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Techno-economic sustainability analysis methodology for conversion routes of renewable feedstock resources to bio-based products – case studies
<p>The dataset provides a set of sustainability principles, criteria and indicators for the evaluation of the conversion routes stage of a bio-based product. The selected case studies on the employment of alternative feedstocks and production of the bio-based products are implemented in order to evaluate the proposed methodology. Mass and energy balances for all case studies, estimated techno-economic metrics, cost of externalities and risk assessment results are provided</p>
Case study result data set for Energy Economics article "Demystifying market clearing and price setting effects in low-carbon energy systems"
<p>The data set contains country-specific power generation and consumption time series data for the European energy system, including both traditional and new market participants due to cross-sectoral integration.</p> <p>Country codes: ALPHA-3<br> Unit: Megawatt (electric) (interval average values, i.e. MWh/h)</p> <p><strong>Generation technology types</strong></p> <ul> <li>batteryStorage (Li-Ion)</li> <li>conventionalHydro (aggregated for different equivalent hydropower systems)</li> <li>natural_gas_CC_COND (Combined Cycle Gas Turbine)</li> <li>natural_gas_CC_EXCOND (Combined Cycle Gas Turbine as extraction condensing CHP plant for district heating)</li> <li>natural_gas_GT_COND (Open-Cycle Gas Turbine)</li> <li>natural_gas_GT_EXCOND (Open-Cycle Gas Turbine as extraction condensing CHP plant for industry)</li> <li>offshoreWind (aggregated for different LCOE and IEC wind turbine classes)</li> <li>offshoreWindExplicit (offshore wind generation considered for offshore grid investments in the North Seas area, aggregated for different LCOE classes)</li> <li>onshoreWind (solar PV, aggregated for different LCOE classes)</li> <li>other (geothermal, waste)</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> <li>solar (solar PV, aggregated for different LCOE classes)</li> <li>uran_ST_COND (steam turbine condensing power plant)</li> </ul> <p><strong>Consumption technology types</strong></p> <ul> <li>BEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>PHEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>airConditioning</li> <li>batteryStorage (Li-Ion)</li> <li>conventionalLoad</li> <li>heatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridHeatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridTruck (Hybrid Overhead-Line truck)</li> <li>largeScaleDirectResistiveHeating (Centralised CHP systems)</li> <li>natural_gas_CC_EXCOND_electrodeHeater</li> <li>natural_gas_CC_EXCOND_heatpumpHeater</li> <li>natural_gas_GT_EXCOND_electrodeHeater</li> <li>natural_gas_GT_EXCOND_heatpumpHeater</li> <li>powerToGas</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> </ul>
Organisation for Economic Co-operation and Development (OECD) data for Antalya (Turkey), Antwerp (Belgium), Cork (Ireland), Thessaloniki (Greece) (source: OECD)
<p>The data have been collected via the official OECD Application Programming Interface (API)<strong> </strong>and<strong> </strong>includes the following indicators:</p> <ul> <li>EmpPlaRes - Employment at place of residence</li> <li>LfPartRa - Labour Force and Participation rate</li> <li>UnemReg - Unemployment in regions </li> <li>RegGdpTL2 - Regional Gross Domestic Product (Large regions TL2)</li> <li>GDPLT3 - Gross Domestic Product (Small regions TL3)</li> <li>RegEmIndu - Regional Employment by industry (ISIC rev 4)</li> <li>RegGVAWorker - Regional GVA per worker</li> <li>RegIncPC - Regional income per capita</li> </ul> <p>Source: https://data.oecd.org/api/</p>
Data from: Behavioural responses to potential dispersal cues in two economically important cereal-feeding eriophyoid mite species
<p>Variables:</p> <ol> <li>species (ABH = <em>Abacarus hystrix</em>, WCM = <em>Aceria tosichella</em> MT1 genetic lineage)</li> <li>variant - experimental treatment (type of dispersal cue): wind, an insect vector, presence of a fresh plant</li> <li>feeding - no. of feeding specimens</li> <li>walking - no. of walking specimens</li> <li>standing - no. of specimens standing vertically</li> <li>cha - no. of specimens forming chains</li> <li>mob - no. of specimens capable to move</li> <li>pop - no. of all specimens (including quiescent stages)</li> </ol>
The ecological and economic benefits of sustainable agricultural practices
