Find research datasets worth reusing

opencc-by-4.0Nov 2023View details →

Supplementary data for the article: Future environmental impacts of metals: a systematic review of impact trends, modelling approaches, and challenges

This repository provides the supplementary data to the paper titled <a href="https://doi.org/10.1016/j.resconrec.2024.107572" target="_blank" rel="noopener">"Future environmental impacts of metals: a systematic review of impact trends, modelling approaches, and challenges"</a>, published 2024 in Resources, Conservation and Recycling. <h4>Contents</h4> The repository is split in 3 parts and comprises the following files (more details are provided in the README.md): A_Database of reviewed studies: <ul> <li>contains the detailed review data, meant for readers to use as an overview file to gather studies relevant to them. It also includes an overview of all data sources that the reviewed studies used.</li> </ul> B_Scientific supplement to paper: <ul> <li>Contains all data relevant to the related publication Harpprecht et al. (2024), such as studies screened , FAIR data analysis, or analyzed impact trends.</li> </ul> C_Data for figures in paper: <ul> <li>This file contains all the data for Figures 3, 4 and 5 in tabular form, representing impact trends, scenario variables, scenario modelling approaches and data sources used.</li> </ul> <h4>Summary</h4> These files allow to reproduce the results of our study. In this work, we systematically reviewed studies which assessed future environmental impacts of metal supply chains. Our review yielded 40 publications covering 15 metals: copper, iron, aluminium, nickel, zinc, lead, cobalt, lithium, gold, manganese, neodymium, dysprosium, praseodymium, terbium, and titanium. We evaluated their results regarding future impact trends, and their methods, i.e., modelling approaches, scenario variables, and data sources of scenario variables. We identified 15 scenario variables. The most common variables are background electricity mix, ore grade, recycling shares, demand, and energy efficiency. We identified 229 unique data sources for the reviewed scenario variables. <h4>Related publication</h4> More details on the data and its interpretation as well as the scientific context are provided in the publication itself: <a href="https://doi.org/10.1016/j.resconrec.2024.107572" target="_blank" rel="noopener">Harpprecht, C., Miranda Xicotencatl, B., van Nielen, S., van der Meide, M., Li, C. , Li, Z., Tukker, A., Steubing, B. (2024). Future environmental impacts of metals: a systematic review of impact trends, modelling approaches, and challenges. Resources, Conservation and Recycling.</a> <h4>Funding </h4> Carina Harpprecht received funding from the Energy Program of the German Aerospace Center in 2022. Zhijie Li received funding from the European Institute of Innovation and Technology (EIT) under the project Valomag (Project No. 14049). <h4>License</h4> CC-BY 4.0 license for DLR (German Aerospace Center)

opencc-by-4.0Jan 2024View details →

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

This repository provides the unregionalized life cycle inventories to the paper "Ginster, R., Blömeke, S., Popien, J. L., Scheller, C., Cerdas, F., Herrmann, C., & Spengler, T. S. (2024). Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts. Journal of Industrial Ecology, 1–18. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a>". <h2>Contents</h2> The repository is split into 2 parts and comprises the following files: 01_production: contains the necessary life cycle inventories for battery production. <ul> <li>01_primary: contains the life cycle inventories for battery production from primary materials.</li> <li>02_secondary: contains the life cycle inventories for battery production from secondary materials.</li> <li>03_active_material: contains the life cycle inventories for the active battery materials from primary materials.</li> <li>04_active_material: contains the life cycle inventories for the active battery materials from secondary materials.</li> </ul>   02_recycling: contains the necessary inventories for battery recycling. <ul> <li>01_process: contains the life cycle inventories for battery recycling.</li> <li>02_intermediate: contains the life cycle inventories for the intermediate system for battery recycling.</li> <li>03_output: contains the life cycle inventories for the resulting substances from battery recycling.</li> </ul> <h2>Summary</h2> 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). <h2>Related publication</h2> More details on the scientific context is provided in the publication itself: Ginster, R., Blömeke, S., Popien, J. L., Scheller, C., Cerdas, F., Herrmann, C., & Spengler, T. S. (2024). Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts. Journal of Industrial Ecology, 1–18. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a> <h2>Funding</h2> 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). 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.

