Find research datasets worth reusing

opencc-by-4.0Sep 2024View details →

Women's Empowerment through Investing in Zanzibari Craftswomen's Eyesight

This non-controlled, mixed-method, before-after, intervention study included 209 craftswomen at baseline and 157 craftswomen 40 years and older at follow-up with correctable presbyopia. The women were corrected with glasses and followed up for six months (April 0222 - October 2022). A questionnaire was administer before and 6 months after they were provided with glasses. <ol> <li>Demographic profile data includes age group, education , craft type, years of experience, marital status, number of children, number of dependents, and possesion of mobile phone.</li> <li>Eye health data include presenting distance vision at 6/12, right eye and left eye, cause vision worse than 6/12 in either eye and better eye, eye health examination for right eye and left eye and reason if fail examination, glasses prescription distance for right and left eye, corrected vision right eye and left eye, presenting near vision at N8 at 40cm, presenting near vision at N8 at working distance, possession of a pair of glasses, final near presentation and corrected near vision.</li> <li>Outcomes of interests are women's empowerment, subjective wellbeing, earning, saving, and spending, self-reported work performance, quality of life and visual analog scale.</li> </ol>

opencc-by-4.0Feb 2023View details →

INVEST Principles

INVEST stands for Independent, Negotiable, Valuable, Scalable, and Testable. Whatever the negotiation process looks like, a story represents the melting point of an aspect of the system. Such an aspect ideally forms a thin functional slice through of the system. This makes the story self-contained. It provides full value for the end user as, once implemented, the described problem is solved and enfolds immediate value to the user. In addition, self-contained stories are better plannable as they are separated from other stories. Providing stories at this level let you decide on-the-go which story to implement next. This makes your planning quite flexible. It is not needed to follow (probably hidden) dependencies before reordering the stories. User stories should contain only the essence of all discussions and decisions to keep them short and clear. Details have to be avoided in the first place. It is recommended to start with a narrow scope and scale later when it becomes necessary. This helps to stay focused and avoid contradiction. To ensure a user story has been finished it should contain a list of acceptance criteria which the implementation could be (ideally) tested against. Testable criteria are very helpful when creating a solution for a problem. Each will support to understand the problem and could be used to validate an implementation against. In addition, there should also be an agreement on when implementing a story is actually done. The team normally agrees on a definition-of-done checklist which has to be worked through for each user story. In general, the definition-of-done includes an agreed set of tasks which have to be part of the result (e.g., unit-tests, documentation, ...).

opencc-by-4.0May 2023View details →

Figure data and code used in Technical comment on "Fairness considerations in global mitigation investments"

The package contains the data and code to create the figure in the associated technical comment in Science published at <a href="https://www.science.org/doi/10.1126/science.adg5893">https://www.science.org/doi/10.1126/science.adg5893</a>

