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3,030 results for “green”
Snow grain data for Niwot Ridge and Green Lakes Valley, 1995 - ongoing.
Snow pits were excavated at various locations on Niwot Ridge and within the Green Lakes Valley. Temperature and snow density were measured at various depths throughout the snow cover profiles to characterize the temperature and snow water equivalent (SWE) of the snowpack throughout the year. Snow density was measured at 10-cm intervals using a 1000-ml cutter. Data on snow grain qualities were collected beginning in the 1994-95 snow season.
Snow cover profile data for Niwot Ridge and Green Lakes Valley, 1993 - ongoing.
Snow pits were excavated at various locations on Niwot Ridge and within the Green Lakes Valley. Temperature and snow density were measured at various depths throughout the snow cover profiles to characterize the temperature and snow water equivalent (SWE) of the snowpack throughout the year. Snow density was measured at 10-cm intervals using a 1000-ml cutter. Data on snow grain qualities were collected beginning in the 1994-95 snow season.
Dataset of "Asparagine-Modified Magnetic Graphene Oxide: An Efficient and Green Nanocatalyst for Synthesis of 5-oxodihydropyrano[3,2-c]chromenes and dihydropyrano[2,3- c]pyrazole derivatives and the Density functional theory calculation".
<p>The primary focus of this study involved the fabrication of a novel nanocatalyst Fe3O4-supported asparagine functionalized graphene oxide (Fe3O4@GO-N-(Asparagine)). The catalyst was synthesized through a four-step procedure.</p>
Microclimate data associated with green infrastructure in Lancaster Pennsylvania, 2022
Green stormwater infrastructure (GSI) is being increasingly implemented as a stormwater management practice. A key reason for its popularity is the potential for co-benefits, such as heat mitigation, in addition to stormwater management functions. This data is the result of field investigation of heat patterns around GSI in Lancaster, Pennsylvania, providing some of the first ever direct measurements of microclimate and thermal comfort near GSI. During summer 2022, we collected data along transects at 10 rain gardens. Microclimate variables were quantified using a Kestrel 5400 Heat Stress Tracker and were used to calculate metrics that represent heat stress experienced by a human such as wet bulb globe temperature (WBGT) and mean radiant temperature (MRT). Measurements were also made at nearby impervious surface and lawn reference sites.
Nutrient mineralization from green leaves in litterbags of three mesh sizes in the LUQ-LTER Canopy Trimming 2 Experiment
Hurricanes generate disturbances in forests that alter physicochemical characteristics of the habitat by opening the canopy and depositing fresh wood and leaves. Our objectives were to evaluate the effects of simulated hurricane driven changes to nutrient fluxes from litter to soil immediately following canopy disturbance. This study used three complete replicated blocks with two canopy treatments, control and trim+debris. Measurements were made in three 5 x 5 m subplots within 20 x 20 m plots nested in the 30 x 30 m treatment areas. Anion and cation resin membranes were inserted into the fermentation layer at the litter-soil interface and retrieved after one week. The measurement intervals were 2-4 weeks before canopy trimming, 0-1, 1-2, 2-3 and 4-5 weeks after trimming. Nutrient mineralization differed significantly between control and trim+detritus. Total N and P fluxes occurred at 4-5 weeks after canopy trimming. Litter decomposition depends primarily on the interaction among climate, litter quality and biota, so consequently any change in habitat will result in changes in these factors. Our objectives were to evaluate the effects of hurricane driven changes to forests on green litter decomposition, invertebrate communities and nutrient mineralization. This study used three complete replicated blocks with two canopy treatments, control and trim+debris. Measurements were made in three 5 x 5 m subplots within 20 x 20 m plots nested in the 30 x 30 m treatment areas. Green leaves were enclosed in litterbags of three different mesh sizes in each subplot. Litterbags were retrieved after 21, 35, 84 and 168 days; decomposer fauna was extracted and identified, mineralized nutrients were measured using ion resin membranes, and weight loss was determined. Arthropod abundance differed significantly through time. In addition, the number of arthropod taxonomic groups and nutrient mineralization differed significantly between control and trim+detritus, and nutrient mineralizat
Streamflow for Green Lake 4, 1981 - ongoing.
This is a summary of discharges from the upper Green Lakes Basin based on stage records from the outlet of Green Lake 4, and consists of daily flow volumes.