opencc-by-4.0Jul 2024View details →

Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions

Files generated from the study described in <a href="https://doi.org/10.1101/2024.02.08.579534">Fernandes et. al (2024)</a> . The file "cvs_h2s.csv" comprises the coefficient of variation and the Cullis heritability for each environment. The file "all_predictions.csv" contains the predictions from all the models evaluated, in different cross-validation (CV) scenarios. The file "coincidence_index.csv" has the Coincidence Index (CI) for each CV and models evaluated in our study. Our study used the multi-environment maize yield trials data from the Genomes to Fields 2022 initiative (<a href="https://doi.org/10.1186/s13104-023-06421-z">Lima et. al 2024</a>).

opencc-by-4.0Nov 2023View details →

Process modeling, environmental and economic sustainability of the valorization of whey and eucalyptus residues for resveratrol biosynthesis

Tables included in the article "Process modeling, environmental and economic sustainability of the valorization of whey and eucalyptus residues for resveratrol biosynthesis"

opencc-by-4.0Nov 2022View details →

Data for Marine Ecological Niche Models, for 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species

Data for Ecological Niche Models: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species.

opencc-by-4.0Jan 2021View details →

Modeling the metabolic profile of Mytilus edulis reveals molecular signatures linked to gonadal development, sex and environmental site

Metabolomics dataset used in the publication "Modeling the metabolic profile of Mytilus edulis reveals molecular signatures linked to gonadal development, sex and environmental site" Jaanika Kronberg, Jonathan J. Byrne, Jeroen Jansen, Philipp Antczak, Adam Hines, John Bignell, Ioanna Katsiadaki, Mark R. Viant and Francesco Falciani  Metabolomics dataset for metabolic bins 1 to 1045 for 376 mussels as used in the publication. Mussel metadata are described in a separate file (spectrum number, sample label, sex, site, species, month, temperature of water, salinity of water, ADG rate, gonadal stage, parasite load) Species 1: Mytilus edulis, species 2: hybrid, species 3: Mytilus galloprovincialis

opencc-by-4.0Jan 2022View details →

Global Environmental and Weather data for PyPSA-Earth: An Open Optimisation Model of the Earth Energy System.

PyPSA-Earth is an open model dataset of the global power system at different network levels that cover our Earth. The African model can be built using the code provided at <a href="https://github.com/pypsa-meets-africa/pypsa-africa">https://github.com/pypsa-meets-africa/pypsa-africa</a>. Other regions follow soon under the same code base. Since the GitHub codebase is not suited for handling large changing files, we provide here separate data bundles and cutouts to be downloaded and extracted as noted in the <a href="https://pypsa-meets-africa.readthedocs.io/en/latest/index.html">documentation</a> The below-provided cutouts are spatiotemporal subsets of the Earth weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset and the <a href="https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002">CMSAF SARAH-2</a> solar surface radiation dataset for the year 2013. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>). They can be reproduced or extended for other weather years (approx. 40-50 years) around the world by using the <a href="https://github.com/pypsa-meets-africa/pypsa-africa/blob/main/scripts/build_cutout.py">build.cutout.py</a> ECMWF ERA5 <ul> <li>Source: <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li>Terms of Use: <a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul>

opencc-by-4.0Aug 2017View details →

Multiscale continuum figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling"

Accessible versions of selected figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling" Environ. Sci. Processes Impacts 19(3): 188-202. DOI: 10.1039/C7EM00053G. The Abstract Art figure shows a classification of variables for predictive/diagnostic models used in silico environmental chemical science, in terms of system scales and variable types. Figure 3 shows a continuum of system scales encompassing the whole scope of predictive/diagnostic modelling for in silico environmental chemical sciences, juxtaposing earth and biological scales. The published version of Figure 3 is tall, for two-column page-layouts, but a wide version of Figure 3 is provided for landscape oriented formats. The 300 dpi versions of each figure should be adequate resolution for most purposes, and therefore are recommended. The large versions of the figures may take significant time to download, but may be useful for high resolution applications. This work is from the perspectives/review paper at the beginning of a themed issue on "Quantitative Structure-Activity Relationships (QSARs) and Computational Chemistry Methods in the Environmental Chemical Sciences", published in the March 2017 issue of the Royal Society of Chemistry journal Environmental Sciences: Process and Impacts. The whole collection of papers can be accessed at rsc.li/qsars.

opencc-by-4.0Jun 2023View details →

From Ridge 2 Reef: An Interdisciplinary Model for Training the Next Generation of Environmental Problem Solvers

