largest effect on beetle communities; oaks had the most highly associated beetle species, including most of the red-listed species, followed by Norway spruce and Eurasian aspen. Most beetle species were more likely to be captured in near-natural than in mature managed forest. Our estimated associations were compatible – for many species – with categorical classifications found in several existing databases of saproxylic beetle preferences. These quantitative beetle-habitat associations will improve future analyses that have typically relied on categorical classifications. Our results highlight the need to prioritize conservation of near-natural forests and oak trees in Scandinavia to protect the habitat of many red-listed species in particular. Furthermore, we underline the importance of carefully considering the species of trees on which traps are mounted in order to representatively sample beetle communities in forest stands.</p>
DETERMINING AGES OF APOGEE GIANTS WITH KNOWN DISTANCES - Full PDFs
<p>Supplementary data to Feuillet+ (2016, ApJ, arXiv:1511.04088):</p> <p>Using the APOGEE survey instrument and the NMSU 1m telescope, we observe a sample of bright, nearby, red giant stars with known distances measured by Hipparcos. By applying Bayesian analysis and hierarchical modeling, we determine individual stellar ages and the star formation histories of single alpha-abundance subsamples. The probability distribution functions (PDFs) and modeled star formation histories (SFHs) of all stars analyzed in this paper.</p> <p>Data included: APOGEE/2MASS ID, the age values for PDFs in log(age), the isochrone matching likelihood function as given in Equation 4 of paper, the age PDF of a Bayesian analysis using a flat SFH (Section 4.3), the hierarchically modeled SFH using an alpha-abundance dependent Gaussian+uniform model (Section 4.6), and the age PDF of an empirical Bayesian analysis using the hierarchically modeled SFH (Section 4.6). Individual ages of stars in the paper are taken as the mean of the age PDF, however, most PDFs are non-Gaussian, which introduces complicated errors into the selection of a single age.</p>
Contour method and neutron diffraction dataset to determine the weld fusion zone shape on residual stress in submerged arc welding
<p>This is a dataset which formed the basis for "The effect of the weld fusion zone shape on residual stress in submerged arc welding" by A. Ishigami, M. J. Roy, J. N. Walsh and P. J. Withers appearing in the Journal of Advanced Manufacturing Technology.</p> <p>Two X-grade steel specimens with different high speed, submerged arc welds with very slight differences in fusion zone shape were compared with a novel contour method application as well as with neutron diffraction. Neutron diffraction was carried out with the SALSA instrument at the Institut Laue-Langevin in Grenoble, France with the assistance of T. Pirling. Data files with 441 in the descriptor refer to 'conventional' parameters (see publication), while 241 refers to 'new'.</p> <p>Provided in this dataset are four *.dat files, which contains data is in the form of a point cloud with one point per line, whitespace delimited in microns. Data was captured with a Nanofocus CF-4 laser profilometer sensor with point spacing 30 µm apart. Data with z coordinates below or above 500 µm are considered outside of the surface detection limits.</p> <p>Also included is an Excel worksheet, which contains the calculated residual stresses as found with LAMP (https://www.ill.eu/instruments-support/computing-for-science/cs-software/all-software/lamp/). Raw data is available here:</p> <p>P. J. Withers, A. Ishigami, T. Pirling, M. Roy, J. Walsh (2014). The effect of weld bead shape on residual stress in novel low heat input welding of steel [Data set]. ILL. http://doi.ill.fr/10.5291/ILL-DATA.1-02-145</p> <p>The authors would like to thank JFE Steel Corporation for both direct and in-direct support of this research. The authors would also like to thank the Institut Max von Laue-Paul Langevin for the allocation of beamtime at SALSA and gratefully acknowledge the help of Thilo Pirling for his assistance in performing the neutron diffraction experiments. A. Ishigami would like to thank Kenji Oi for his support of this research. M. J. Roy would like to thank Ian Winstanley for his assistance in performing the contour cuts. M. J. Roy acknowledges financial support from the EPSRC (EP/L01680X/1) through the Materials for Demanding Environments Centre for Doctoral Training.</p>
