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54 results for “environmental productivity”
Data for: Techno-economic and environmental assessment of converting mixed prairie to renewable natural gas with co-product hydroxycinnamic acid, Iowa, USA, 2022-2023.
This dataset compiles model outputs, parameter sets, and documentation supporting a techno‑economic analysis (TEA) and life‑cycle assessment (LCA) of co‑digesting beef cattle manure with pretreated mixed prairie biomass to produce renewable natural gas (RNG), with hydroxycinnamic acids (HCA) and digestate‑derived biochar co‑products. It accompanies the study by Katherine Wild, Elmin Rahic, Lisa A Schulte Moore, and Mark Mba Wright "Techno-economic and environmental assessment of converting mixed prairie to renewable natural gas with co-product hydroxycinnamic acid," in Biofuels, Bioproducts, & Biorefining, 2024 (https://doi.org/10.1002/bbb.2710). The integrated simulation and assessment framework quantifies process performance, economics, and greenhouse‑gas intensity across five scenarios representing combinations of alkaline‑ethanol pretreatment for HCA extraction, liquid recirculation fractions, and biochar addition. This data collection includes: stream‑level mass flow/composition tables for each scenario; RNG, biochar, and HCA annual production summaries; literature‑based methane/biogas yield benchmarks; equipment‑level capital costs; TEA assumptions; emission‑factor inventories and displacement credits; and full sensitivity/uncertainty matrices for MFSP and GWP.
Life cycle inventories for the article: Circular Battery Production in the EU: Insights from integrating Life Cycle Assessment into System Dynamics Modeling on Recycled Content and Environmental Impacts
<p>This repository provides the unregionalized life cycle inventories to the paper "<span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a>".</p> <h2>Contents</h2> <p>The repository is split into 2 parts and comprises the following files:</p> <p><strong>01_production: </strong>contains the necessary life cycle inventories for battery production.</p> <ul> <li><strong>01_primary</strong>: contains the life cycle inventories for battery production from primary materials.</li> <li><strong>02_secondary</strong>: contains the life cycle inventories for battery production from secondary materials.</li> <li><strong>03_active_material</strong>: contains the life cycle inventories for the active battery materials from primary materials.</li> <li><strong>04_active_material</strong>: contains the life cycle inventories for the active battery materials from secondary materials.</li> </ul> <p> </p> <p><strong>02_recycling: </strong>contains the necessary inventories for battery recycling.</p> <ul> <li><strong>01_process</strong>: contains the life cycle inventories for battery recycling.</li> <li><strong>02_intermediate</strong>: contains the life cycle inventories for the intermediate system for battery recycling.</li> <li><strong>03_output</strong>: contains the life cycle inventories for the resulting substances from battery recycling.</li> </ul> <h2>Summary</h2> <p>These files allow to reproduce the results of our study. Each file contains the life cycle inventory of one distinct battery capacity (20, 45, 68, 85, 95, 100 kWh) with a specific cell chemistry (LFP, NCA, NMC333, NMC532, NMC622, NMC811, NMC955) for battery production (based on Knehr et al. 2022) or for battery recycling (based on Blömeke et al. 2023).</p> <h2>Related publication</h2> <p>More details on the scientific context is provided in the publication itself:</p> <p><span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a></p> <h2>Funding</h2> <p>This publication (Raphael Ginster and Steffen Blömeke) was created within the Research Training Group CircularLIB, supported by the Ministry of Science and Culture of Lower Saxony with funds from the program zukunft.niedersachsen of the Volkswagen Foundation (MWK | ZN3678).</p> <p>The publication on which this dataset is based were funded by the German Federal Ministry of Education and Research within the Competence Cluster Recycling & Green Battery (greenBatt) under the grant numbers 03XP0302A (Christian Scheller) and 03XP0331A (Jan-Linus Popien). The authors are responsible for the contents of this publication.</p>
Dataset and R script for the analysis in the article "Food waste between environmental education, peers, and family influence. Insights from primary school students in Northern Italy", Journal of Cleaner Production
