Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
292
datasets available to search
ShareScore release 0.9.0
Dataset results
292 results for “Recreation”
Fig. 3. Correlation coefficients estimated between water quality indicators and morphological Fig. 4 in Investigation Of Common Reed Regrowth On The Shores Of Recreational Lakes
Fig. 3. Correlation coefficients estimated between water quality indicators and morphological Fig. 4. The number of holidaymakers near parameters of common reeds on the shores of Bridvaisis, Gaustvinis and Gilius lakes (in the Bridvaisis, Gaustvinis and Gilius Lakes. The order from the bottom to the top) and differences boundary of the continuous line side indicates in morphological parameters of plants after the cases where p <0.01; dashed lines, where p <0.05. holidaymakers' visits.
Fig. 2 in Investigation Of Common Reed Regrowth On The Shores Of Recreational Lakes
Fig. 2. Differences in water temperature (graph on the left) and dissolved oxygen concentration (graph on the right) of Bridvaisis, Gaustvinis and Gilius lakes in May-September, 2017.
Fig.1 in Investigation Of Common Reed Regrowth On The Shores Of Recreational Lakes
Fig.1. Water temperature (graph on the left) and dissolved oxygen concentration (graph on the right) of Bridvaisis, Gaustvinis and Gilius lakes in May-September, 2017.
Fig. 2 in Dendrological And Recreational Values Of Arlaviskės Juniper Formation
Fig. 2. Reasons for visiting Arlaviškes Junipers Fig. 3. Types of management proposed by visiformationt. tors.
Data for recreating figures of scientific paper by Hermanson et al on volcanic impacts on climate
<p>This data is the data necessary to recreate the figures that appear in a draft manuscript submitted to the AGU journal Journal of Geophysical Research - Atmospheres for peer review. When / if the manuscript is accepted then the article will be linked from here. The data is in netcdf files. The data was created by processing (mainly averaging) data from five different institutions. It was given to the authors for the purpose of scientific research only.</p>
Fig. 1 in Species-specific qPCR assays allow for high-resolution population assessment of four species avian schistosome that cause swimmer's itch in recreational lakes
Fig. 1. Abundance of cercariae by sampling site. Water samples were obtained in mid-June and cercariae abundance was determined using the pan-avian schistosomes qPCR.
Fig. 2 in Species-specific qPCR assays allow for high-resolution population assessment of four species avian schistosome that cause swimmer's itch in recreational lakes
Fig. 2. Percent contribution of T. stagnicolae, T. szidati, T. physellae and A. brantae species to each lake. Water samples from different locations and dates were tested using the species-specific qPCR assay and results were pooled by lake to understand the relative contribution overall of each species to each lake. The percent contribution (based on gene copy number) of each species was calculated.
Fig. 3 in Species-specific qPCR assays allow for high-resolution population assessment of four species avian schistosome that cause swimmer's itch in recreational lakes
Fig. 3. Lifecycles of T. stagnicolae, A. brantae, T. szidati, and T. physellae. life cycle summary of the avian schistosome species targeted for species-specific qPCR tests designed in this study.
Linked collectors and determiners for: Vermont Lady Beetle (Coccinellidae) Specimens in the Vermont Forest, Parks and Recreation Insect Collection.
Natural history specimen data linked to collectors and determiners held within, "Vermont Lady Beetle (Coccinellidae) Specimens in the Vermont Forest, Parks and Recreation Insect Collection". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/c1e163c3-30f4-4230-a551-bbfc0677853e">https://bionomia.net/dataset/c1e163c3-30f4-4230-a551-bbfc0677853e</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/c1e163c3-30f4-4230-a551-bbfc0677853e">https://gbif.org/dataset/c1e163c3-30f4-4230-a551-bbfc0677853e</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Vermont Bark and Ambrosia Beetle Records - Vermont Department of Forest, Parks and Recreation.
Natural history specimen data linked to collectors and determiners held within, "Vermont Bark and Ambrosia Beetle Records - Vermont Department of Forest, Parks and Recreation". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/2ff00ea3-8fa2-41bf-9bf0-273ed628c1d8">https://bionomia.net/dataset/2ff00ea3-8fa2-41bf-9bf0-273ed628c1d8</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/2ff00ea3-8fa2-41bf-9bf0-273ed628c1d8">https://gbif.org/dataset/2ff00ea3-8fa2-41bf-9bf0-273ed628c1d8</a>. Formatted as a Frictionless Data package.