This dataset contains the raw data from the evaluation instruments and accompanies the manuscript: "From Ridge 2 Reef: An Interdisciplinary Model for Training the Next Generation of Environmental Problem Solvers". It contains all trainee and advisor interviews from 2018 - 2022, as well as a select few partner interviews. It also contains pre and post-annual trainee survey data and the codebook to decipher the survey data. Rubric criteria and scores are included for trainees enrolled in the R2R Communication Skills course. The R script contains the statistical analyses reported in the manuscript and code used to generate figures.

opencc-by-4.0Sep 2023View details →

Results of the expert opinion survey on environmental modeling with InVEST, Mapbiomas, and Open Street Maps

This is the repository for the results of the 'expert opinion survey on environmental modeling with InVEST, Mapbiomas, and Open Street Maps'. Note: check the most recent version in the sidebar <table> <tbody> <tr> <td>Current version</td> <td>v.0.2</td> </tr> <tr> <td>Date</td> <td>2024/01/10</td> </tr> <tr> <td>Respondants</td> <td>30</td> </tr> </tbody> </table> Available files: <table> <tbody> <tr> <td>File</td> <td>Type</td> <td>Description</td> </tr> <tr> <td><a href="../api/files/a241155a-1fb9-4b1d-b2b4-3e5cca19ff4a/responses_v01_public.csv">responses_v01_public.csv</a></td> <td>CSV table</td> <td>Survey raw results (anonymous)</td> </tr> <tr> <td><a href="../api/files/a241155a-1fb9-4b1d-b2b4-3e5cca19ff4a/responses_v01_stats.csv">responses_v01_stats.csv</a></td> <td>CSV table</td> <td>Questions statistics</td> </tr> <tr> <td><a href="../api/files/a241155a-1fb9-4b1d-b2b4-3e5cca19ff4a/responses_v01_mean_sd.jpg">responses_v01_mean_sd.jpg</a></td> <td>JPEG Image</td> <td>Illustration of Stats (mean and standard deviation)</td> </tr> <tr> <td><a href="../api/files/a241155a-1fb9-4b1d-b2b4-3e5cca19ff4a/responses_v01_bands.jpg">responses_v01_bands.jpg</a></td> <td>JPEG Image</td> <td>Illustration of Stats (uncertainty bands)</td> </tr> </tbody> </table> The column descriptions in the statistical table are as follows: Prefixes: <ul> <li>HABITAT: habitat suitability score</li> <li>WEIGHT: Threat weight</li> <li>MAX_DIST: Maximum distance of negative influence (impact)</li> </ul> Suffixes: <ul> <li>mean: Average</li> <li>std: Standard deviation</li> <li>min: Minimum value</li> <li>p05: 5th percentile</li> <li>p25: 25th percentile</li> <li>p50: 50th percentile (median)</li> <li>p75: 75th percentile</li> <li>p95: 95th percentile</li> <li>max: Maximum value</li> </ul> These prefixes and suffixes describe various statistical measures used to analyze the environmental modeling data.

opencc-zeroJul 2020View details →

Accounting for environmental variation in co‐occurrence modelling reveals the importance of positive interactions in root‐associated fungal communities

Understanding the role of interspecific interactions in shaping ecological communities is one of the central goals in community ecology. In fungal communities, measuring interspecific interactions directly is challenging because these communities are composed of large numbers of species, many of which are unculturable. An indirect way of assessing the role of interspecific interactions in determining community structure is to identify the species co-occurrences that are not constrained by the environmental conditions. In this study, we investigated co-occurrences among root-associated fungi, asking whether fungi co-occur more or less strongly than expected based on the environmental conditions and the host plant species examined. For this purpose, we generated molecular data on root-associated fungi of five plant species evenly sampled along an elevational gradient at a high Arctic site. We analysed the data using a joint species distribution modelling approach that allowed us to identify those co-occurrences that could be explained by the environmental conditions and the host plant species, as well as those co-occurrences that remained unexplained and thus more likely reflect interactive associations. Our results indicate that positive interactions play an important role in shaping microbial communities in arctic plant roots. In particular, we found that mycorrhizal fungi are especially prone to positively co-occur with other fungal species. Our results bring new understanding to the structure of arctic interaction networks by suggesting that interactions among root-associated fungi are predominantly positive.