<p><strong>Code and data for Mata et al.'s <em>The ecological and economic benefits of sustainable agricultural practices: Evidence from on-farm trials in broad-acre crops. </em></strong></p> <p><strong>Abstract</strong></p> <p>1. A transition to more sustainable agricultural practices is essential to mitigate the negative environmental impacts of conventional farming and to ensure long-term food security. However, widespread adoption requires robust evidence demonstrating their efficacy and economic viability.</p> <p>2. We co-designed a two-year field trial with farmers and agronomy advisors in Australia to evaluate the ecological and economic outcomes of sustainable agricultural practices for managing the redlegged earth mite, a major pest of Australian crops and pastures. We compared 'Novel' treatments – representing long-term farmer-implemented sustainable practices based on biological control – with 'Conventional' treatments, and 'Plus' treatments designed as counterfactuals to disentangle the effects of specific pest control and plant nutrient components.</p> <p>3. Redlegged earth mite densities remained below economic thresholds across all treatments and years, demonstrating effective pest control in both conventional and sustainable systems. Notably, the Novel treatment supported higher densities of beneficial arthropods, suggesting increased biological control potential.</p> <p>4. Yield and gross profit margins were generally similar across treatments, indicating that sustainable agriculture practices can maintain profitability while fostering biodiversity.</p> <p>Practical implication. Our study provides evidence that biological control and biofertiliser supplementation can be effectively used to manage agricultural pests. It also demonstrates the value of close collaboration with farmers and agronomy advisors in conducting ecological field research with real-world applications.</p>
Selkie GIS Techno-Economic Tool input datasets
<p>This data was prepared as input for the Selkie GIS-TE tool. This GIS tool aids site selection, logistics optimization and financial analysis of wave or tidal farms in the</p><p>Irish and Welsh maritime areas. Read more here:</p><p>https://www.selkie-project.eu/selkie-tools-gis-technoeconomic-model/</p><p> </p><blockquote><p>This research was funded by the Science Foundation Ireland (SFI) through MaREI, the SFI Research Centre for Energy, Climate and the Marine and by the Sustainable Energy Authority of Ireland (SEAI). Support was also received from the European Union's European Regional Development Fund through the Ireland Wales Cooperation Programme as part of the Selkie project.</p></blockquote><p> </p><p>********************</p><p><strong>File Formats</strong></p><p>********************</p><p>Results are presented in three file formats:</p><p> </p><p><strong>tif</strong> Can be imported into a GIS software (such as ARC GIS)</p><p><strong>csv</strong> Human-readable text format, which can also be opened in Excel</p><p><strong>png</strong> Image files that can be viewed in standard desktop software and give a spatial view of results</p><p> </p><p> </p><p>******************</p><p><strong>Input Data</strong></p><p>******************</p><p>All calculations use open-source data from the Copernicus store and the open-source software Python. The Python xarray library is used to read the data.</p><p> </p><p>Hourly Data from 2000 to 2019</p><p> </p><p><i>- Wind -</i></p><p>Copernicus ERA5 dataset</p><p>17 by 27.5 km grid </p><p>10m wind speed</p><p> </p><p><i>- Wave -</i></p><p>Copernicus Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis dataset</p><p>3 by 5 km grid</p><p> </p><p> </p><p>*********************</p><p><strong>Accessibility</strong></p><p>*********************</p><p>The maximum limits for Hs and wind speed are applied when mapping the accessibility of a site. </p><p>The Accessibility layer shows the percentage of time the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5) are below these limits for the month.</p><p> </p><p>Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined by checking if </p><p>the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total number of hours for the month.</p><p> </p><p>Environmental data is from the Copernicus data store (https://cds.climate.copernicus.eu/). Wave hourly data is from the 'Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis' dataset. </p><p>Wind hourly data is from the ERA 5 dataset. </p><p> </p><p> </p><p>********************</p><p><strong>Availability</strong></p><p>********************</p><p>A device's availability to produce electricity depends on the device's reliability and the time to repair any failures. The repair time depends on weather </p><p>windows and other logistical factors (for example, the availability of repair vessels and personnel.). A 2013 study by O'Connor et al. determined the </p><p>relationship between the accessibility and availability of a wave energy device. The resulting graph (see Fig. 1 of their paper) shows the correlation between</p><p>accessibility at Hs of 2m and wind speed of 15.0m/s and availability. This graph is used to calculate the availability layer from the accessibility layer.</p><p> </p><p>The input value, accessibility, measures how accessible a site is for installation or operation and maintenance activities. It is the percentage time the </p><p>environmental conditions, i.e. the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5), are below operational limits. </p><p>Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined </p><p>by checking if the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total </p><p>number of hours for the month. Once the accessibility was known, the percentage availability was calculated using the O'Connor et al. graph of the relationship</p><p>between the two. A mature technology reliability was assumed.