Data - Ant identity determines the fungi richness and composition of a myrmecochorous seed
<p>Data set and analyse used in the manuscript title "<span>Ant </span><span>identity determines the fungi richness and composition of myrmecochorous seeds". In this manuscript w<span>e explore the effects of seed manipulation on fungi communities promoted by two ants with contrasting effects on seed germination and antimicrobial strategies. We hypothesize that i) seeds manipulated by <em>Atta sexdens</em> (increase seed germination and has broad cleaning strategies) will present lower fungi richness than those manipulated by <em>Acromyrmex subterraneus</em> (impair seed germination and has narrow cleaning strategies); <span>ii) seeds manipulated by </span><em>A. sexdens </em>and<em> Ac. subterraneus </em>will present<em> </em>dissimilar<em> </em><span>fungi composition. </span>We tested the hypotheses by identifying fungi morphotypes present in three groups of seeds: i) manipulated by <em>Atta sexdens</em>; ii) manipulated by <em>Ac. subterraneus</em>; iii) unmanipulated. </span></span><span>From the seeds manipulated by ants, we randomly take a sub-sample of 20 seeds per nest to evaluate the fungi community. We also took 20 unmanipulated seeds (the ones left outside each experimental nest). Therefore, we had three seed treatment groups: <span><span> </span></span>i) manipulated by <em>A. sexdens</em> (20 seeds per nest = 80 seeds)<em>;</em> ii) manipulated by <em>Ac. Subterraneus</em> (20 seeds per nest = 80 seeds)<em> </em>and iii) control - unmanipulated seeds left outside of each experimental nest (20 seeds outside of each nest = 160 seeds).</span><span>To allow the fungi growth on seeds, we placed each seed separately on sterile Petri dishes (90 x15 mm) filled with 15 ml of Potato-Dextrose-Agar (PDA) culture medium. We then transported each Petri dish to a Bio-Oxygen-Demand incubator (BOD) at 25°C for 28 days. After that period, we sampled the fungi and prepared microscope slides for each fungus morphotype found in each Petri dish. We identified the fungi to the lower taxonomic level possible using “The genera of Hyphomycetes” <span><span>(Seifert et al. 2011)</span></span> and the website mycobank.org . We used this method because it is widely used to identify pathogens in seeds, has a low cost and has good specificity to identify fungi<span> </span>. Furthermore, PDA medium is a non-selective fungi growth media suitable for a broad range of fungi species.</span></p> <p><span><span> </span></span></p>
Determinants of rooftop solar uptake: a comparative analysis of the residential and non-residential sectors in the Basque Country (Spain)
<p>Data for <strong>Determinants of rooftop solar uptake: a comparative analysis of the residential and non-residential sectors in the Basque Country (Spain)</strong></p> <p>Rooftop solar, both in the residential and the non-residential sector, is emerging rapidly as a popular source of clean electricity. Together with utility-scale photovoltaics, its future growth is essential to achieve decarbonization targets. Therefore, understanding adoption determinants for firms and households is key to efficiently promoting its diffusion. There is a gap, however, in the knowledge of non-residential adoption determinants, as less attention has been given to this sector compared to the residential sector. As a result of this gap, there is an absence of comparative analysis across sectors. As determinants of adoption cannot be assumed to be the same in both sectors, the objective of this research is threefold. First, to analyze whether the residential and non-residential sectors share key determinants of rooftop solar investment; second, to compare the sectoral differences in these determinants; and third, to assess the policy implications of the results obtained to further promote distributed solar photovoltaic energy. For this purpose, a regional case study in the Basque Country (Spain) was conducted, applying key theoretical frameworks to both sectors in a way that maximized the comparability of the results obtained across them. The results showed that adoption determinants are very different across sectors and, therefore, sector-specific policy actions need to be taken in each sector to efficiently promote rooftop solar. For the residential sector, policy actions could build upon behavioral aspects; for the non-residential sector, economic incentives are expected to be more successful, especially among medium size businesses, which are identified as the most promising segment.</p> <p> </p>