opencc-by-4.0Aug 2023View details →

The Updated Investment Facilitation Index

The Investment Facilitation Index (IFI) provides information on the current adoption of investment facilitation measures at country level for 142 World Trade Organisation (WTO) Members. It was developed by the German Institute of Development and Sustainability (IDOS), previously known as the Deutsches Institut für Entwicklungspolitik / German Development Institute (DIE), in cooperation with the WTO. The IFI is a composite index measuring the adoption of investment facilitation measures in 2021 and applying a multiple binary scoring scheme. Departing from an earlier version of the index (<a href="https://doi.org/10.23661/dp23.2021">Berger et al., 2021</a>), it has been conceptually revised and extended regarding its country coverage. It now consists of 101 measures composing six regulatory dimensions and corresponds closely to the main policy areas and developments within current policy debates, including the newly negotiated <a href="https://www.wto.org/english/news_e/news23_e/infac_06jul23_e.htm">Investment Facilitation for Development (IFD) Agreement</a> among the WTO Members. The data set provides the foundation for analysing specific facilitation hurdles in investment frameworks of a large number of economies. The fine grained data of the IFI can be used for investigating economic benefits and challenges of investment facilitation reforms, support the assessment of implementation gaps, as well as prioritisation of technical assistance and capacity development. It can also be used by investors seeking information on a country’s investment regime. For a detailed description of the methodology and coding of the IFI, please have a look at the uploaded data documentation, contained in the file ifi_documentation.pdf. It provides information on the conceptual composition of the index, its evolution from the first version, as well as the coding, data generation and validation processes. In the annex, it also features a detailed overview of each measure contained in the index. The file ifi_codebook.csv contains the codebook for the 101 investment facilitation measures included in the IFI. The file features six variables (columns): <ul> <li>Measure: The code of a measure under observation;</li> <li>Area: Specification of the policy area a given measure belongs to;</li> <li>Measure_Description: A short description of what investment facilitation feature is evaluated by a given measure;</li> <li>Weight: Specification of the individual weight of a measure, the product of the allocated score (0, 1 or 2) and this weight denotes the contribution to the total score of a given measure;</li> <li>Unit: The measurement unit for the answer of a given measure, it can take values "Score", meaning that the answer is directly measured by score from the multiple binary scoring scheme, or specify another measurement unit, e.g. number of documents, days, US Dollars, etc.;</li> <li>Coding_0: Specifies the answer coding which allocates a score of 0 to this measure;</li> <li>Coding_1: Specifies the answer coding which allocates a score of 1 to this measure;</li> <li>Coding_2: Specifies the answer coding which allocates a score of 2 to this measure.</li> </ul> The file ifi_table.csv or ifi_table.xlsx (please choose your preferred file format) contains all 14484 data points resulting from the 101 measures coded for 142 economies. Moreover, it also contains the total score for each country calculated by applying the expert weighting scheme. The file contains the following variables (columns): <ul> <li>CountryCode: <a href="https://unstats.un.org/unsd/methodology/m49/">ISO-alpha3</a> code of a country for which a given measure is coded;</li> <li>Country: Name of a country for which a given measure is coded;</li> <li>Measure: Code of a measure that is coded in a given row;</li> <li>Area: Specification of the policy area a given measure belongs to;</li> <li>Measure_Description: A short description of what investment facilitation feature is evaluated by a given measure;</li> <li>Answer: The answer coded for a given measure and country;</li> <li>Score: The allocated score based on the answer, according to the definition of the measure (see codebook);</li> <li>Unit: The measurement unit for the answer of a given measure;</li> <li>Coding: The answer option coded for a given measure and country (corresponds to either Coding_0, Coding_1 or Coding_2 in the codebook);</li> <li>Source: Source statement for the provided answer.</li> </ul> For further inquiries please contact the authors.

opencc-by-4.0Nov 2022View details →

Characterization of investments profiles on the energy transition for european citizens

