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3,480 results for “values”
Survey of Pacific Northwest public land managers science and values project, 2023
This dataset records survey data about public land managers who work in Oregon and Washington (Forest Service, Bureau of Land Management, Fish and Wildlife Service, National Park Service, Oregon Department of Forestry, Washington Department of Natural Resources). Data was collected in 2023 via the online survey platform Qualtrics. Data collection is complete. The dataset includes measures of managers beliefs about 1) variable density thinning of mature growth forests, 2) salvage logging of burned areas, 3) translocation of plant species from hotter and drier seed zones to adapt to climate change. It includes how managers evaluate the usefulness of scientific evidence and the soundness of action prescriptions for each of the three management issues Respondents were randomly assigned to either receive long-term or short-term studies, and positive or negative results. The dataset includes measures of sense of belonging (how much managers believe they belong at their workplace) and measures of public support/public threat (how much they believe the public understands and supports the actions they take on the landscape). The dataset includes respondent agency.
Harmonized National Land Cover Dataset Values for HydroBASINS Basins
Water quality is largely reflective of processes occurring on the surrounding landscape. While national landcover data are widely available via remotely sensed products, they are usually not aggregated in a manner that is expeditiously merged with basin-level data. To facilitate national-scale analyses of basin-level landcover with co-located water quality data, we present aggregated land cover data for the Contiguous United States. Data are aggregated using the HydroBASINS basin shapefiles. HYBAS_ID is retained to enable merging with HydroBASINS parent datasets.
Instructor Perspectives on APA Style in Nursing Education: Implicit and Explicit Value
<p><strong>OBJECTIVE:</strong> Disparities in how nursing faculty teach APA Style can confuse students and the librarians who help them. To clarify faculty expectations of and approaches to APA </p><p>Style, this study seeks to answer the following questions: (1) What do nursing faculty perceive as the impact or value of APA Style? (2) How do nursing faculty teach and grade APA Style? By revealing the unspoken assumptions about APA Style and the value it adds to nursing education, it is hoped that health sciences librarians can more intentionally and effectively support this aspect of the nursing curriculum.</p><p><strong>METHODS: </strong>A mixed-methods study was designed to investigate potential gaps between nursing instructors' expectations for APA and their teaching/grading practices. The study incorporated an online survey of 75 nursing faculty at 14 Carnegie institutions with nursing programs, as well as qualitative interviews with 12 faculty at those institutions. A grounded theory approach was used to uncover salient themes.</p><p><strong>RESULTS:</strong> Nursing instructors consistently emphasized the importance of APA for referencing/in-text citations in both the survey and the interviews. When discussing the value of APA Style in nursing education, interviewees stressed APA Style as essential in developing professional nursing communication skills and evidence-based practice. However, faculty expectations for students' APA skills were not in line with their teaching and grading practices.</p><p><strong>CONCLUSION:</strong> Given the wide range of reporting teaching and grading practices for APA Style, nursing programs should work to clarify expectations for APA Style, and standardize how it is taught.</p>
LAGOS - Predicted and observed maximum depth values for lakes in a 17-state region of the U.S.
This dataset includes predicted and observed values of maximum depth for lakes in the upper Midwest and northeast United States. All observed values came from LAGOS ver 1.040.0 (LAke multi-scaled GeOSpatial and temporal database), an integrated database of lake ecosystems (Soranno et al. 2015). LAGOS contains a complete census of lakes great than or equal to 4 ha with corresponding geospatial information for a 17-state region of the U.S., and a subset of the lakes has observational data on morphometry and chemistry. Approximately 40 different sources of data were compiled for this dataset and were mostly generated by government agencies (state, federal, tribal) and universities. Here, observed maximum depth values (n = 8164) were used to train and validate a predictive mixed effects model for lake depth using terrestrial and lake morphology as predictors (Oliver et al., submitted). Predicted values (n = 50 607) generated by the model had a root mean squared error of 7.1 m. This research was supported by the NSF Macrosystem Biology awards 1065786, 1065818, and 1065649.