Pathogen lifestyle determines host genetic signature of quantitative disease resistance loci in oilseed rape (Brassica napus)
<p>Supplemental datasets associated with publication: Pathogen lifestyle determines host genetic signature of quantitative disease resistance loci in oilseed rape (<em>Brassica napus</em>)</p> <p><strong>Abstract</strong></p> <ul> <li>Crops are affected by several pathogens, but these are rarely studied in parallel to identify common and unique genetic factors controlling diseases. Broad-spectrum quantitative disease resistance (QDR) is desirable for crop breeding as it confers resistance to several pathogen species.</li> <li>Here, we use associative transcriptomics (AT) to identify candidate gene loci associated with <em>Brassica napus</em> constitutive QDR to four contrasting fungal pathogens: <em>Alternaria brassicicola</em>, <em>Botrytis cinerea</em>, <em>Pyrenopeziza</em><em> brassicae</em> and <em>Verticillium longisporum. </em>We did not identify any loci associated with broad-spectrum QDR to fungal pathogens with contrasting lifestyles. Instead, we observed QDR dependent on the lifestyle of the pathogen—hemibiotrophic and necrotrophic pathogens had distinct QDR responses and associated loci, including some loci associated with early immunity. Furthermore, we identify a genomic deletion associated with resistance to <em>V. longisporum </em>and potentially broad-spectrum QDR.</li> <li>This is the first time AT has been used for several pathosystems simultaneously to identify host genetic loci involved in broad-spectrum QDR. We highlight constitutively expressed candidate loci for broad-spectrum QDR with no antagonistic effects on susceptibility to the other pathogens studies as candidates for crop breeding. In conclusion, this study represents and advancement in our understanding if broad-spectrum QDR in <em>B. napus </em>and is a significant resource for the scientific community. </li> </ul> <p><strong>Description of data files</strong></p> <p><strong>Full dataset for input into AT analysis </strong>Full datasets (infection phenotypes for <em>A. brassicicola, B. cinerea, </em>or <em>V.longisporum, </em>ROS measurements for chitin, flg22, or elf18) and link to original <em>P. brassicae </em>dataset. These datasets were used for input into the Associative Transcriptomics pipeline (Nichols, 2022, <a href="https://github.com/bsnichols/GAGA. https://zenodo.org/badge/latestdoi/512807075">https://github.com/bsnichols/GAGA. https://zenodo.org/badge/latestdoi/512807075</a>). </p> <p><strong>Table S1 </strong>Mean, normalized phenotype data for resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and <em>Verticillium longisporum</em>) and ROS response induced by PAMPS (chitin, flg22, and elf18). These data were used for association transcriptomic analysis.<strong> </strong></p> <p><strong>Table S2 </strong>Full list of single nucleotide polymorphism (SNP) markers and significance levels from genome-wide association (GWA) analyses for resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and <em>Verticillium longisporum</em>) and ROS response induced by PAMPS (chitin, flg22, and elf18). Each excel tab contains the analyses for a single trait. The best fit model for GWA analysis is indicated in the tab title. Manhattan plots showing marker-trait association are included for data visualization; x-axis indicates SNP location along the chromosome; the y-axis indicates the -log10(p) (P value). Qqplots are included to demonstrate model fit.