</p><p> </p><p> </p><p>**********************</p><p><strong>Weather Window</strong></p><p>**********************</p><p>The weather window availability is the percentage of possible x-duration windows where weather conditions (Hs, wind speed) are below maximum limits for the </p><p>given duration for the month.</p><p> </p><p>The resolution of the wave dataset (0.05° × 0.05°) is higher than that of the wind dataset </p><p>(0.25° x 0.25°), so the nearest wind value is used for each wave data point. The weather window layer is at the resolution of the wave layer.</p><p> </p><p>The first step in calculating the weather window for a particular set of inputs (Hs, wind speed and duration) is to calculate the accessibility at each timestep. </p><p>The accessibility is based on a simple boolean evaluation: are the wave and wind conditions within the required limits at the given timestep?</p><p> </p><p>Once the time series of accessibility is calculated, the next step is to look for periods of sustained favourable environmental conditions, i.e. the weather </p><p>windows. Here all possible operating periods with a duration matching the required weather-window value are assessed to see if the weather conditions remain </p><p>suitable for the entire period. The percentage availability of the weather window is calculated based on the percentage of x-duration windows with suitable </p><p>weather conditions for their entire duration.The weather window availability can be considered as the probability of having the required weather window available </p><p>at any given point in the month.</p><p> </p><p>*****************************</p><p><strong>Extreme Wind and Wave</strong></p><p>*****************************</p><p>The Extreme wave layers show the highest significant wave height expected to occur during the given return period.</p><p>The Extreme wind layers show the highest wind speed expected to occur during the given return period. </p><p> </p><p>To predict extreme values, we use Extreme Value Analysis (EVA). EVA focuses on the extreme part of the data and seeks to determine a model to fit this reduced </p><p>portion accurately. EVA consists of three main stages. The first stage is the selection of extreme values from a time series. The next step is to fit a model </p><p>that best approximates the selected extremes by determining the shape parameters for a suitable probability distribution. The model then predicts extreme values </p><p>for the selected return period. All calculations use the python pyextremes library. Two methods are used - Block Maxima and Peaks over threshold.</p><p> </p><p>The Block Maxima methods selects the annual maxima and fits a GEVD probability distribution.</p><p> </p><p>The peaks_over_threshold method has two variable calculation parameters. The first is the percentile above which values must be to be selected as extreme (0.9 or 0.998). The</p><p>second input is the time difference between extreme values for them to be considered independent (3 days). A Generalised Pareto Distribution is fitted to the selected </p><p>extremes and used to calculate the extreme value for the selected return period.</p>
Implications of Socio-Economic Conditions on Common Mental Disorders: A Case-study of Salt Pan Workers in Marakkanam Block of Tamil Nadu
<p>Socio-economic indicators of Saltpan workers in Marakaanam, Tamil Nadu, India and their SRQ-20 scoring.</p><p>Data regarding; Consumption, Wages, Debt, Social Group, Gender, Age, Ration Card, Education, Distance from work(saltpan), ownership of house and the SRQ-20 Questionanaire used for Screening of CMDs and Distress levels </p>
Data of paper "Global supply chains amplify economic costs of future extreme heat risk"
<p>This is the database of articles "Global supply chains amplify economic costs of future extreme heat risk". The database contains the number of deaths caused by future heat waves in regions around the world under different SSP scenarios (e.g. SSP119, SSP245, SSP585), as well as global health losses, labor losses, and indirect losses as a percentage of regional or sectoral value added under different SSP scenarios. The regions of the database are aggregated using the GTAP 141 aggregating schema.</p>
LAMASUS NUTS-level economic data 1980-2021