Multi-disciplinary survey data for the assessment of regulating and recreational ecosystem services in urban parks under heat and drought conditions
<p>The research group GreenEquityHEALTH provides quantified knowledge on how urban green spaces contribute to the mitigation of climate change induced challenges and challenges from urbanization to improve health, well-being and environmental justice. The project identifies the mediating pathways or direct effects of divers urban green spaces that act to either promote health, encourage healthy behaviours like social interaction or physical activity, or to decrease risk factors such as air pollution or urban heat.</p> <p>Here we present core data of our interdisciplinary multi-method campaigns that included in-situ stationary and aerial (remote sensing-based) environmental measurements, mobile air quality measurements, and social science-informed surveys, namely, park vistor observations and countings and a questionnaire survey.</p> <p><strong>List of data and content</strong></p> <ul> <li>Aarial_survey: digital surface model, orthophoto and thermal infrared images as raster files (*.tif); flight and processing report (*.pdf)</li> <li>Air_quality: stationary PM measurement data (*.csv); coordinates (*.txt)</li> <li>Meteorology: stationary air temperature and humidity data (*.csv), sensor meta data (*.csv)</li> <li>ParkVisitor_Surveys: survey data and questionnaire replies (*.csv), survey sheets (*.docx), questionnaire form (*.pdf)</li> </ul> <p><strong>Data acquisition and processing</strong></p> <p>For details on the data (e.g. sensors, calibration, survey settings) please refer to the linked publication, incl. Supplementary Material.</p> <p><strong>Acknowledgments</strong><br> We would like to thank the City of Leipzig, Department for Urban Green and Waters, for supporting the project. We would like to thank Henrique Miguel Pereira (Head of Research Group Biodiversity Conservation of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig) for providing equipment for the meteorological field campaigns. We also thank Anhalt University of Applied Sciences, Institute of Geoinformation and Surveying with Lutz Bannehr for conducting the airborne campaigns, Marco Pohle and Helko Kotas (both Helmholtz Centre for Environmental Research - UFZ) for technical support, and Judith Rakowski for support during the field surveys. This work was carried out within the research project ‘Environmental-health Interactions in Cities<br> (GreenEquityHEALTH) – Challenges for Human Wellbeing under Global Changes’ (2017 to 2022) funded by the German Federal Ministry of Education and Research (BMBF), funding code: 01LN1705A.</p> <p><strong>Related publication</strong><br> Kabisch, N. et al. (2021). A methodological framework for the assessment of regulating and recreational ecosystem services in urban parks under heat and drought conditions. <em>Ecosystems and People</em>. <a href="https://doi.org/10.1080/26395916.2021.1958062">doi:10.1080/26395916.2021.1958062</a></p>
The coevolution of effort and replication, recreated, replicated and corrected
<p>We replicated “The natural selection of bad science” by Paul Smaldino and Richard McElreath (2016). The replication was successful with one exception. We find that selection acting on scientist’s propensity for replication frequency caused a brief period of exuberant replication not observed in the original paper due to a coding error. This difference does not, however, change the authors’ original conclusions.</p> <p>The three panels displayed here concern Figure 5 – titled “The coevolution of effort and replication” – of the original study. Panel (a) is based on the original data and is a recreation of the original figure. Panel (b) is the result of a replication based on the same coding error and panel (c) displays the corrected result.</p> <p>While effort, false positive rate (α), and false-discovery rate converge to the originally reported values when the simulated steps are extended beyond the original 1e6 time steps, the replication rate’s progress and convergence are different from the original.</p> <p>The results of this corrected model show the following pattern: Starting with a high effort, low effort replications are more attractive than conducting novel research (that is, employing this strategy received higher payoffs), which results in the replication rate reaching nearly 100% after ~730,000 steps. At this point the decline of effort has made low-effort novel research more attractive than low-effort replications (because publishing a novel positive result is associated with a higher payof than publishing a replication) and consequently the replication rate decreases again. With the decline of effort, alpha rises up to 0.67, comparable with the value reported in the study by Smaldino and McElreath (2016).</p> <p>Smaldino, P. E., & McElreath, R. (2016). The natural selection of bad science. <em>Royal Society Open Science</em>, <em>3</em>(9), 160384. <a href="https://doi.org/10.1098/rsos.160384">https://doi.org/10.1098/rsos.160384</a></p>