</p> <p><strong>Table S3</strong> Full list of gene expression markers (GEMs) and significance levels from GEM analyses for resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae and Verticillium longisporum</em>) and ROS response induced by PAMPS (chitin, flg22, and elf18). Each excel tab contains the analyses for a single trait. Manhattan plots showing marker-trait association are included for data visualization; x-axis indicates GEM location along the chromosome; the y-axis indicates the -log10(p) (P value). </p> <p><strong>Table S4 </strong>184 gene expression markers (GEMs) associated with chitin-induced ROS compared with GEMs associated with resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and<em> Verticillium longisporum</em>) and ROS response induced by flg22, and elf18. Lists correspond to Venn diagrams in Fig. 2. The first tab includes all 184 GEMs associated with chitin-induced ROS. The subsequent tabs include lists of shared GEMs associated with chitin-induced ROS response and each additional trait (quantitative disease resistance (QDR) to each fungal pathogen or additional PAMP-induced ROS responses). The title of each tab indicates the data included in each comparison and the number of shared GEMs. Predicted <em>Arabidopsis thaliana</em> orthologs and corresponding descriptions are shown where possible. </p> <p><strong>Table S5</strong> Enrichment analyses to determine if the number of gene expression markers (GEMs) shared between different lists is greater than the number of GEMs that would be expected by chance (e.g., lists of quantitative disease resistance (QDR) GEMs for two fungal pathogens). The representation factor is the number of overlapping GEMs divided by the expected number of overlapping GEMs drawn from two independent groups (traits), considering the total number of GEMs sequenced (53884). A representation factor > 1 indicates more overlap than expected of two groups, a representation factor < 1 indicates less overlap than expected, and a representation factor of 1 indicates that the two groups by the number of genes expected for independent groups of genes. </p> <p><strong>Table S6 R</strong>esults from Weighted Co-expression Gene Network Analysis (WGCNA). The first tab indicates significant modules from WGCNA analysis. Black and magenta modules are associated with antagonistic effects on resistance/susceptibility to all four pathogens. The second tab includes a full list of the GEM markers (Table S3), which are in significant WGCNA modules. The third, fourth and, fifth tabs indicate all significant GEMs in the black module, GO terms associated with GEMs in the black module, and all GO terms associated with the black module, respectively. The sixth, seventh and, eighth tabs indicate all significant GEMs in the magenta module, GO terms associated with GEMs in the magenta module, and all GO terms associated with the magenta module, respectively.</p> <p><strong>Table S7 </strong>Shared gene expression markers (GEMs) associated with resistance to different pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and <em>Verticillium longisporum</em>). Lists correspond to matrices and Venn diagrams in Fig. 3. The first tab includes all GEMs associated quantitative disease resistance (QDR) to the fungal pathogens. The subsequent tabs include lists of shared GEMs associated with QDR to two or more fungal pathogens. The title of each tab indicates the data included in each comparison and the number of shared GEMs. Predicted <em>Arabidopsis thaliana</em> orthologs and corresponding descriptions are shown where possible. </p> <p><strong>Table S8 </strong>List of genes in linkage disequilibrium with the top marker for <em>Verticillium longisporum</em> resistance from genome-wide association (GWA) analysis on chromosome A09 (107 genes)(Tab 1) and the homoeologous region on C08 (Tab 2). Their percentage identity and query coverage in <em>Brassica napus</em> reference genotypes Quinta, Tapidor, Westar and Zhongshuang 11 compared to the <em>B. napus</em> pantranscriptome is indicated. Predicted <em>Arabidopsis thaliana</em> orthologs and corresponding descriptions are shown where possible. </p> <p> </p>
Using the traditional microscope for mineral grain orientation determination: A prototype image analysis pipeline for optic-axis mapping (POAM). Original dataset.