<p>This dataset comprises spatial and temporal economic data compiled from the Annual Regional Database of the European Commission (ARDECO) and education attainment from Eurostat, covering the period from 1980 to 2021(2024). The dataset consists of three files, each corresponding to a different level of NUTS coding (NUTS 1-3) according to the 2016 NUTS specification.</p> <p>For each file, the following columns are included: <br><br>Identifier:</p> <ol> <li>NUTS Code: The unique identifier for the NUTS (2016) region</li> <li>Year: The year of the data point</li> </ol> <p>Variables:</p> <p> 3. - 8. Hours Worked by NACE sector in 1000 hours (empHour_*)<br> 9. - 15. Employment by NACE sector in 1000 jobs (emp_*)<br> 16. Total employment in 1000 jobs (empl)<br> 17. GDP at constant prices ref. 2015 in mio EUR (gdp)<br> 18. - 23. GVA by NACE sector at constant prices ref. 2015 in mio EUR (gva_*)<br> 24. Total Labour Force in 1000 jobs (labour)<br> 25. Total Population (Regional Accounts) in persons (pop)<br> 26. - 31. Compensation of Employees by NACE sector at constant prices ref. 2015 in mio EUR (wage_*)<br> 32. Share of low education workers in per cent (loweduc) [not available for NUTS3]<br> 33. Share of high education workers in per cent (terteduc) [not available for NUTS3]</p> <p>The temporal dimension is yearly, ranging from 1980 to 2021(2024). The spatial dimension is identified by NUTS codes (2016), with granularity ranging from level 1 to level 3.</p> <div> <p>This dataset has been created as part of LAMASUS Project under the scope of Deliverable 3.2 titled "Database on EU policies and payments for agriculture, forest, and other LUM related drivers ". The data is directly linked to the work described on pages 45-47 belonging to section 3.3 Sectoral Income and Employment. The full text of the deliverable can be accessed via: <a href="https://www.lamasus.eu/wp-content/uploads/LAMASUS_D3.2_policy-and-payment-database.pdf">https://www.lamasus.eu/wp-content/uploads/LAMASUS_D3.2_policy-and-payment-database.pdf.</a></p> <p>Please note that this dataset is intended for research and analysis in the fields of climatology, environmental science, and related disciplines. Users are encouraged to cite this dataset appropriately if utilized in academic or scientific publications.</p> </div>
Tool for the environmental and economic impact assessment of industrial recycling routes for lithium-ion traction batteries
<p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).</p> <p> </p> <p>Please send your inquiries regarding the tool to s.bloemeke@tu-braunschweig.de.</p>
Dataset and Input Files for the "Sustainability and Resilience Through Connection: The Economic Metacommunites of the Western USA" Manuscript
<p>Datasets and input files used for the Ecology and Society manusript "Sustainability and Resilience Through Connection: The Economic Metacommunites of the Western USA". </p>
Supplementary Datafile for "A reform of value-added taxes on foods can have health, environmental and economic benefits in Europe"
<p>The dataset contains the results of the study "A reform of value-added taxes on foods can have health, environmental and economic benefits in Europe" by Marco Springmann, Eugenia Dinivitzer, Florian Freund, Jørgen Dejgård Jensen, and Clara G Bouyssou. </p> <p>It contains VAT rates on foods across Europe, as well as the results of reforming VAT rates for foods, including increasing rates for meat and dairy and reducing rates for fruits and vegetables. The set of results include changes in food demand, changes in environmental impacts (greenhouse gas emissions, land use, water use, and eutrophication potential), changes in diet-related mortality, changes in costs (revenues, climate change costs, costs of illness). </p>
Data and code for the publication "The effect of rainfall changes on economic production"
<p>The zipped file of this repository contains code and data to reproduce the results of the publication:</p> <p>Kotz et al, Nature, The effect of rainfall changes on economic production. (2021). <a href="http://doi.org/10.1038/s41586-021-04283-8">https://doi.org/10.1038/s41586-021-04283-8</a></p> <p> </p> <p>Economic data are derived from the DoSE database: <a href="http://doi.org/10.5281/zenodo.4681305">https://doi.org/10.5281/zenodo.4681305</a></p> <p>Climate data are derived from the ERA-5 reanalysis: <a href="https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 ">https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 </a></p> <p> </p> <p>Please see the README document for detailed:</p> <p>- Descriptions of the code and data provided</p> <p>- Lists of the required dependencies</p> <p>- Naming conventions for variables</p> <p> </p>
The Bomber's Baedeker. A Guide to the Economic Importance of German Towns and Cities
<p>The Bomber's Baedeker</p> <p>The two-volume printed work “The Bomber's Baedeker. A Guide to the Economic Importance of German Towns and Cities” was produced during the Second World War by the British Foreign Office and the Ministry of Economic Warfare. It lists towns and cities of the German Reich with more than a thousand inhabitants and information on their war-related infrastructure, industrial and production facilities. Only four verified copies still exist worldwide and none of them have been accessible for scholarly digital use until now. “The Bomber's Baedeker” was re-discovered in 2019 in the library of the Leibniz Institute of European History (IEG), digitised in cooperation with the Mainz University Library and made accessible and processed by the Digital Historical Research | DH Lab and the Darmstadt University of Applied Sciences as part of a cross-institutional cooperation (including courses with students) so that “The Bomber's Baedeker” can now be used, analysed and processed as an open, machine-readable data source in compliance with FAIR principles.</p>