opencc-by-sa-4.0Jun 2023View details →

XIS: A daily spatiotemporal machine-learning model for environmental exposures in the contiguous United States

These Parquet files contain the outputs used for many analyses and plots in the linked papers. For temperature and humidity, the full sets of observations for cross-validation aren't included because we used restricted-use MADIS data.

opencc-zeroDec 2021View details →

Data for Nicola Chinook Ricker stock-recruit model with environmental covariates

<ol> <li>Climate change and human activities are transforming river flows globally, with potentially large consequences for freshwater life. To help inform watershed and flow management, there is a need for empirical studies linking flows and fish productivity.</li> <li>We tested the effects of river conditions and other factors on 22 years of Chinook salmon productivity in a watershed in British Columbia, Canada.</li> <li>Freshwater conditions during adult salmon migration and spawning, as well as during juvenile rearing, explained a large amount of variation in productivity.</li> <li>August river flows while salmon fry reared had the strongest effect on productivity – our model predicted that cohorts that experience 50% below average flow in the August of rearing have 21% lower productivity.</li> <li>These contemporary relationships are set within long-term changes in climate, land use, and hydrology. Over the last century, average August river discharge decreased by 26%, air temperatures warmed, and water withdrawals increased. 17% of the watershed was logged in the last 20 years. </li> <li>Our results suggest that, in order to remain stable, this Chinook salmon population being assessed for legal protection requires substantially higher August flow than previously recommended. Changing flow regimes – driven by watershed impacts and climate change – can threaten imperiled fish populations.</li> </ol>

opencc-by-4.0Apr 2022View details →

Environmental data at the sampling event level collected with Inline instruments, almanach, models and satellites during the Tara Pacific Expedition 2016-2018

The Tara Pacific expedition (2016-2018) sampled coral ecosystems around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples. The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis. Here we provide at the sampling event level, the environmental data originating from all instruments acquiring continuously during the full course of the campaign. This dataset is augmented with the addition of variables originating from almanach (local sun/moon set/rise, local zenith), from operational models obtained from Copernicus Marine Services, but also from satellite imagery (MODIS-AQUA satellite - Level 3 mapped product, 8 day average, 4km resolution) at <a href="https://oceandata.sci.gsfc.nasa.gov">https://oceandata.sci.gsfc.nasa.gov</a>. The zone corresponding to the station position and date was recovered either by taking a two pixel buffer around the given location (total zone being a 5 by 5 pixels square of 20 km side) and in order to propose an alternative measure in the inevitable case where clouds were present an alternative 12 pixels buffer was taken (total zone being a 25 by 25 pixels square of 100 km side). All data were provided as mean, standard deviation (sd) together with 0.05, 0.25, 0.5, 0.75 and 0.95 quartiles

opencc-zeroJul 2022View details →

Data for: Water system simulation modeling with hydropower optimization and environmental flows: An example with Pywr

This dataset was used in the CenSierraPywr model created for the project "Optimizing Hydropower Operations While Sustaining Ecosystem Functions in a Changing Climate", for the California Energy Commission. Specifically, this data is to support reproducibility of the article describing the basic methods (Rheinheimer et al., in review). The model was built in Pywr, an open-source, linear programming-based Python package for modeling basin-scale water systems in the Central Sierra Nevada, California. Here, we focus on the Stanislaus and Upper San Joaquin River basins as they have high elevation hydropower typically operated to maximize revenue. CenSierraPywr consists of daily water allocations that include both hydroeconomic drivers for hydropower and more advanced environmental flows. Piecewise linear electricity prices from simulated hourly price data are used to drive discretionary hydropower, while environmental flows include the addition of ramping rates. Hydrological inputs include runoff data at the sub-basin level, based on the historical (1950 to 2011) daily gridded (1/16 degree) runoff data generated by the Variable Infiltration Capacity (VIC) hydrologic model developed by Livneh et al. (2013), forced with observed meteorological data and bias-corrected using local gauge data. All data inputs for reproducibility of CenSierraPywr for the Stanislaus and Upper San Joaquin Rivers are included, including original and preprocessed electricity and hydrological data and management-related data specific to certain hydropower projects or facilities.