<p>The data repository contains data obtained with the microscope Nikon Eclipse LV100ND that was stitched with <a href="https://imagej.net/plugins/trakem2/">TrakEM2 software</a>. The files allow reproducing the results obtained and plot in <a href="https://doi.org/10.1111/jmi.13284">Acevedo et al. (2024)</a> <strong>"Using the traditional microscope for mineral grain orientation determination: A prototype image analysis pipeline for optic-axis mapping (POAM)."</strong> by Acevedo Zamora, M. A., Schrank, C. E., & Kamber, B. S.</p> <p>The prototype uses MatLab scripts (<a href="https://github.com/marcoaaz/AcevedoEtAl._2024a_POAM">AcevedoEtAl._2024a_POAM</a>) that were documented in the paper Supplementary Material 1. The metadata can be found in Supplementary Material 3 and follows the structure of this data repository. The user needs downloading and changing the paths to run the same scripts and reproduce the results.</p> <p>Note: After download, unzip and merge (copy-paste) the folders (parts 1, 2 and 3). Before merging, the containing folder should be re-named to 'paper 2_datasets' to match exactly the MatLab scripts and reproduce our work.</p> <p>The remaining questions should be addressed to Marco Acevedo (maaz.geologia@gmail.com ; marco.acevedozamora@qut.edu.au)</p> <p>Thanks.</p>
A stakeholder-centered determination of High-Value Data sets: the use-case of Latvia
<p>The data in this dataset were collected in the result of the survey of Latvian society (2021) aimed at identifying high-value data set for Latvia, i.e. data sets that, in the view of Latvian society, could create the value for the Latvian economy and society.<br> The survey is created for both individuals and businesses.<br> It being made public both to act as supplementary data for "Towards enrichment of the open government data: a stakeholder-centered determination of High-Value Data sets for Latvia" paper (author: Anastasija Nikiforova, University of Latvia) and in order for other researchers to use these data in their own work.</p> <p>The survey was distributed among Latvian citizens and organisations. The structure of the survey is available in the supplementary file available (see Survey_HighValueDataSets.odt)</p> <p>***Description of the data in this data set: structure of the survey and pre-defined answers (if any)***<br> 1. Have you ever used open (government) data? - {(1) yes, once; (2) yes, there has been a little experience; (3) yes, continuously, (4) no, it wasn’t needed for me; (5) no, have tried but has failed}<br> 2. How would you assess the value of open govenment data that are currently available for your personal use or your business? - 5-point Likert scale, where 1 – any to 5 – very high<br> 3. If you ever used the open (government) data, what was the purpose of using them? - {(1) Have not had to use; (2) to identify the situation for an object or ab event (e.g. Covid-19 current state); (3) data-driven decision-making; (4) for the enrichment of my data, i.e. by supplementing them; (5) for better understanding of decisions of the government; (6) awareness of governments’ actions (increasing transparency); (7) forecasting (e.g. trendings etc.); (8) for developing data-driven solutions that use only the open data; (9) for developing data-driven solutions, using open data as a supplement to existing data; (10) for training and education purposes; (11) for entertainment; (12) other (open-ended question)<br> 4. What category(ies) of “high value datasets” is, in you opinion, able to create added value for society or the economy? {(1)Geospatial data; (2) Earth observation and environment; (3) Meteorological; (4) Statistics; (5) Companies and company ownership; (6) Mobility}<br> 5. To what extent do you think the current data catalogue of Latvia’s Open data portal corresponds to the needs of data users/ consumers? - 10-point Likert scale, where 1 – no data are useful, but 10 – fully correspond, i.e. all potentially valuable datasets are available<br> 6. Which of the current data categories in Latvia’s open data portals, in you opinion, most corresponds to the “high value dataset”? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}<br> 7. Which of them form your TOP-3? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}<br> 8. How would you assess the value of the following data categories?