<p>We hereby publish the dataset (with metadata) and the R script (R Core team, 2018) used for implementing the analysis presented in the paper "Food waste between environmental education, peers, and family influence. Insights from primary school students in Northern Italy", <em>Journal of Cleaner Production </em>(Piras et al., 2023). The dataset is provided in csv format with semicolons as separators and "NA" for missing data. The dataset includes all the variables used in at least one of the models presented in the paper, either in the main text or in the Supplementary Material. Other variables gathered by means of the questionnaires included as Supplementary Material of the paper have been removed. The dataset includes inputted values for missing data on independent variables. These were inputted using two approaches: last observation carried forward (LOCF) - preferred when possible - and last observation carried backward (LOCB). The metadata are presented as a PDF file.</p>
European consumers ́ preference and willingness to pay for food products labelled as obtained by a circular farming system -in relation to environmental attitudes and consumption behaviours
<p>Data was collected with questionnaire-based research carried out in Belgium, Croatia, Hungary, Italy, Poland, and Spain as part of a European project. The survey questions were designed to obtain the Willingness to pay using 2 different methodologies the discrete choice experiment and the open-end choice experiment. The survey also included questions about consumers environmental attitudes, and consumption behavior (purchase, use and recycling), to identify if them have influence on preferences towards more sustainable food products. The 3 analyzed food products were pork meat, milk and bread, all of them obtained through different agricultural production systems (circular, conventional, and organic agriculture). The sample was stratified in terms of gender and age to be representative to the average population in each country. Furthermore, respondents included in this study were those that are mainly, or in part responsible for the household food shopping. The questionnaire was translated to the languages of the countries involved in the data collection and pre-launched using a pilot sample of 50 consumers in each case study country. Finally, a total of 5,362 validated questionnaires were obtained. Data was collected online using the Qualtrics market research company, and Net panel market company for Hungary from June 2021 to January 2022.</p>
Gross methane production and consumption estimated for intact soil cores from agricultural plots including environmental covariates and example raw isotope pool dilution data
This study was performed to determine how different soil moistures, soil sources, and agricultural practices affected the gross CH4 fluxes (i.e., rates of methanogenesis) of soils. We extracted intact soil cores from two agricultural sites in the USA in row crop plots under conventional, no-till, and organic management. We then took them to the lab, manipulated their moisture levels, incubated them at room temperature for 22 weeks, and measured gas fluxes at weeks 6 and 21. We developed and utilized a new form of CH4 isotope pool dilution (IPD) to estimate gross CH4 production and consumption fluxes. This new method can measure IPD in a bag headspace that loses volume over time due to sampling. We fit the IPD model to the data and extracted gross CH4 production (P) and consumption (K) constants. These along with calculated fluxes and covariates measured (e.g., moisture, inorganic N) are reported in the main data table.
Repository: Quantifying environmental impacts of primary aluminum ingot production and consumption: A trade-linked multilevel life cycle assessment
<p>This repository contains the input data, codes and results of the model developed in the paper "Quantifying environmental impacts of primary aluminum ingot production and consumption: A trade-linked multilevel life cycle assessment" published in the Journal of Industrial Ecology (2020) by Alexandre Milovanoff, I. Daniel Posen, Heather L. MacLean.</p>
A global gross primary productivity product considering canopy nitrogen concentrations and multiple environmental factors from 2001 to 2018
<p>The <strong>NI-LUE GPP</strong> with 0.05° spatial resolution and at 8 days interval from 2001 to 2018 was generated based on an improved light use efficiency (LUE) model that simultaneously considered temperature, water, atmospheric CO<sub>2</sub> concentrations, radiation components, and nitrogen (N) index. In the model, a vegetation index capable of characterizing canopy N concentrations was selected to achieve dynamic mixmum LUE. In addition, global optimum temperature distributions mapped based on satellite-retrieved SIF were introduced to calculate the temperature stress factor. This dataset includes<strong> global GPP product</strong> and its <strong>uncertainty data</strong>.</p> <p>Period: 2001-2018</p> <p>Spatial resolution: 0.05°</p> <p>Temporal resolution: 8 days</p> <p>Projection: geographic latitude/longitude</p> <p>Data format: Tiff</p> <p>Upper left coordinates: -180°E, 90°N</p> <p>Scale factor: 1000</p> <p>Unit: gCm-2d-1</p>