Spatial distribution of snow depth for the Green Lakes Valley, 1997 - 2019
Climate warming represents an abiotic driver for change in alpine ecosystems, potentially altering the seasonal snowpack and thus water availability into the surrounding landscape. Future changes in snow accumulation and snowmelt distribution may have profound impacts on the flora and fauna of alpine ecosystems. In this regard, recent research has leveraged multi-year estimates of the spatial distribution of snow water equivalent (SWE) toward understanding alpine ecosystem function. The purpose of this project is to investigate the spatial variability of maximum snow depth at Niwot Ridge on an inter-annual basis.
Annual snow survey, Green Lakes Valley, Niwot Ridge, Colorado, 2013 - ongoing.
Yearly snow surveys were conducted in the Green Lakes Valley in the City of Boulder Watershed at the estimated peak of snowpack in late spring. Over a period of several days, surveying teams (1 to several people) traversed valley slopes measuring snow depth with avalanche probes. Locations of each depth measurement were recorded as waypoints in Garmin hand-held GPS units. Snow depths were recorded on standardized field sheets along with dates, recorder names, waypoint numbers, and comments.
GREEN-VARAN scores resources (CADD GRCh37)
<p>Processed CADD scores to be used with GREEN-VARAN</p> <p>This dataset contains the GRCh37 version for CADD v.1.4.</p> <p>See: <a href="https://cadd.gs.washington.edu/">https://cadd.gs.washington.edu/</a></p> <p>If you use CADD score annotations with GREEN-VARAN don't forget to cite also the original CADD paper.</p>
GREEN-VARAN scores resources (FATHMM-XF GRCh38)
<p>Processed FATHMM-XF non-coding scores to be used with GREEN-VARAN</p> <p>This dataset contains the GRCh38 version for FATHMM-XF v2.3 non-coding annotations.</p> <p>See: <a href="http://fathmm.biocompute.org.uk/">http://fathmm.biocompute.org.uk/</a></p> <p>If you use FATHMM-XF score annotations with GREEN-VARAN don't forget to cite also the original FATHMM-XF paper.</p>
GREEN-VARAN scores resources (EIGEN GRCh38)
<p>Processed EIGEN and EIGEN-PC scores to be used with GREEN-VARAN</p> <p>This dataset contains the GRCh38 version for EIGEN v1.1 non-coding annotations, obtained by coordinates liftover.</p> <p>See: <a href="http://www.columbia.edu/~ii2135/eigen.html">http://www.columbia.edu/~ii2135/eigen.html</a></p> <p>If you use EIGEN score annotations with GREEN-VARAN don't forget to cite also the original EIGEN paper.</p>
# Replication code and data for: Tracking green space along streets of world cities
<p># Replication code and data for: Tracking green space along streets of world cities<br>Falchetta, G., & Hammad, A. T. (2025). Tracking green space along streets of world cities. Environmental Research: Infrastructure and Sustainability. https://doi.org/10.1088/2634-4505/add9c4 </p> <p>The file "gvi_358cities_2016_2023_yearly_falchetta_hammad.csv" contains<strong> output data</strong>, reporting sampling-point level data on the yearly (2016-2023) values of the Green View Index for the 190 cities covered in the paper AND an additional number of world cities (for a total of 358 cities). The "README_gvi_358cities_2016_2023_yearly_falchetta_hammad.txt" file contains a dictionary of each column name and units. </p> <p>____<br><br></p> <p>To replicate the analysis, the results, and the figures of the paper:</p> <ul> <li>Download input data from this Zenodo repository and code from Github https://github.com/giacfalk/urban_green_space_mapping_and_tracking</li> <li><em>*Optional data extraction steps* </em>(processed output data are already available in the Zenodo repository):<br> <ul> <li>Adjust your working directory</li> <li>Run [lines 4-11] of workflow/sourcer.R</li> <li>Run the Javascript scripts written by the string_generator_training.R and string_generator_prediction.R files in Google Earth Engine (https://code.earthengine.google.com) and complete the export to Drive tasks to generate the output .csv files</li> </ul> </li> <li>Run workflow/sourcer.R [lines 15-46] to train the ML model and make predictions (including figures and tables replication)</li> </ul> <div> <div> <div> </div> <div> <div> <div> </div> <div> <p dir="auto"> </p> <p dir="auto"> </p> </div> </div> </div> </div> </div> <div> <div> <div> </div> <div> <div> <div> </div> <div> <p dir="auto"> </p> <p dir="auto"> </p> </div> </div> </div> </div> </div>
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>
Identifying the mechanisms by which irrigation can cool urban green spaces in summer