<br> 8.1. sensor data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable<br> 8.2. real-time data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable<br> 8.3. geospatial data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable<br> 9. What would be these datasets? I.e. what (sub)topic could these data be associated with? - open-ended question<br> 10. Which of the data sets currently available could be valauble and useful for society and businesses? - open-ended question<br> 11. Which of the data sets currently NOT available in Latvia’s open data portal could, in your opinion, be valauble and useful for society and businesses? - open-ended question<br> 12. How did you define them? - {(1)Subjective opinion; (2) experience with data; (3) filtering out the most popular datasets, i.e. basing the on public opinion; (4) other (open-ended question)}<br> 13. How high could be the value of these data sets value for you or your business? - 5-point Likert scale, where 1 – not valuable, 5 – highly valuable<br> 14. Do you represent any company/ organization (are you working anywhere)? (if “yes”, please, fill out the survey twice, i.e. as an individual user AND a company representative) - {yes; no; I am an individual data user; other (open-ended)}<br> 15. What industry/ sector does your company/ organization belong to? (if you do not work at the moment, please, choose the last option) - {Information and communication services; Financial and ansurance activities; Accommodation and catering services; Education; Real estate operations; Wholesale and retail trade; repair of motor vehicles and motorcycles; transport and storage; construction; water supply; waste water; waste management and recovery; electricity, gas supple, heating and air conditioning; manufacturing industry; mining and quarrying; agriculture, forestry and fisheries professional, scientific and technical services; operation of administrative and service services; public administration and defence; compulsory social insurance; health and social care; art, entertainment and recreation; activities of households as employers;; CSO/NGO; Iam not a representative of any company<br> 16. To which category does your company/ organization belong to in terms of its size? - {small; medium; large; self-employeed; I am not a representative of any company}<br> 17. What is the age group that you belong to? (if you are an individual user, not a company representative) - {11..15, 16..20, 21..25, 26..30, 31..35, 36..40, 41..45, 46+, “do not want to reveal”}<br> 18. Please, indicate your education or a scientific degree that corresponds most to you? (if you are an individual user, not a company representative) - {master degree; bachelor’s degree; Dr. and/ or PhD; student (bachelor level); student (master level); doctoral candidate; pupil; do not want to reveal these data}</p> <p>***Format of the file***<br> .xls, .csv (for the first spreadsheet only), .odt</p> <p>***Licenses or restrictions***<br> CC-BY</p> <p> </p> <p> </p>
Anion concentrations for LOD/LOQ determination
<p>This is a small dataset of different anion concentrations measured using a Metrohm ion chromatography compact IC. A standard of 0.1 mg/L was measured five times in order to determine the LOD and LOQ of the apparatus for these specific anions.</p>
Data set related to "Secretory and metabolic determinants of brain metastasis"
<p>This repository includes the data sets related to the publication titled "Secretory and metabolic determinants of brain metastasis"</p>
Factors determining distributions of rainforest Drosophila shift from interspecific competition to high temperature with decreasing elevation (original datasets)
<p>This repository provides the data for the manuscript "Factors determining distributions of rainforest Drosophila shift from interspecific competition to high temperature with decreasing elevation"</p> <p>We investigated thermal tolerances and interspecific competition as causes of species turnover in the nine most abundant species of <em>Drosophila</em> along elevational gradients in the Australian Wet Tropics. Specifically, we 1) analyzed the distribution patterns of the studies <em>Drosophila</em> species; 2) fitted thermal performance curves; 3) tested the correlation between multiple thermal traits and distribution patterns; 4) fitted the Beverton-Holt model to describe the single-generation intra- and inter-specific competition effect; 5) examined the long-term effect of competition and temperature on the population size of a pair of Drosophila species.</p> <p>More details are provided in the README file.</p>
Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models
<p>This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models" to be published in the journal Animal - Open Space.</p>
Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 1. Weight changes due to fasting, bleeding, and chilling
<p>This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 1. Weight changes due to fasting, bleeding, and chilling" to be published in the journal Animal - Open Space. </p>
Data from: Development of Single Nucleotide Polymorphism (SNP) Panel for determination of environmental influence on genome for wild Columbia River redband trout (Oncorhynchus mykiss gairdnerii) in Southwest Idaho streams
<p>DNA were derived from fin tissue samples taken from individual trout captured from Little Jacks Creek, Big Jacks Creek , and Duncan Creek of the Owyhee mountains and Keithly Creek and Upper Mann Creek in the Hitt mountains of Western Idaho, United States. Fin tissues were collected from individual trout from each stream during monthly sampling events in June through October 2020. </p> <p><em>DNA Extraction:</em> Extraction of DNA from caudal fin tissues were performed using Quick-DNA Miniprep Plus purification kits (Zymo Research Inc.©). Small sections of fin tissue (≤ 25 mg) were collected from each sample. This was mixed with a digesting solution comprised of ultra-pure water, solid tissue buffer (Zymo Research Inc.©) and proteinase K. All tissues were digested in sealed microcentrifuge tubes for at minimum 3 h at 55°C in a water bath. We then aliquoted 100 µL of digestion supernatant and combined with 200 µL of genomic binding buffer (Zymo Research Inc.©). DNA was eluted in 50, 75, and 100 µL of elution buffer to determine which volume provided sufficient DNA concentration for genotyping. After it was determined all quantities produced suitable concentrations, going forward, 50 µL of elution buffer used.</p> <p><em>Genotyping:</em> Following extraction, genotyping-in-thousands sequencing took place at the Hagerman National Fish Hatchery’s genetics research facility with the assistance of the Columbia River Intertribal Fish Commission (CRTFC). Genotyping protocols were as described in Campbell et al. (2015) and summarized below. First, samples were prepared for amplification via PCR by combining DNA extracts with a Qiagen Plus multiplex master mix and a species-specific pooled primer mix. This step added the Illumina sequencing primer sites to amplicons. Following the creation of the PCR cocktail, thermocycling was conducted for amplification. Amplified samples were then diluted 20-fold. Diluted samples were transferred to new 96-well PCR plates where two genetic indexes and barcodes provides a unique set of tagging primers to each well and plate. Tagged plates then underwent a second PCR step. After the second PCR, all DNA were transferred to Charm Biotech normalization plates where DNA was bound to wells, washed, and finally eluted. After normalization, all DNA was pooled together and a purification step using magnetized beads in two steps to selectively remove fragments of DNA that are both too large and too small for sequencing. Following purification, each plate was quantified via qPCR using Life Technologies QuantStudio 6 Flex Instrument (Life Technologies). Finally, sequencing was performed using an Illumina HiSeq 1500 instrument.</p> <p><strong>Ancillary peer-reviewed manuscripts:</strong><br> <em>Genotyping protocols</em><br> Campbell NR, Harmon SA, Narum SR. 2015. Genotyping-in-Thousands by sequencing (GT-seq): A cost effective SNP genotyping method based on custom amplicon sequencing. Mol Ecol Resour, 15: 855-867. https://doi.org/10.1111/1755-0998.12357<br> <em>SNP loci reference</em><br> Collins EE, Hargrove JS, Delomas TA, Narum SR. 2020. Distribution of genetic variation underlying adult migration timing in steelhead of the Columbia River basin. Ecology and Evolution, 10(17): 9486-9502. https://doi.org/10.1002/ece3.6641 </p> <p><strong>Data Use</strong>:<br> <em>License</em>: <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> <br> <em>Recommended Citation</em>: Wooding AP, Narum SR, Pradhan DS. 2022. Data from: Development of Single Nucleotide Polymorphism (SNP) Panel for determination of environmental influence on genome for wild Columbia River redband trout (Oncorhynchus mykiss gairdnerii) in Southwest Idaho streams (0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7055582</p> <p>Funding for this project is provided by US National Science Foundation and Idaho EPSCoR through award: OIA-1757324 </p>
Determinant Quantum Monte Carlo data for the Hubbard model on the square and honeycomb lattice.