Supplement code and data for "A global multi-indicator assessment of the environmental impact of livestock products"
<p>This data and code supplements the publication "A global multi-indicator assessment of the environmental impact of livestock products" by Giorgio A. Bidoglio, Florian Schwarzmueller, Thomas Kastner available here: https://doi.org/10.1016/j.gloenvcha.2024.102853<br><br></p> <p>Version 1.1 add the code to the visualization tool available online at https://livestockimpactassessment.shinyapps.io/multi-indicator_impact_assessment_of_livestock_products/</p>
Meta-analysis on Stakeholder Engagement in the Co-Production of Knowledge for Environmental Decision-making
<p>This dataset corresponds to a meta-analysis to examine the peer-reviewed scholarship on stakeholder engagement in co-production processes in environmental decision-making.</p> <p>The dataset was developed by searching keywords, abstracts, and titles in Scopus on three key themes: co-production, transdisciplinary, and community-based participatory research. A total of 709 publications were reviewed, from which 144 met the selection criterion. The 144 articles constitute this dataset. They pertain to articles published between January 2005 and June 2020.</p> <p>The data includes information on the following topics:</p> <ol> <li>Number of unique case studies reported</li> <li>The projects’ time span</li> <li>When stakeholder engagement activities occur during the project cycle</li> <li>Are stakeholders (non-university researchers) an author</li> <li>Geographic region of project</li> <li>Environmental issues addressed</li> <li>Geographic scale of project </li> <li>Types of stakeholders engaged </li> <li>How stakeholders were engaged</li> <li>What output resulted from stakeholder engagement</li> <li>Approach to stakeholder engagement</li> <li>Factors that enabled stakeholder engagement</li> <li>Barriers or challenges to engagement</li> <li>Societal impacts associated with stakeholder engagement</li> <li>Direct evidence of societal impacts of stakeholder engagement</li> </ol> <p>The methods and analysis are presented in "Stakeholder Engagement in the Co-Production of Knowledge for Environmental Decision-making," which was submitted for publication in 2022 to the Journal <em>World Development</em>. A draft of the manuscript can be provided upon request to authors.</p>
Fine-root production in boreal peatland forests: effects of stand and environmental factors
<p>Fine-root production (FRP) data along with climatic variables (annual precipitation, temperature sum, and latitude) and stand variables (tree stand stem volume; tree stand basal area; stand basal area of tree species including Scots pine, Norway spruce and deciduous trees; site type; peat type; peat depth; C:N ratio of topmost 20 cm peat layer; grouping of sites to nutrient rich and nutrient poo; average soil water-table level) from 28 forestry-drained peatland forest sites in Finland,</p> <p>FRP and its depth distribution were estimated using ingrowth cores. The ingrowth cores were installed between October 15th and November 27th, 2013, and recovered after two years in late November 2015.</p>
Supporting Information from The Next Frontier of Environmental Unknowns: Substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials (UVCBs)
<p>Supporting Information (Open Data) from The Next Frontier of Environmental Unknowns: Substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials (UVCBs)</p>
Environmental Performance Assessment of a Novel Process Concept for Propanol Production from Widely Available and Wasted Methane Sources
<p>Dataset for associated publication.</p>
Beef database for prediction of main environmental challenges to resilience and efficiency in cattle production systems at regional resolution
<p>The beef database comprises average values for a wide range of variables (110 or 119), available in 4 worksheets, and is further differentiated into All Beef, Breeders and Fatteners: BasicFarmType (18 rows), DetailedFarmType (75 rows), ClimateClass+BasicFarmType (270 rows), NUTS+BasicFarmType (2074 rows). Data are omitted when the sample size (n) is below 15, as per the confidentiality agreement under FADN data use rules.</p> <p>A combined farm characterisation database was constructed using two major data sources, the Farm Accountancy Data Network (FADN), and the Gridded Agro-Meteorological Data in Europe (AGRI4CAST). The database initially constructed was further enhanced through the addition of forage and crop yield data from the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) developed Agro-Ecological Zones (AEZ) methodology database (FAO, 2012). The data was processed and is presented in D1.2 as two databases (dairy and beef), as averages for a wide range of variables at basic or detailed farm types, and at NUTS2 regional scale.