<ul> <li>Name: Characterization of investments profiles on the energy transition for european citizens</li> <li>Summary: The dataset contains: (1) surveyee consent form for the study, (2) different scenarios about the energy transition, (3) determinant factors about those scenarios, (4) socioeconomic description of the surveyee, (5) investment decisions, (6) and household characterization/description. </li> <li>License: cc-BY-SA</li> <li>Acknowledge: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.</li> <li>Disclaimer: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European commission (Ec). EASME or the Ec are not responsible for any use that may be made of the information contained therein.</li> <li>Collection Date: 22/07/2022</li> <li>Publication Date: 15/10/2023</li> <li>DOI: 10.5281/zenodo.4455198</li> <li>Other repositories:</li> <li>Author: University of Deusto</li> <li>Objective of collection: This data was originally collected to analyze quantitatively the decisions of everyday people in relation to their energy consumption and their reactions to specific political interventions.</li> <li>Description: The dataset contains a CSV file file containing data collected from a survey about energy consumption investments. The fields that can be found for each entry are (1) Different scenarios about the energy transition and reactions to those scenarios, (money spent on energy investments, decisions about scenarios, actions taken under a blackout, etc.) (2) Determinant factors about the chosen scenarios in the previous question, which include different choices that could affect your decision about a scenario (3) socioeconomic information about the user (age, country of residence, studies), (4) estimation of the prices of various technologies related to the energy transition and (5) descriptive statistics about the household living situation (gender of user, people living in household, yearly rent, average savings per month, type of house, size of house) and also includes questions about climate change expertise. Next you can found a description of each field in the dataset <ul> <li>Section 1 - Scenarios for energy transition. <ul> <li>ID90. Rank in order of priority, from top to bottom, in which scenario you will be willing to live or to contribute/invest to make it possible. </li> <li>ID36, ID38, ID43, ID44, ID72. Percentage of money people are willing to spend/save out of their income per scenario</li> <li>ID191, ID192.. Amount of money people would spend based on an assumed case.</li> <li>ID191, ID192. Priority service provision in case of Intermittent energy service. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority for you and 10 stars means it is absolutely necessary for you.</li> <li>[ID325, ID326, ID327, ID328, ID329, ID330, ID331, ID332, ID333, ID334, ID335, ID336, ID337, ID338, ID339, ID340, ID341, ID133, ID242]. Priority service provision in case of Intermittent energy service. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary.</li> <li>[ID251, ID256, ID257, ID292, ID293, ID294, ID295, ID296, ID297, ID298, ID299, ID301, ID302, ID303, ID304, ID305, ID306, ID250, ID251]. Priority service provision in case of full black-outs. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary.</li> <li>[ID141, ID5, ID147]. Used for statements that best represent survey responder</li> </ul> </li> <li>Section 2 - Determinants (factors). Questions used to rate (from 0 to 100) factors that may influence the decision-making process contributing to make an ideal scenario possible. <ul> <li>ID100 Risk profile</li> <li>ID101 Added value</li> <li>ID102 Self-Satisfaction</li> <li>ID103 Technical Fit</li> <li>ID104 Own competence</li> <li>ID105 Knowledge</li> <li>ID106 Cost-Efficiency</li> <li>ID107 Safety</li> <li>ID108 Trust</li> <li>ID109 Autarky</li> <li>ID110 Legal</li> <li>ID111 Climate Protection</li> <li>ID112 Wellbeing</li> <li>ID113 Coziness</li> <li>ID114 Rights and Duties</li> <li>ID115 Peer-Pressure</li> <li>ID116 Socialising</li> <li>ID117 Support</li> <li>ID118 Agreement</li> <li>ID119 Brag</li> <li>ID120 Fun</li> <li>ID121 Novelty</li> <li>ID122 Trends</li> <li>ID123 Authority</li> <li>ID124 Own Significance</li> <li>ID125 Poseur</li> <li>ID2 Frugality</li> <li>ID3 Environmental concerns</li> <li>ID31 Adherence</li> <li>ID52 Commitment</li> <li>ID97 Profits</li> <li>ID99 Credit Score</li> </ul> </li> <li>Section 3 - “Socio-economic” description. Questions about the socio-economic information of the survey respondents for data stratification. The indentation represents the dependency of questions and whether this data was asked <ul> <li>ID164 Understanding of questions</li> <li>ID300 Country of residence</li> <li>ID137 Age</li> <li>ID178 Highest level of education</li> <li>ID136 Willingness to provide data on the investment decision (respond apply for -Investment decision section)</li> </ul> </li> <li>Section 4 - Investment decision. Questions about specific prices of potential purchases-decisions related to four scenarios (respondent's lifestyle) <ul> <li>Appliances <ul> <li>ID42 Affordable cost of a Regular refrigerator</li> <li>ID45 Energy efficient refrigerator costs</li> <li>ID50 Willingness to purchase an energy efficient refrigerator <ul> <li>ID65 Why no</li> <li>ID66 affordable cost of an energy efficient option</li> <li>ID67 Years to amortize an efficient option</li> </ul> </li> </ul> </li> <li>Insulation <ul> <li>ID47 Affordable cost of updating to a state of the art insulation on the facade</li> <li>ID56 Willingness for paying/invest <ul> <li>ID74 Why no?</li> <li>ID20 affordable cost of an energy efficient option</li> <li>ID34 Years to amortize an energy efficient option</li> </ul> </li> </ul> </li> <li>Energy Generation <ul> <li>ID68 Affordable cost of a solar photovoltaic system</li> <li>ID76 Willingness for paying/invest <ul> <li>ID84 Why no?</li> <li>ID132 Affordable cost of a photovoltaic system</li> <li>ID138 Years that amortize a  photovoltaic system</li> </ul> </li> </ul> </li> <li>Energy Storage <ul> <li>ID142 Affordable cost of an energy storage system</li> <li>ID146 Willingness for paying/invest <ul> <li>ID181 Why no? </li> <li>ID182 Affordable cost of an energy storage system </li> <li>ID183 Years that amortize an energy storage systems</li> </ul> </li> </ul> </li> <li>Heating <ul> <li>ID140 Affordable cost of a gas boiler</li> <li>ID209 Affordable cost of an energy efficient heating system</li> <li>ID217 Willingness for paying/invest <ul> <li>ID238 Why no?</li> <li>ID239 Affordable cost of a energy efficient option</li> <li>ID241 Years that amortize a heat pumps</li> </ul> </li> </ul> </li> <li>Mobility <ul> <li>ID41 Average kilometers traveled a typical day</li> <li>ID51 Usual travel option</li> <li>ID264 Affordable cost of a diesel or gasoline mid-range brand new car</li> <li>ID265 Affordable cost of a mid-range brand new electric car</li> <li>ID281 Willingness to buy an electric car <ul> <li>ID289 Why no?</li> <li>ID290 Affordable price of an electric car</li> <li>ID291 Years that amortize an electric car</li> </ul> </li> </ul> </li> </ul> </li> <li>Section 5 - Household characterization <ul> <li>ID127 Selecting an asked value</li> <li>ID189 Type of living area</li> <li>ID202 Gender identity</li> <li>ID1 Those living in the house</li> <li>ID32 Number of inhabitants</li> <li>ID220 Average neat yearly income</li> <li>ID229 Average monthly saving</li> <li>ID240 Type of housing</li> <li>ID249 Owner / co-owner</li> <li>ID255 Usable area of the property (m²)</li> <li>ID263 Insulation level</li> <li>ID270 Climate zone</li> <li>ID86 Level of self-awareness about climate change. On scale of 0-10, where 0 is “climate change does not exist” and 10 is “I am a climate change expert/activist”</li> <li>ID87 Level of awareness of climate change among your peers or relatives, On a scale of 0-10, where 0 is “climate change does not exist” and 10 is “They are climate change experts/activists”</li> <li>ID88 Level of self-awareness about  energy transition. On a scale of 0-10, where 0 is “It is the first time I hear about it” and 10 is “I am an expert or activist”</li> <li>ID89 Level of awareness of energy transition among your peers or relatives On a scale of 0-10, where 0 is “It is the first time they hear about it” and 10 is “They are experts or activists”</li> <li>ID190 feedback about survey</li> </ul> </li> </ul> </li> <li>5 star: ⭐⭐⭐</li> <li>Preprocessing steps: anonymization, data fusion, imputation of gaps.</li> <li>Reuse: NA</li> <li>Update policy: No more updates are planned</li> <li>Ethics and legal aspects: Spanish electric cooperative data contains the CUPS (Meter Point Administration Number), which is personal data. A pre-processing step has been carried out to substitute the CUPS by a random value hash.</li> <li>Technical aspects: </li> <li>Other:</li> </ul>

