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Dataset results
53 results for “environmental science”
Weather Data, 1940-2016, Adirondack Long-Term Ecological Monitoring Program by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York, USA
These datasets include information collected from 1940 to 2016 in or around Huntington Wildlife Forest at the Adirondack Ecological Center in Newcomb, New York. Data were collected daily and include minimum and maximum daily temperature, precipitation, snowfall, and sometimes additional measurements (e.g., wind direction, weather events). These data were collected as part of the Adirondack Long Term Ecological Monitoring Program (ALTEMP).
Supplementary Table 1. Characteristics of the Group 1 data journals that publish in the fields of biology, environmental science, chemistry, medicine, and health sciences
<p>Supplementary Table 1. Characteristics of the Group 1 data journals that publish in the fields of biology, environmental science, chemistry, medicine, and health sciences.</p> <p>This file is a digital supplement to William H. Walters, "Data journals: incentivizing data access and documentation within the scholarly communication system," <em>Insights: The UKSG Journal</em> 33, article 18 (June 10, 2020), 1–20, <a href="http://doi.org/10.1629/uksg.510">http://doi.org/10.1629/uksg.510</a>.</p>
Data for Olive et al., Removal of waterborne viruses by Tetrahymena pyriformis is virus-specific and coincides with changes in protist swimming speed, Environmental Science and Technology, 2022 (https://doi.org/10.1021/acs.est.1c05518)
<p>This entry contains the data shown in: Olive et al., <em>Removal of waterborne viruses by Tetrahymena pyriformis is virus-specific and coincides with changes in protist swimming speed,</em> Environmental Science and Technology, 2022 (https://doi.org/10.1021/acs.est.1c05518)</p> <p>Net removal values (log10 C/C0 or log10 N/N0) shown in Figures 1 and 4</p> <p>Raw data used to calculate net removal values in Figure 1</p> <p>Raw removal values shown in Figure 2</p> <p>Raw data for protist movement analysis shown in Figure 3</p> <p>R code used for protist movement analysis (as text file)</p> <p>Raw data for all Supporting Figures (S1-S6)</p>
Linked collectors and determiners for: Estonian University of Life Sciences Institute of Agricultural and Environmental Sciences Vascular Plant Herbarium.
Natural history specimen data linked to collectors and determiners held within, "Estonian University of Life Sciences Institute of Agricultural and Environmental Sciences Vascular Plant Herbarium". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/6b5e4c2d-127f-492e-bf1c-2026d515e0de">https://bionomia.net/dataset/6b5e4c2d-127f-492e-bf1c-2026d515e0de</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/6b5e4c2d-127f-492e-bf1c-2026d515e0de">https://gbif.org/dataset/6b5e4c2d-127f-492e-bf1c-2026d515e0de</a>. Formatted as a Frictionless Data package.
[DATA_SCIENCE] Interviews: The Medical and Environmental Data Mash-up Infrastructure (MEDMI)
<p>This is a set of interview transcripts executed by Niccolò Tempini between March and October 2016, as part of the ERC project "The Epistemology of Data-Intensive Science", and in the context of a case study of MEDMI. Please read the "Notes on transcript editing" document for further information.</p> <p>Three papers or chapters that specifically make use of these interviews have been published as of 2020:</p> <ul> <li>Tempini, N., Leonelli, S., 2018. Concealment and discovery: The role of information security in biomedical data re-use. Soc Stud Sci 48, 663–690. <a href="https://doi.org/10.1177/0306312718804875">https://doi.org/10.1177/0306312718804875</a></li> <li>Leonelli, S., Tempini, N., 2018. Where health and environment meet: the use of invariant parameters in big data analysis. Synthese 1–20. <a href="https://doi.org/10.1007/s11229-018-1844-2">https://doi.org/10.1007/s11229-018-1844-2</a></li> <li>Tempini, N. 2020. The Reuse of Digital Computer Data: Transformation, Recombination and Generation of <em>Data Mixes</em> in Big Data Science. In: Leonelli S., Tempini N. (eds) Data Journeys in the Sciences. Springer, Cham. <a href="https://doi.org/10.1007/978-3-030-37177-7_13">https://doi.org/10.1007/978-3-030-37177-7_13</a></li> </ul> <p>The transcripts document MEDMI researchers' experience of infrastructure development and data curation and re-use practices. Researchers have consented to have these transcripts made available as Open Data. Other interviewees did not give consent, so those transcripts are held securely by the research team in Exeter.<br> You also find the information sheet provided to interviewees, which gives you the context for this project. Further information and related publications can be found at www.datastudies.eu.</p>
Supplementary material 1 from: Thaung R, Frechette J, Luskin MS, Amir Z (2023) Combining Camera Trap Data and Environmental Data to Estimate the Effects of Environmental Gradients on Abundance of the Asian Elephant Elephas maximus in Cambodia. Biodiversity Information Science and Standards 7: e112100. https://doi.org/10.3897/biss.7.112100
Environmental Variables Used in the study
Post-fire Debris Flows: Leveraging Science for Environmental Management and Community Resiliency
Open the record for dataset details and reuse information.
Data from: Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community
<p>It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on > 100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 years are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human-environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community's use of NEON data, and opportunities for the next 10 years of NEON operations in emergent science themes, open science best practices, education and training, and community building.</p>
Figure 1 from: Neittaanmäki P, Huttula T, Karvanen J, Frisk T, Tuomisto J, Simola A, Tuovinen T, Ropponen J (2016) Unicorn–Open science for assessing environmental state, human health and regional economy. Research Ideas and Outcomes 2: e9232. https://doi.org/10.3897/rio.2.e9232
Figure 1 - Links and interactions between the work packages
Figure 2 from: Neittaanmäki P, Huttula T, Karvanen J, Frisk T, Tuomisto J, Simola A, Tuovinen T, Ropponen J (2016) Unicorn–Open science for assessing environmental state, human health and regional economy. Research Ideas and Outcomes 2: e9232. https://doi.org/10.3897/rio.2.e9232
Figure 2 - Time line of the tasks in UNICORN-project
Using Focus Groups to Assess the Impact of Environmental Health Science Programs for K-12 Educational Community
ClinicalTrials.gov study NCT00428402. IPD Sharing: Not stated. Countries: 1. Publications: 0.
De Oorzaak: Citizen Science Project on the Impact of Environmental Noise
ClinicalTrials.gov study NCT06466668. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Data from: Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community
Open the record for dataset details and reuse information.
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.