</p> <p>Detailed FADN data (anonymised individual farm data) was requested for all ruminant and mixed farm types, over 10 years and the most recent data available at request (2011-2013) was utilised for the analysis. Following receipt of the data (~250k farms) this has been compiled into two consistent datasets, one for dairy (141,961) farms and one for beef farms (54,417). Each dataset comprises some values directly from the FADN data, but also a large number of calculated variables, to identify dairy or beef enterprise performance at per animal, per output product unit or per hectare. These values were calculated according to the respective dairy and beef enterprise allocation methodologies described by FADN. Further economic and structural variables have been calculated as necessary, as described in GenTORE D1.1 (Quiédeville et al., 2019).</p> <p>For each farm within the dataset, the structural, production and economic data from the FADN data is supplemented with the addition of meteorological data. The daily meteorological data was downloaded from the AGRI4STAT database web portal at a NUTS2 scale. For each NUTS2 region data was available for a number of weather stations. This large dataset was processed through scripts in STATA software to generate annual values for a wide range of climatic variables, including Temperature Humidity Index (THI), and indicators of drought and seasonality of weather. Furthermore, the altitude values per weather station allowed for a sub-grouping of weather station data by altitude zone (aligned with values available in the FADN dataset).</p> <p>Using a Latent Class Analysis process, the meteorological data was analysed to identify consistent environmental regions in Europe. Selected climatic variables, together with altitude zone, were utilised to statistically identify differing zones, and to classify each NUTS2 region to a zone, resulting in 6 lowland zones and 3 upland zones (above 600m) The LCA process enhanced an earlier method of manually overlaying the Metzger et al. (20054) pedo-climatic zone allocation, but closely correlates. Therefore for each farm in the dairy and beef datasets, meteorological and environmental zone data was allocated on a NUTS2 by altitude zone basis and this dataset has been subsequently assessed and submitted as papers; Quiédeville et al., (submitted May 2020) and Grovermann et al. (submitted May 2020).</p> <p>The GAEZ forage and crop yield data was downloaded from the GAEZ data portal as baseline and two future climate prediction periods: Baseline (1961-2000), 2020s (2011-2040), and 2050s (2041-2070), for the Hadley CM3 model and IPCC scenario A (the most extreme scenario). See: <a href="http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/">http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/#</a>). A zonal statistics was applied to the GAEZ layers to aggregate the data to NUT2 region and altitude zone (0-300m, 300-600m, 600m+) with raster package in R. The result is an average yield<a href="#_ftn1">[1]</a> for varying forages and crops for each altitude zone in each nuts2, for both the baseline and the future climate scenario. This data allows further analysis of the future impacts on cattle farming at both a regional scale, but also by farm type or system, which may be affected differently (Moakes et al. in preparation).</p> <p>All variable processing from FADN data is shown in the Annex, as performed in Stata software.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> The mean was performed on non-zero yield pixels in order to exclude non-suitable areas from average.</p>
Dairy database for prediction of main environmental challenges to resilience and efficiency in cattle production systems at regional resolution
<p>The dairy database comprises average values for a wide range of variables (110 or 119), available in 4 worksheets: BasicFarmType (18 rows), DetailedFarmType (10 rows), ClimateClass+BasicFarmType (100 rows), NUTS+DetailedFarmType (1452 rows). Data are omitted when the sample size (n) is below 15, as per the confidentiality agreement under FADN data use rules.</p> <p>A combined farm characterisation database was constructed using two major data sources, the Farm Accountancy Data Network (FADN), and the Gridded Agro-Meteorological Data in Europe (AGRI4CAST). The database initially constructed was further enhanced through the addition of forage and crop yield data from the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) developed Agro-Ecological Zones (AEZ) methodology database (FAO, 2012). The data was processed and is presented in D1.2 as two databases (dairy and beef), as averages for a wide range of variables at basic or detailed farm types, and at NUTS2 regional scale.