opencc-by-4.0Mar 2024View details →

Estimated life-cycle-based environmental indicators and social indicators for companies and investment funds

The data files represent the 26 estimated life-cycle-based indicators for a sample of companies and funds, obtained using the methodology described in the linked journal article. The files SD1 and SD2 contain the individual values estimated for the fund and company samples. These estimates are based on the methodology described in the linked article. The data herein is the source for producing all figures of the paper. All companies and funds have been anonymized, as the data is sourced from proprietary databases. At the same link, supplementary file SD3 contains the summary statistics and comparison of sustainable funds versus conventional funds sample. The file SD4 contains the data used to create Figure 4. The file SD5 contains sample data to create Figure 5. The file SD6 contains sample data to create Figure 6. Additional more detailed data can be provided upon reasonable request, but cannot be publicly disclosed as it contains data from licenced databases.

opencc-by-4.0Jan 2024View details →

Video 3 - Open Science: a better return on investment.

An interview with Roberto Sabatino, Research Engagement Officer at HEAnet, Dublin Ireland; Nadia Tonello, Data Management Manager at the Barcelona Supercomputing Centre; and Eva Mendes, PhD in Library and Information Science on the potential of Open Science to enhance humanity’s ability to respond to crises and to provide a better return on investment. Science is increasingly collaborative, and this includes sharing data. Funding needs to include data management, sharing, and infrastructure. Increased interoperability in research can help humanity collaboratively face challenges such as climate change. The response to the Covid-19 pandemic was also facilitated by data sharing, showing the positive societal impact and net benefit of Open Science.