Recreational Value of the NBS for Valladolid city
<p>Key Performance Indicator calculated for the H2020 UrbanGreenUP project for Valladolid city. </p>
Anisotropic Gold Nanomaterial Synthesis Using Peptide Facet Specificity and Timed Intervention: Experiment and simulation data, figures, and notebooks for recreating figures
<p><strong>/Data</strong></p> <p>Contains all experimental and simulation data used to generate figures in the manuscript. Each folder is labelled with the relevant technique: atomic force microscopy (AFM), dynamic light scattering (DLS), molecular dynamics (MD), small-angle X-ray scattering (SAXS), scanning electron microscopy (SEM), transmission electron microscopy and selected area electron diffraction (TEM-SAED), and ultraviolet-visible light spectroscopy (UV-Vis). Folders may contain subfolders from different experiments and should be unchanged for the notebooks to point to the correct path when loading data. Any file which contains SI in the filename indicates sample information, which includes sample concentration, delay associated with creating the sample (Delay4 is the relevant column name), unique identification (UID), etc.</p> <p><strong>/Data/AFM</strong></p> <p>Each filename is labelled with the number of delay associated with the sample or No Z2 Control in the case of the control sample. Each sample contains 3 files, the raw AFM data, a processed image, and an excel file with line profile measurements.</p> <p><strong>/Data/DLS</strong></p> <p>Contains exported data from Malvern Nano ZS DLS instrument.</p> <p><strong>/Data/Images</strong></p> <p>Images from the colloidal stability experiment.</p> <p><strong>/Data/MD</strong></p> <p>Molecular dynamics data and some notebooks for processing peptide and facet combinations.</p> <p><strong>/Data/SAXS</strong></p> <p>Reduced SAXS data from time-resolved study of nanoplatelet growth.</p> <p><strong>/Data/SEM</strong></p> <p>SEM images of samples prepared with and without dynamic intervention (control).</p> <p><strong>/Data/TEM_SAED</strong></p> <p>TEM images of samples prepared with and without dynamic intervention (control), and SAED image of a nanoplatelet.</p> <p><strong>/Data/UV-Vis</strong></p> <p>Spectroscopy data from different Z2 variants, the microplate based stability assay, cuvette based stability assay, and the time resolved (kinetics) measurements of particle growth as a result of manual dynamic intervention.</p> <p><strong>/Notebooks</strong></p> <p>This folder contains all of the Jupyter notebooks used to plot data and fit SAXS scattering profiles.</p> <p>See <strong>/Notebooks/environment.yml</strong> for packages necessary to execute the notebooks here and in <strong>/Synthesis_Protocol</strong>. We recommend installing this environment by using:</p> <p> </p> <p>Refer to <a href="https://github.com/SasView/sasmodels">https://github.com/SasView/sasmodels</a> and the first cell of <strong>/Notebooks/SAXS_Fitting.ipynb</strong> for specific instructions on how to complete installation of the sasmodels module (sasmodels will be installed by Pip if you correctly use the shared environment.yml file).</p> <p><strong>/Figures</strong></p> <p>Figures presented in the publication</p>
Data from: Insights from a 31-year study demonstrate an inverse correlation between recreational activities and red deer fecundity, with body weight as a mediator
Open the record for dataset details and reuse information.
Recreating Fall Risk Appraisal (FRA) matrix using R to support fall prevention programs
Open the record for dataset details and reuse information.
Data from: Quantifying impacts of recreation on elk (Cervus canadensis) using novel modeling approaches
Open the record for dataset details and reuse information.
Data and code from: Recreational fisheries selectively capture and harvest large predators
Open the record for dataset details and reuse information.
Data from: Habitat selection and outdoor recreation explain human-carnivore conflict
Open the record for dataset details and reuse information.
Summary of the commercial and recreational striped bass fisheries conducted in Massachusetts from 1986 to present.
This dataset summarizes the commercial and recreational striped bass fisheries conducted in Massachusetts. Data sources used to characterize the state fisheries come from monitoring programs of the Massachusetts Division of Marine Fisheries (Marine Fisheries, the Division) and National Marine Fisheries Service (NOAA Fisheries), which are considered to be essential elements of the long-term management approach described in Section 3 of the Atlantic States Marine Fisheries Commission’s (ASMFC) Fisheries Management Report No. 41 (Amendment #6 to the Interstate Fishery management Plan for Atlantic Striped Bass (IFMP)). Fisheries data are compiled from four Massachusetts regions (Cape Cod Canal, Southern Massachusetts, Cape Cod Bay, Northern Massachusetts). Data begins with year 1986 and is updated to present years as data is made available.
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.