</p> <p>Detailed FADN data (anonymised individual farm data) was requested for all ruminant and mixed farm types, over 10 years and the most recent data available at request (2011-2013) was utilised for the analysis. Following receipt of the data (~250k farms) this has been compiled into two consistent datasets, one for dairy (141,961) farms and one for beef farms (54,417). Each dataset comprises some values directly from the FADN data, but also a large number of calculated variables, to identify dairy or beef enterprise performance at per animal, per output product unit or per hectare. These values were calculated according to the respective dairy and beef enterprise allocation methodologies described by FADN. Further economic and structural variables have been calculated as necessary, as described in GenTORE D1.1 (Quiédeville et al., 2019).</p> <p>For each farm within the dataset, the structural, production and economic data from the FADN data is supplemented with the addition of meteorological data. The daily meteorological data was downloaded from the AGRI4STAT database web portal at a NUTS2 scale. For each NUTS2 region data was available for a number of weather stations. This large dataset was processed through scripts in STATA software to generate annual values for a wide range of climatic variables, including Temperature Humidity Index (THI), and indicators of drought and seasonality of weather. Furthermore, the altitude values per weather station allowed for a sub-grouping of weather station data by altitude zone (aligned with values available in the FADN dataset).</p> <p>Using a Latent Class Analysis process, the meteorological data was analysed to identify consistent environmental regions in Europe. Selected climatic variables, together with altitude zone, were utilised to statistically identify differing zones, and to classify each NUTS2 region to a zone, resulting in 6 lowland zones and 3 upland zones (above 600m) The LCA process enhanced an earlier method of manually overlaying the Metzger et al. (20054) pedo-climatic zone allocation, but closely correlates. Therefore for each farm in the dairy and beef datasets, meteorological and environmental zone data was allocated on a NUTS2 by altitude zone basis and this dataset has been subsequently assessed and submitted as papers; Quiédeville et al., (submitted May 2020) and Grovermann et al. (submitted May 2020).</p> <p>The GAEZ forage and crop yield data was downloaded from the GAEZ data portal as baseline and two future climate prediction periods: Baseline (1961-2000), 2020s (2011-2040), and 2050s (2041-2070), for the Hadley CM3 model and IPCC scenario A (the most extreme scenario). See: <a href="http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/">http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/#</a>). A zonal statistics was applied to the GAEZ layers to aggregate the data to NUT2 region and altitude zone (0-300m, 300-600m, 600m+) with raster package in R. The result is an average yield<a href="#_ftn1">[1]</a> for varying forages and crops for each altitude zone in each nuts2, for both the baseline and the future climate scenario. This data allows further analysis of the future impacts on cattle farming at both a regional scale, but also by farm type or system, which may be affected differently (Moakes et al. in preparation).</p> <p>All variable processing from FADN data is shown in the Annex, as performed in Stata software.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> The mean was performed on non-zero yield pixels in order to exclude non-suitable areas from average.</p>
Effects of water source and technology on energy use and environmental impacts of rice production in northern Iran
<p>To analyze the energy flow and greenhouse gas emissions, a total of 200 paddy fields were selected based on the water source utilized for irrigation (river as the surface water source and well as the underground water source), transplanting methods (traditional and mechanical) and grain yield (low- and high-yielding rice cultivars) as a factorial experimental design (2×2×2=8 conditions). Data were collected in 2020 from 200 rice growers in northern Iran using a face-to-face questionnaire survey.</p>
Computer-aided design of optimal environmentally benign solvent-based adhesive products
<p>The files contain all the product design problems implemented in GAMS for this publication. All models were run on a single core of a dual 8 core Intel(R) Xeon(R) CPU E5-2650 machine at 3.52 GHz with 125GB of memory.</p> <p> </p> <p><strong>Abstract</strong></p> <p>In this work, a general systematic methodology for the design of optimal adhesive products with low environmental impact is presented. The proposed approach integrates computer-aided design tools and Generalised Disjunctive Programming to formulate and solve the product design problem. Key design decisions in product design (number of ingredients, identity of compounds and their proportions) are optimised simultaneously. This methodology is applied to the design of solvent-based acrylic adhesives, which are commonly used in construction. First, optimal product formulations are determined with the aim to minimize toxicity. This reveals that that high performance can be achieved by investigating different number of components as well as by optimising all ingredients simultaneously rather than sequentially. The relation between two competing objectives is then explored by obtaining a set of Pareto optimal solutions. This leads to significant trade-offs and large areas of discontinuity driven by discrete changes in the list of optimal product ingredients.</p>