Evidence Accumulation in Value-Based decisions
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Data associated with the following publication: Developing the Playground Play Value and Usability Audit Tool (PVUA): An Evaluation of Content Validity via an Expert Panel
<p>This data set contains the supporting data associated with the following publication:</p> <p>Morgenthaler, T., Loebach, J., Lynch, H., Pentland, D., Kottorp, A., & Schulze, C. (in press). Developing the Playground Play Value and Usability Audit Tool (PVUA): An Evaluation of Content Validity via an Expert Panel. Children, Youth and Environments. [DOI was not yet available when the data set was published]</p> <p>The data set includes the following files:</p> <ul> <li>read me file [contains all relevant information to understand and reuse this data set] </li> <li>13 additional files [for description, see read me file]</li> </ul> <p>For more information, please contact the lead researcher, Thomas Morgenthaler (tom.morgenthaler@gmail.com or 121101888@umail.ucc.ie)</p> <p> </p>
SM2RAIN-ASCAT (2007-2021) global daily satellite rainfall including aggregated values and trend parameters as 10km resolution GeoTIFFs
<p>This is a GeoTIFF version of the <a href="http://hydrology.irpi.cnr.it/download-area/sm2rain-data-sets/">SM2RAIN-ASCAT (2007-2021): global daily satellite rainfall from ASCAT soil moisture</a> data set v1.1 (Brocca et al. 2019). Conversion steps are available <a href="https://github.com/Envirometrix/LandGISmaps/tree/master/input_layers/SM2RAIN"><strong>here</strong></a>. Few important notes:</p> <ul> <li>Daily values are stored as integers, whereas in the NetCDF the dataset is rounded to one decimal place.</li> <li>The NetCDF has also a Quality Flag for a better and more informed use of the data (here omitted).</li> <li>P05, P50 and P95 indicate quantiles derived per pixel.</li> </ul> <p>Includes also long-term trends (trend.logit.ols) which was produced by fitting regression models to de-seasonalized time-series as explained in this <strong><a href="https://gitlab.com/openlandmap/global-layers/-/blob/master/input_layers/MOD13Q1/03-data-access.ipynb">python tutorial</a></strong>. Basically models are fitted for <strong>each pixel</strong> and the model parameters are saved as images.</p> <p>Monthly averages and s.d. of precipitation are available in the files:</p> <ul> <li>clm_precipitation_sm2rain.*_m_10km_s0..0cm_2007..2021_v1.5.tif = monthly precipitation in mm,</li> <li>clm_precipitation_sm2rain.*_sd.10_10km_s0..0cm_2007..2021_v1.5.tif = standard deviation of precipitation in mm * 10 per month (multiplied by 10 so Integers can be used),</li> </ul> <p>Downscaled monthly averages (1 km) are also available (<a href="https://doi.org/10.5281/zenodo.1435912">https://doi.org/10.5281/zenodo.1435912</a>).</p> <p>To cite this data set please refer to the <strong><a href="https://doi.org/10.5281/zenodo.2591214">original copy</a></strong> of the data set.</p> <ul> <li>Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., Wagner, W. (2019). <strong><a href="https://doi.org/10.5194/essd-11-1583-2019">SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations</a></strong>. Earth Syst. Sci. Data, 11, 1583–1601. <a href="https://doi.org/10.5194/essd-11-1583-2019">https://doi.org/10.5194/essd-11-1583-2019</a></li> </ul>
Calculation of parameter values based on observations for the herbaceous biomass plantation PFT representing Miscanthus in JSBACH3.2
<p>This dataset provides the calculation of parameter values and the observational data that was collected from literature used in these calculations for the re-implementation of a herbaceous biomass plantation (HBP) PFT representing Miscanthus in the dynamic global vegetation model (DGVM) JSBACH3.2 (Egerer et al. subm., Nützel et al. in prep.). The parameters included are the maximum rubisco capacity (Vmax) at 25°C, the PEPcase CO2 specificity (k) and specific leaf area (SLA). Some of the observed parameter values were already compiled in a dataset by Li et al. (2018). These observations were therefore re-used in this dataset (which is specified within the dataset sheets) and complemented with additional observed values from literature that has become available since then or was not included in the study by Li et al. (2018). A detailed methodology of the parameter calculations for JSBACH3.2 can be found on the first sheet of the dataset. </p>
Nuclear Magnetic Resonance values for the Eptachori, Pentalofos and Tsotyli formations in West Macedonia
<p>The data comprises work under the Project Pilot Strategy GA No. 101022664, funded by the European Union. </p> <p>The work relates to rock samples collected in 2022 in West Macedonia, Greece. For full details, please refer to the following:</p> <ol> <li>Tsotyli formation: <a href="https://app.geosamples.org/sample/igsn/IE5770001">https://app.geosamples.org/sample/igsn/IE5770001</a> - <strong>WGS84 Lat : 40.3075, </strong><strong>WGS84 Long : 21.3354</strong></li> <li>Pentalofos formation: <a href="https://app.geosamples.org/sample/igsn/IE5770002">https://app.geosamples.org/sample/igsn/IE5770002</a> - <strong>WGS84 Lat : 40.1332,</strong> <strong>WGS84 Long : 21.1997</strong></li> <li>Eptachori formation: <a href="https://app.geosamples.org/sample/igsn/IE5770003">https://app.geosamples.org/sample/igsn/IE5770003</a> - <strong>WGS84 Lat : 40.1332, </strong><strong>WGS84 Long : 21.1997</strong></li> </ol> <p>The focus of the work is related to CO2 storage in appropriate saline aquifers in West Macedonia. The bulk samples were shipped to IFP Energies for porosity and permeability laboratory investigation conducted by Nuclear Magnetic Resonance techniques. </p> <p>Further to the raw data from the NMR, a depiction of the latter is provided in the corresponding figures</p> <p> </p>
Greenup values for interior alaska 1976 - Present
Greenup, the frequent rapid transformation of Interior Alaska from Brown space to spring green as the leaves deciduous trees burst forth, is suddenly enough to allow assignment of a single date in a given area to this culmination of a key biological process. Greenup is important for more than just an aesthetic or biological perspective. For example, once Greenup has occurred, there is a rapid increase in evapotranspiration. Which in turn has important consequences in the areas mesoscale meteorology. Here, dates for green around Fairbanks for more than 20 springs are correlated against several different temperature derived indices calculated entirely from routinely available climatological data. We examined a variety of degree day statistics and temperature thresholds. The relationships between these parameters and Greenup dates in Interior Alaska should be useful in projecting future greenup dates. They may also be useful to post analyze Greenup dates in years for which there is meteorological data but no surviving record of Greenup dates.