A neuro-metabolic account of why daylong cognitive work alters the control of economic decisions
<p>This is the data to reproduce the main analysis of the article "A neuro-metabolic account of why daylong cognitive work alters the control of economic decisions".</p> <p>Please contact antonius.wiehler@gmail.com if you have any questions.</p>
Techno-economic details of fixed-bottom offshore wind projects deployed in the European markets
<p>Version (with all files) - Updated version (research article is accepted).</p> <p>Publishing Date: July 10, 2022</p> <p>This dataset describes the techno-economic information of fixed-bottom offshore wind projects deployed in the North Sea region (DK, NL, BE, DE, and the UK). </p> <p>Contents: </p> <p>1) Offshore wind farm project prices and technical characteristics (farm size, turbine rated power, water depth, etc.,)</p> <p>2) Offshore wind farm capacity factor and cumulative energy generation</p> <p>3) Monopile weight </p> <p>4) Offshore wind farm installation duration </p> <p>5) UK offshore wind farms' transmission system cost</p> <p> </p>
Data repository for the publication "Economic Interests Cloud Hazard Reductions in the European Regulation of Substances of Very High Concern"
<p>This repository contains the data and scripts associated with the article “Economic Interests Cloud Hazard Reductions in the European Regulation of Substances of Very High Concern“, written by Jessica Coria, Erik Kristiansson and Mikael Gustavsson.</p>
Economic losses from hurricanes cannot be nationally offset under unabated warming - Data Supplement
<p>This data set includes the raw data for the figures of the article "Economic losses from hurricanes cannot be nationally offset under unabated warming".</p>
Trajetorias dataset: environmental, epidemiological, and economic indicators for the Brazilian Amazon
<p>The Trajetorias dataset is a harmonized set of environmental, epidemiological, and poverty indicators for all municipalities of the Brazilian Legal Amazon (BLA). This dataset is the result of a scientific synthesis research initiative conducted by scientists from several natural and social sciences fields, consolidating multidisciplinary indicators into a coherent dataset for integrated and interdisciplinary studies of the Brazilian Amazon. The Trajetorias dataset is organized in dimensions describing: environmental degradation, land use and land cover, human mobility, climate anomalies, the burden of vector-borne diseases, and poverty indices for rural and urban populations for each of the BLA municipalities. Characterizing the environmental, epidemiological, and socioeconomic profile of the municipalities. These indicators were designed to unveil the specificities of the Amazon region, so that the relationships between these dimensions can be explored regarding past and current enacted policies. The Trajetorias dataset relies on four surveys - the two demographic censuses conducted in 2000 and 2010, and the two agrarian censuses conducted in 2006 and 2017, from which were defined fixed timestamps for analysis. The demographic censuses are the source of data for the multidimensional poverty indices. Environmental data come from satellite images collected by several national and international programs, such as the Amazon Deforestation Monitoring Program (PRODES), DEGRAD, and DETER, accounting for changes in landscape that took place between each demographic census and the subsequent agrarian census. Lastly, disease control data was obtained from the National Disease Notification System and summarized for the 5-year period centered in the agrarian censuses.</p>
Dataset: Economical Accommodations for Neurodivergent Students in Software Engineering Education: Experiences from an Intervention in Four Undergraduate Courses
<p>This dataset contains anonymised raw data and examples of accommodations made for neurodiverse students in four undergraduate courses in Computer Science and Software Engineering programmes. The dataset is published as a part of a book chapter in which we report the accommodations.</p> <p>Overall guidelines we followed, including their sources, are contained in <strong>guidelines.md.</strong></p> <p>The raw data for the two surveys is contained in the two Excel files <strong>survey1.xlsx</strong> and <strong>survey2.xlsx</strong>. Free-text answers have been aggregated by neurodiverse and neurotypical students and anonymised, and are available in the files<strong> survey1_freetext_neurodiverse.txt, survey1_freetext_neurotypical.txt, survey2_freetext_neurodiverse.txt, </strong>and<strong> survey2_freetext_neurotypical.txt.</strong></p> <p>The remaining files are examples of the adapted lecture slides and assignment texts. Here, files starting with WEBcourse are from a mandatory undergraduate course on web development, while files starting with SEcourse are from a mandatory undergraduate course giving an overview of Software Engineering.</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.