opencc-by-4.0Sep 2022View details →

Deep Learning based Urban Morphology for City-scale Environmental Modeling

The WRF simulations were performed using the Weather Research and Forecasting (WRF) model, version 4.2.1. The three nested domains are centered over Chicago, USA, with a spatial resolution of 9, 3, and 1 km for the outermost, middle, and innermost domains. The model was implemented with 42 pressure levels, with the first model level located at 21.2 m and the first 1 km vertical height containing 11 model levels. The initial and boundary conditions are taken from the National Centers for Environmental Prediction (NCEP) Final Reanalysis dataset at 1 degree spatial and 6-hourly temporal resolution.The physics components include the WRF single moment 6 class for microphysics, Dudhia for shortwave, the Rapid Radiative Transfer Model for longwave radiation parameterizations, Bougeault for the planetary boundary layer, Noah for the land surface model, Building Environment Parametrization (BEP)  for the urban model, and Grell for the cumulus scheme (only for the outermost domain of 9 km spatial resolution). The LCZs of Chicago, USA, are generated using the crowd-sourcing method. The training dataset, created manually, is obtained from the WUDAPT portal, and random forest classification is applied to Landsat 8 imagery to derive the LCZs for the desired region. The simulations are performed from 1/Jul/2018 00:00 to 7/Jul/2018 06:00, where the first 6 hours are discarded as spin-up time.The Digital Synthetic City (DSC) of Chicago, USA, uses satellite imagery and global-scale population and elevation data as input to the automatic method for producing a statistically similar and synthetic city-scale 3D urban model as output.The Control simulations use National Land Cover Database land use/land cover with  NUDAPT parameters, the three default WRF urban classes, and corresponding UCPs; the WUDAPT uses the MODIS classes with additional urban LCZs and UCPs from Brousse et al. (2016), and the DSC uses the WUDAPT classes with UCPs generated from DSC method.The dataset contains:1. Output from DSC in Shapefile.2. WRF model output for the third domain (1 km) spatial resolution domain for (a) NUDAPT or Control (b) WUDAPT or LCZs (c) DSC

opencc-zeroJun 2024View details →

An environmental resistance model to inform the biogeography of aquatic invasions in complex stream networks

Freshwater invasions are a global conservation issue. Emerging tools for biogeographical analyses can provide critical information for their effective management and monitoring. Here, we propose a method to assess the distribution of environmental resistance of stream ecosystems to biological invasions by coupling multi‐stage habitat potential models for non‐native species. Location: Andean Patagonia (Chile and Argentina).Taxa: North American beaver (Castor canadensis), Chinook salmon (Oncorhynchus tshawytscha), and coho salmon (O. kisutch). Methods: Environmental resistance to invasive species was mapped throughout a large region of Patagonia by stacking multi‐stage habitat relationships for each target species and assessing the complementation between critical habitats at multiple scales. We generated an environmental model of stream networks derived from high‐resolution topographic and climatic data representing 15,406 drainage basins (>1 km2) covering an area of 369,791 km2. We quantified the intrinsic potential of stream reaches (100 m and 1000 m) to sustain high‐quality habitats and assessed habitat complementation (i.e., abundance and proximity) at the sub‐basin scale as a proxy for environmental resistance. Results: Our model revealed high heterogeneity in the distribution of environmental resistance to invasions throughout the study region, providing case‐specific insights for the research and management of invaders. Conclusions: Environmental resistance modelling is a novel method to study the biogeography of riverine invasions. Our approach is compatible with additional sources of information about species and the environment and shows versatility to diverse invasion scenarios and data sources. This method can be useful in prioritising research and management of incipient and spreading invasions, especially for large and data‐poor regions.

opencc-by-4.0Dec 2016View details →

Fig. 2 in Macro-Scale Environmental Preferences Of Bombina Bombina: A Modeling Approach

Fig. 2. The receiver operating characteristic (ROC) curve generated in Maxent, showing an average of 50 repetitions of the model; the dark blue range shows the mean of the standard deviations.

opencc-by-4.0Dec 2016View details →

Fig. 3 in Macro-Scale Environmental Preferences Of Bombina Bombina: A Modeling Approach

Fig. 3. Results of jackknife test of variable importance, using training gain. The jackknife test in blue bars shows individual environmental variable importance relative to the red bar which shows all environmental variables. Light blue bar shows whether a variable has any information that isn't present in the other variables, and a dark blue bar shows whether a variable has any useful information by itself. Values shown are averages over replicate runs.