Large consumer isotope values, Shark River Slough, Everglades National Park (FCE LTER), Florida, USA, May 2005 - ongoing
This dataset provides information on the stable isotope values from multiple tissues from various consumers (especially bull sharks and American alligators) sampled within the Shark River Slough.
Shark catches (longline), water temperatures, salinities, and dissolved oxygen levels, and stable isotope values in the Shark River Slough, Everglades National Park (FCE LTER), Florida, USA, May 2005 - ongoing
This dataset provides information on the catches of sharks in the Shark River Slough in relation to physical factors including dissolved oxygen, water temperature, salinity, and distance upstream. Analysis of data collected suggest that distance from the Gulf of Mexico and dissolved have the largest effects on shark catch rates, with most juvenile bull sharks being caught in Tarpon Bay. This dataset includes all sharks caught on longline gear, their morphometric data, and CNS stable isotope analysis for selected individuals.
Stable isotope values of consumers, producers, and organic matter in the Shark River Slough and Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, 2019 – ongoing
Wetland food webs have often been characterized as detrital-based ‘brown’ energy pyramids, whereas the relative role of autotrophic (‘green’) vs. microbial (‘brown’) energy sources falls along a continuum set by physical drivers, as well as autochthonous and allochthonous inputs (Moore et al. 2004; Evans-White & Halvorson 2017) that change with ecosystem development (Schmitz et al. 2006). In the Florida Coastal Everglades (FCE), metabolic imbalances, including the collapse of calcareous periphyton mats, begin with a loss of foundation species primary production and legacy organic matter (Gaiser et al. 2006). This process likely enhances heterotrophic microbial productivity (Schulte 2016) and the supply of detrital energy to consumers by changing bioavailable and recalcitrant carbon supplies (Baggett et al. 2013). A shift from complex periphyton communities to transient planktonic communities under elevated P exposure reduces habitat structure and animal refuges but increases ‘green’ energy supplies and edibility (Trexler et al. 2015; Naja et al. 2017). Multiple sites (n=9) within the FCE were selected to document changes in coastal food webs as a result of eutrophication and increasing hydrologic variability. The project began in 2019 and is currently ongoing. References: Baggett, L. P., Heck, K. L., Frankovich, T. A., Armitage, A. R., & Fourqurean, J. W. (2013). Stoichiometry, growth, and fecundity responses to nutrient enrichment by invertebrate grazers in sub-tropical turtle grass (Thalassia testudinum) meadows. Marine biology, 160, 169-180. Evans-White, M. A., and H. M. Halvorson. 2017. Comparing the Ecological Stoichiometry in Green and Brown Food Webs – A Review and Meta-analysis of Freshwater Food Webs. Frontiers in Microbiology 8:1184. Gaiser, E. E., Childers, D. L., Jones, R. D., Richards, J. H., Scinto, L. J., & Trexler, J. C. (2006). Periphyton responses to eutrophication in the Florida Everglades: cross‐system patterns of structural and compositional cha
Developmental change in prefrontal cortex recruitment supports the emergence of value-guided memory
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Listed values of Ibex 35 companies from January 2020 to November 2020
<p>The data included in the dataset are the listed values of the Ibex 35 companies from January 2020 to November 2020. The dataset is a file for each Ibex 35 company.</p>
Working time, energy throughput and value added embodied in production, consumption and trade by subsectors for the US, the EU, China and rest of the world (2011)
<p>This repository contains the data needed to reproduce the results in:</p> <p>Pérez-Sánchez, L., Velasco-Fernández, R., Giampietro, M., The international division of labor and embodied working time in trade for the US, the EU and China, Ecological Economics. <a href="http://doi.org/10.1016/j.ecolecon.2020.106909">https://doi.org/10.1016/j.ecolecon.2020.1069097</a></p> <p>Sources of data are specified in the dataset (under tab "references")</p> <p> </p>
Values of the reference prior for the Poi(s+b) model from JINST 7 (2012) P01012