<p>This dataset contains the measured soil moisture and microclimate data from two (2021 and 2022) urban green space irrigation experiments conducted in Burnley, Melbourne, Australia. The experiments consisted of two treatments, irrigated turf and unirrigated turf. The purpose of the experiments was to provide testing (2021) and evaluation (2022) data for an urban ecohydrological model, UT&C. </p> <p><br>After evaluating the performance of UT&C in modelling soil moisture and microclimate, UT&C was used to model the surface energy balance and evapotranspiration processes of the irrigated and unirrigated turf. This dataset also contains the modelled soil moisture, microclimate, surface energy balance and evapotranspiration data, as well as the measured background climate data at the reference climate station and the forcing data for the model.</p> <p><br>The aims of this study were to:<br>i) identify the proportional contribution of different evapotranspiration processes to irrigation cooling effect, and <br>ii) quantify the impacts of different irrigation amounts (from 2 to 30 mm/d) on the cooling effect of irrigating turfgrass in Melbourne, Australia during normal summer conditions.</p> <p>This study was published in:<br>Pui Kwan Cheung, Naika Meili, Kerry A. Nice, Stephen J. Livesley (2024). Identifying the mechanisms by which irrigation can cool urban green spaces in summer. Urban Climate. 55,101914. https://doi.org/10.1016/j.uclim.2024.101914.</p>
GREEN-VARAN additional regions resources
<p>Processed functional regions datasets to be used with GREEN-VARAN</p> <p>This repository contains the GRCh37 and GRCh38 files as indexed BED files. The GRCh38 version of UCNE and TAD were obtained by coordinate liftover.</p> <ul> <li>TFBS from ENCODE v3</li> <li>DNase hypersensitivity peaks from ENCODE v3</li> <li>UCNE (ultra-conserved non-coding elements) from https://ccg.epfl.ch/UCNEbase/</li> <li>TAD (topologically associating chromatin domains) from http://dna.cs.miami.edu/TADKB/</li> <li>Super enhancer from dbSuper at http://bioinfo.au.tsinghua.edu.cn/dbsuper/</li> </ul> <p>Please refer to the original datasets listed in related identifiers and references for eventual limits in use and distribution</p>
GREEN-VARAN scores resources (ncER)
<p>Processed ncER scores to be used with GREEN-VARAN</p> <p>This dataset contains the GRCh37 and GRCh38 versions for ncER v2 single-base resolution annotations. GRCh38 file is obtained by coordinates liftover.</p> <p>Original scores obtained from: https://github.com/TelentiLab/ncER_datasets </p> <p>Original publication: https://www.nature.com/articles/s41467-019-13212-3</p> <p>If you use ncER score annotations with GREEN-VARAN don't forget to cite also the original ncER paper.</p>
Decarbonizing primary steel production : Techno-economic assessment of green steel production in Norway
<p>Python codes for the modelling of a grid connected Hydrogen direct reduced plant combined with an electrical arc furnace for steel production. </p>
Data and code for the manuscript "From white to green: Snow cover loss and increased vegetation productivity in the European Alps"
<p>Data and code used for the manuscript "From white to green: Snow cover loss and increased vegetation productivity in the European Alps" by Rumpf et al., submitted December 2021 to Science</p> <p>See file ReadMe.txt for a description of the content and the original publication for further explanations.</p> <p>You are free to use these data and code for scientific purposes but are obliged to cite the above-mentioned publication.<br> For further questions, contact sabine.rumpf@unibas.ch</p>
Inclusive Green Growth Dataset for African Countries
<p><span>Tracking the progress of countries in inclusive green growth (IGG) is crucial for shaping effective sustainable development policies. However, comprehensive IGG data is often inaccessible. Accordingly, rigorous empirical contributions in this direction in the context of Africa remain sparse. To address this, we computed IGG scores for 22 African countries from 2000-2020. Our data reveal that only nine of these countries are achieving green and inclusive growth. This dataset equips researchers and institutions to assess IGG progress and identify pathways that African governments can leverage to promote sustainable development.</span></p>
Compilation of Digital Tools on Food Green House Gas Mitigation (CHOICE Project)
<p>A compilation of digital tools to support behaviour change and action to food mitigation measures. The compilation was created for the CHOICE Horizon Europe project (Grant Agreement -101081617).</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.