Pre-analysis and figure data for Lomax et al. (2024), Untangling the environmental drivers of gross primary productivity in African rangelands
<p>Data required to reproduce main analyses and main text figures for the following publication:</p> <p>Lomax, G. A., Powell, T. W. R., Lenton, T. M., Economou, T., and Cunliffe, A. M. (in press), Untangling the environmental drivers of gross primary productivity in African rangelands.</p> <p> </p> <p>File details:</p> <ol> <li>df_annual.csv - the full 19-year dataset of model variables for reproducing the main analysis (for Figures 2-3).</li> <li>df_multi_annual.csv - a smaller dataset of multi-annual mean and variability variables for reproducing the analysis behind Figure 4.</li> <li>Fig1_data.tif - raster dataset containing the four variables shown in Figure 1.</li> <li>Fig2_data.csv - model results for the main analysis underlying Figure 2.</li> <li>Fig3_data.csv - model results for the binned analysis underlying Figure 3.</li> <li>Fig4_data.csv - model results for the multi-annual analysis underlying Figure 4.</li> </ol>
Data from: High environmental stress and productivity increase functional diversity along a deep-sea hydrothermal vent gradient
<p>Productivity and environmental stress are major drivers of multiple biodiversity facets and faunal community structure. Little is known on their interacting effects on early community assembly processes in the deep sea (>200 m), the largest environment on Earth. However, at hydrothermal vents productivity correlates, at least partially, with environmental stress. Here, we studied the colonization of rock substrata deployed along a deep-sea hydrothermal vent gradient at four sites with and without direct influence of vent fluids at 1700 m depth in the Lucky Strike vent field (Mid-Atlantic Ridge, MAR). We examined in detail the composition of faunal communities (>20 m) established after two years and evaluated species and functional patterns. We expected the stressful hydrothermal activity to (1) limit functional diversity and (2) filter for traits clustering functionally similar species. However, our observations did not support our hypotheses. On the contrary, our results show that hydrothermal activity enhanced functional diversity. Moreover, despite high species diversity, environmental conditions at surrounding sites appear to filter for specific traits, thereby reducing functional richness. In fact, diversity in ecological functions may relax the effect of competition allowing several species to coexist in high densities in the reduced space of the highly-productive vent habitats under direct fluid emissions. We suggest that the high productivity at fluid-influenced sites supports higher functional diversity and traits that are more energetically expensive. The presence of exclusive species and functional entities led to a high turnover between surrounding sites. As a result, some of these sites contributed more than expected to the total species and functional β-diversities. The observed faunal overlap and energy links (exported productivity) suggest that rather than operating as separate entities, habitats with and without influence of hydrothermal fluids may be considered as interconnected entities. Low functional richness and environmental filtering suggests that surrounding areas, with their very heterogeneous species and functional assemblages, may be especially vulnerable to environmental changes related to natural and anthropogenic impacts, including deep-sea mining.</p>
Related data to article "Environmental Drivers of Gross Primary Productivity and Light Use Efficiency of a Temperate Spruce Forest"
<p>Data related to the article "Environmental Drivers of Gross Primary Productivity and Light Use Efficiency of a Temperate Spruce Forest", currently (2022-12-05) under review for publication in JGR:Biogeosciences.</p>
Supporting information from Prospective environmental risk screening of seven advanced materials based on production volumes and aquatic ecotoxicity
<p>Supporting information (Opne data) for the publication Prospective environmental risk screening of seven advanced materials based on production volumes and aquatic ecotoxicity</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.