<p>Plain text table with the values of the reference prior π(s) for the Poi(s+b) model used in the statistical inference about counting experiments, as explained in JINST 7 (2012) P01012, doi:10.1088/1748-0221/7/01/P01012, http://arxiv.org/abs/1108.4270. The values are useful to find approximate expressions which are quicker to compute than the original prior, as explained in http://arxiv.org/abs/1407.5893 (where this dataset is referred to).</p> <p>Each line is a sequence of spaces-separated values, and the file can be considered a table. The first line starts with two strings "shape" and "rate" which represent the titles of the corresponding columns in the data table. They refer to the shape and rate parameters defining the background prior. Next, N signal values starting from s=0 to s=70 are reported. They are the values at which π(s) is computed for any subsequent line.</p> <p>Starting from the second line, the format is always the same. The first two values are the shape and rate parameters defining the background prior used to compute π(s) in this line. Next, the N values π(s=0), ..., π(s=70) are reported. As π(0) = 1, the third column is constant (it might be useful to debug the data reading).</p> <p>As explained in http://arxiv.org/abs/1108.4270, simple functional forms may be used to fit the N points (s, π(s)). As the shape and rate parameters from the user's application may be different from those reported in this table, the following procedure shall give a very good approximation to π(s). In the (log(shape), log(rate)) parameters space, locate the neighboring points to the user's background parameter values (in log-log scale). Then interpolate each of the π(s) values to obtain a set of N values (a linear interpolation in log-log scale shall be sufficient). Finally, fit these interpolated values to find the reference prior for the user's application.</p>
Results: Predicted cooling effect, deaths prevented and associated economic value from public green spaces in Paris V2
<p>This dataset represents results predicting the cooling effect, deaths prevented and associated economic value for public green spaces in Paris for 40 hot days above the minimum mortality threshold in 2019. </p> <p>This is version 2. The value of a statistical life (VSL) has been corrcted and all values adjusted. </p> <p>The data format is a shapefile with coordinate reference system RGF93 v1 / Lambert-93 (EPSG:2154).</p> <p>Please see the Variable_name csv file for description of the variable names. </p> <p>The (non-reproducible) code is available at https://github.com/j-k-garrett/REGREEN_Paris_heat</p> <p>These results are from the submitted (September 2025) paper entitled:</p> <p><strong><span>Nature-Based Solutions for Urban Heat: Health and Economic Value of Paris’s Public Green Spaces</span></strong></p> <p>Authored by:</p> <p>Joanne K. Garrett<sup>1</sup>, David Neil Bird<sup>2</sup>, Timothy J. Taylor<sup>1</sup>, Elizabeth McCarthy<sup>3</sup>, David H. Fletcher<sup>4</sup>, Benedict W. Wheeler<sup>1</sup>, Marianne Zandersen<sup>5</sup>, Laurence Jones<sup>3</sup></p> <p><sup>1</sup>European Centre for Environment and Human Health, University of Exeter, Penryn, Cornwall, UK</p> <p><sup>2 </sup>Institute for Climate, Energy and Society, JOANNEUM RESEARCH, Graz, Austria</p> <p><sup>3</sup> Department of Environmental Studies, Schiller Institute for Integrated Science and Society, Boston College, USA</p> <p><sup>4</sup> UK Centre for Ecology & Hydrology, Environment Centre Wales, Bangor, Gwynedd, Wales, UK</p> <p><sup>5 </sup>Department of Environmental Science, iClimate Interdisciplinary Centre for Climate Change, Aarhus University, Denmark</p> <p> </p>
Wind Value Literature Selection for End-of-Life Valuation Excel File WP 5.1
<p>Authors from Wind Value, the Re-Wind Network and IEA Wind Task 45 carried out research on methods of processing end-of-life wind turbine blades. This included a structured literature review which selected the literature in the Excel file. Part of this work helps to estimate the value of an end-of-life wind farm contributing to Work Package 5.1 of the Wind Value project. The paper was pubished as Deeney et al. (2025) <a href="https://www.sciencedirect.com/science/article/pii/S1364032125000917?via%3Dihub">End-of-life wind turbine blades and paths to a circular economy</a>, <em>Renewable and Sustainable Energy Reviews. </em></p>
Raw band intensity values for ATG3 WT or Mutants in vitro LC3 lipidation
<p>Raw band intensity values used for the quantification of in vitro LC3 lipidation results (Fig4C) in Three-step docking by WIPI2, ATG16L1 and ATG3 delivers LC3 to the phagophore.</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.