<p>Data generated with QUEST 1.4.9. For documentation see these two homepages:<br> Original homepage: http://quest.ucdavis.edu/<br> Newest version available at: https://code.google.com/archive/p/quest-qmc/</p> <p>Available data from equal time measurements:</p> <ul> <li>up-up charge correlation function</li> <li>up-dn charge correlation function</li> <li>sz-sz spin correlation function</li> <li>pair correlation function</li> <li>kinetic energy</li> <li>total energy</li> <li>chi thermal</li> <li>squared magnetization</li> <li>ZZ AF structure factor</li> </ul> <p>Data for the square lattice calculated for</p> <ul> <li>lattice sizes 8x8, 10x10, 12x12</li> <li>trotter discretizations 0.05, 0.1, 0.2</li> <li>U 0.0 to 7.1 in steps of 0.1</li> </ul> <p>Data for the honeycomb lattice calculated for</p> <ul> <li>lattice sizes 6x6, 9x9, 12x12</li> <li>trotter discretizations 0.05, 0.1, 0.2</li> <li>U 0.0 to 7.1 in steps of 0.1</li> </ul> <p>All energies are in units of the hopping parameters which is set to t=1. All simulations are done for half filling.</p> <p>The data are used in the publication "First-order metal-insulator transitions in the extended Hubbard model due to self-consistent screening of the effective interaction" available on the arXiv (arXiv:1706.09644). There it is used to do an extrapolation of finite size and finite trotter errors and finally calculate derivatives of charge correlation functions w.r.t. the interaction U.</p> <p>The data are available in hdf5 archives and can easily be accessed, e.g., with python and h5py. An example python script is included. Relevant input parameters are included in the h5 files.</p> <p>All calculated quantities are averaged over multiple consecutive simulations, which is why the data is not presented in the usual QUEST output. This was necessary due to limited walltime on the used supercomputer.</p> <p>This version (v2) includes the number of bins used in each simulation and a slighlty changed python script to read the data.</p>
Dataset for "A simple and accurate method to determine fluid-crystal phase boundaries from direct coexistence simulations"
<p>This is a dataset for the article "A simple and accurate method to determine fluid-crystal phase boundaries from direct coexistence simulations", available at https://arxiv.org/abs/2403.10891 (Full citation data will be added upon final publication of the article.)</p> <p>This package provides figure data and representative configuration files associated with the systems studied in the article above. Additionally, for the hard sphere system, this package includes direct coexistence data for all reported system sizes and crystal orientations.</p> <p> </p> <p> </p>
University dropout: A systematic review of the main determinant factors
<p><strong><span>Introduction:</span></strong><span> This research is a systematic review aimed at synthesizing scientific evidence on the causes of university dropout, focusing on the subcategories of vocational guidance, academic performance, socioeconomic status, and institutional aspects between 2020 and June 2024. <strong>Methods:</strong> Only articles addressing university dropout were considered, analyzing dimensions such as vocational guidance, academic performance, socioeconomic status, and institutional aspects. Articles published in indexed scientific journals with double-blind, double-blind peer, or open reviews between 2020 and June 2024 were included. The main databases used were Scopus, Web of Science, and Google Scholar. To assess the risk of bias in qualitative studies, the criteria from the article "Validity criteria for qualitative research: three epistemological strands for the same purpose" were used. For quantitative studies, the criteria from the article "Evaluating survey research in articles published in Library Science journals" were followed. For mixed-method studies, both sets of criteria were combined. <strong>Results:</strong> A total of 23 studies were included: 15 quantitative (65.22%), 3 qualitative (13.04%), and 5 mixed-method (21.74%). All studies (100%) addressed the subcategories of socioeconomic status and institutional aspects. Regarding the academic performance subcategory, 86% of the studies addressed it, while the vocational guidance subcategory was covered by 73.91% of the studies. <strong>Conclusions:</strong> Vocational guidance, academic performance, socioeconomic status, and institutional aspects are crucial for reducing university dropout. Providing adequate professional guidance, academic support, financial assistance, and strong institutional support is fundamental to improving student retention and academic success.</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.