opencc-by-4.0Jan 2022View details →

Profitability and investment risk of Texan power system winterization

Profitability and investment risk of Texan power system winterization This data repository contains interim and final results of the <a href="https://www.nature.com/articles/s41560-022-00994-y">paper </a>“Profitability and investment risk of Texan power system winterization” published in Nature Energy. Code used to generate these results can be found at <a href="https://github.com/inwe-boku/texas-power-outages">github</a> Abstract A lack of winterization of power system infrastructure resulted in significant rolling blackouts in Texas in 2021 though debate about the cost of winterization continues. Here, we assess if incentives for winterization on the energy only market are sufficient. We combine power demand estimates with estimates of power plant outages to derive power deficits and scarcity prices. Expected profits from winterization of a large share of existing capacity are positive. However, investment risk is high due to the low frequency of freeze events, potentially explaining under-investment, as do high discount rates and uncertainty about power generation failure under cold temperatures. As the social cost of power deficits is one to two orders of magnitude higher than winterization cost, regulatory enforcement of winterization is welfare enhancing. Current legislation can be improved by emphasizing winterization of gas power plants and infrastructure. Date and time format Please observe that we omit the date column from the description of columns below for all datasets. The ERA5 data in input/ is in UTC, all other input datasets are in local Texas time (GMT-6). In interim, temperatures/temppop/, temperatures/temp_gas_powerplant.csv, temperatures/temp_gas_outages.csv, temperatures/temp_coal_powerplant.csv, temperatures/temp_coal_outages.csv and the wind power simulation output (windpower/) is in UTC. All other datasets are in local Texas time. Data cache/ Data cache used by the scripts analyzing the extreme events: extreme temperatures, loss of load, their return periods, durations, maxima/minima (the cached files are not included, but can be generated with scripts/R/events.R) figures/ Figures shown in the manuscript <ul> <li>raw_data: includes raw data for reproducing the figures in the main part of the manuscript</li> <li>outage_model: figures representing the outage function as derived with our model</li> </ul> input/ Input data from external sources (with exception of orcd not included due to licensing issues) <ul> <li>ERA5_windspeeds_USA: available from the <a href="https://cds.climate.copernicus.eu/#!/home">CDS</a>. Download with scripts/download_era5_USA.py</li> <li>gas_production: available from the Texas Railroad Commission in PDF format <a href="https://www.rrc.state.tx.us/media/qcpp3bau/2020-12-monthly-production-county-gas.pdf">here</a>. We extracted the data manually.</li> <li>Load: available from ERCOT <a href="https://www.ercot.com/gridinfo/load">here</a></li> <li>orcd: Scarcity prices as regulated by ERCOT. Manually extracted from <a href="https://doi.org/10.1016/j.enpol.2019.111143.334">J. Zarnikau et al.</a></li> <li>outages: Outage Events from ERCOT with geo locations provided by Edgar Virguez <a href="https://bit.ly/EGOVADatabase">here</a> resulting from unit outage data provided by <a href="http://www.ercot.com/content/wcm/lists/226521/Unit_Outage_Data_20210312.xlsx">Ercot</a></li> <li>population: population density data provided by arcgis <a href="https://www.arcgis.com/home/item.html?id=28bcaee42e2c4ace9fcb7c8b9ca524e7">here</a></li> <li>powerplants: locations of power plants in Texas provided by the Energy Information Administration <a href="https://www.eia.gov/maps/layer_info-m.php">here</a></li> <li>shp: shapefile of Texas state boundaries provided by arcgis <a href="https://gis-txdot.opendata.arcgis.com/datasets/texas-state-boundary-detailed">here</a></li> <li>temperatures: available from the CDS <a href="https://cds.climate.copernicus.eu/#!/home">here</a>. Can be downloaded with script scripts/download_era5_TX_temp.py</li> <li>USWTDB: US wind turbine data base provided by the US Geological Service <a href="https://eerscmap.usgs.gov/uswtdb/">here</a>. We used version: uswtdb_v3_3_20210114</li> <li>GWA2: Global Wind Atlas Version 2.1 accessible <a href="https://silo1.sciencedata.dk/shared/cf5a3255eb87ca25b79aedd8afcaf570?path=%2FGWA2.1">here</a></li> </ul> interim/ Intermediary files from the analysis <ul> <li>bootstrap_year.csv: 30 randomly selected years between 1950 and 2021, 10,000 times used for bootstrapping Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb</li> <li>bootstrap_year2020.csv: 30 randomly selected years between 1950 and 2020, 10,000 times used for bootstrapping without 2021 event Generated by outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb</li> <li>turbine_data.csv: turbine data for Texan wind turbines Generated by scripts/prepare_TX_turbines.py Columns: <ul> <li>capacity: turbine capacity (kW)</li> <li>height: turbine height (m)</li> <li>lon: longitude coordinate (°)</li> <li>lat: latitude coordinate (°)</li> <li>sp: specific power (W/m²)</li> <li>ind: running index</li> </ul> </li> </ul> interim/load/ Temperature dependent estimates of electricity load for Texas. <ul> <li>load_est70_LR24_temptrend_Hook-8.csv Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb Columns: <ul> <li>load_est: load estimated for the period 1950-2021 assuming an average load level as in 2021 (MWh)</li> <li>temp: population weighted temperature (°C)</li> </ul> </li> <li>load_est10_LR24_temptrend_Hook-8.csv Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb Columns: <ul> <li>load: observed load in period 2012-2021 as published by ERCOT (MWh)</li> <li>load_est: load estimated for period 2012-2021 considering time trend, i.e. this is a replication of the observed load without outages with our model for validation purposes (MWh)</li> </ul> </li> <li>load_est9_LR24temptrend2021_Hook-8.csv Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb Columns: <ul> <li>load: observed load in period 2004-2021/01 and load forecast 2021/02 as published by ERCOT (MWh)</li> <li>load_est: load estimated for the years 2012-2020 for cross validation of load model. For training, the years 2012-2021 (2021/02 forecast) were used, except the predicted year, i.e. this is a replication of the observed load with our model for validation purposes. (MWh)</li> </ul> </li> <li>load_est17_crossvalidation_LR24temptrend_Hook-8.csv Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb Columns: <ul> <li>load: observed load 2004 - 2021/01 and load forecast 2021/02 as published by ERCOT (MWh)</li> <li>load_est: load estimated for cross validation for years 2004-2021, training years 2012-2020, trained with each year in traning period except modelled year with variable load level, i.e. this is a replication of the observed load with our model for validation purposes(MWh)</li> </ul> </li> <li>load_est_LR24temptrend_Hook-8.csv Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb Columns: <ul> <li>load: observed load 2004 - 2021 as published by ERCOT (MWh)</li> <li>load_est: years 2004-2021 predicted with a model which was trained for the years 2012-2020 considering time trend, i.e. this is a replication of the observed load without outages with our temperature dependent model with our model for validation purposes (MWh)</li> </ul> </li> </ul> interim/outages <ul> <li>outages.feather Outage by minute of all generation units in Texas in February 2021. Created by scripts/R/create-ercot-outage-timeseries.R In Texas local time. Columns: <ul> <li>station: name of power plant</li> <li>unit: name of generation unit</li> <li>fullname: concatenated string of station and name</li> <li>dataset: ercot or edgar. ercot refers to the raw dataset provided by ERCOT, Edgar to the dataset provided by Edgar Virguez (for details see above in section input/)</li> <li>Longitude: Longitude of location of power plant</li> <li>Latitude: Latitude of location of power plant</li> <li>reduction: hourly reduction of capacity due to outage in this minute (MW)</li> <li>cap_available: available capacity in this minute (MW)</li> <li>cap_max: maximum capacity of unit (MW)</li> </ul> </li> <li>outages-hourly.feather Hourly outages at all generation units in Texas in February 2021. Created by scripts/R/create-ercot-outage-timeseries.R In Texas local time. Columns: <ul> <li>station: name of power plant</li> <li>unit: name of generation unit</li> <li>fullname: concatenated string of station and name</li> <li>dataset: ercot or edgar. ercot refers to the raw dataset provided by ERCOT, Edgar to the dataset provided by Edgar Virguez (for details see above in section input/)</li> <li>Longitude: Longitude of location of power plant</li> <li>Latitude: Latitude of location of power plant</li> <li>reduction: hourly reduction of capacity due to outage in this time step (MW)</li> <li>cap_available: hourly available capacity in this minute (MW)</li> <li>cap_max: maximum capacity of unit (MW)</li> </ul> </li> <li>outages_reduction.csv Hourly outages per fuel (MW). We use these outages for COAL and GAS only in the analysis. Generated by notebooks/prepare_outages_NSsplit.ipynb Columns: <ul> <li>NG: natural gas power plants</li> <li>WIND: wind power plants</li> <li>SOLAR: solar power plants</li> <li>ESR: energy storage resource</li> <li>HYDRO: hydropower plants</li> <li>NUCLEAR: nuclear power plants</li> </ul> </li> <li>outages_reductionNorth.csv Hourly outages for the Northern part of Texas (latitude > 30) (MW). We use these outages for WIND only in the analysis. Generated by notebooks/prepare_outages_NSsplit.ipynb Columns as above.</li> <li>outages_reductionSouth.csv Hourly outages for the Southern part area of Texas (latitude <= 30) (MW). We use these outages for WIND only in the analysis. Generated by notebooks/prepare_outages_NSsplit.ipynb Columns as above.</li> </ul> interim/temperatures <ul> <li>temppop Generated by scripts/calc_temppopC.py <ul> <li>contains population weighted temperatures for Texas, one file for each year (°C).</li> </ul> </li> <li>temp_coal_outage.csv Generated by notebooks/temperatures_NSsplit.ipynb Columns: <ul> <li>t2m: temperature weighted by coal power plants experiencing outages in February 2021 (°C)</li> </ul> </li> <li>temp_coal_powerplant.csv Generated by notebooks/temperatures_NSsplit.ipynb Columns: <ul> <li>t2m: temperature weighted by all coal power plants (°C)</li> </ul> </li> <li>temp_gas_outage.csv Generated by notebooks/temperatures_NSsplit.ipynb Columns: <ul> <li>t2m: temperature weighted by gaspower plants experiencing outages in February 2021 (°C)</li> </ul> </li> <li>temp_gas_powerplant.csv Generated by notebooks/temperatures_NSsplit.ipynb Columns: <ul> <li>t2m: temperature weighted by all gas power plants (°C)</li> </ul> </li> <li>temp_gasfields.csv Generated by notebooks/temperatures_NSsplit.ipynb Columns: <ul> <li>t2m: temperature weighted by all gasfields (°C)</li> </ul> </li> <li>tempWP_NSsplit.csv Generated by notebooks/wp_temp_NSsplit.ipynb Columns: <ul> <li>t2mSouth: temperatures weighted by all wind power plants in the South (°C)</li> <li>t2mNorth: temperatures weighted by all wind power plants in the North (°C)</li> </ul> </li> </ul> interim/thresholds <ul> <li>thresh_total63.5GW.csv Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>total available capacity of gas, coal and wind considering outages, assuming gasfield temperatures for gas outages (GW)</li> </ul> </li> <li>thresh_totalPP63.5GW.csv Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>total available capacity of gas, coal and wind, considering outages, assuming gas power plant temperatures for gas outages (GW)</li> </ul> </li> <li>threshold_coal.csv Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>outages of coal power plants based on coal power plant temperatures (GW)</li> </ul> </li> <li>threshold_gas.csv Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>outages of gas power plants based on gasfield temperatures (GW)</li> </ul> </li> <li>threshold_gasPP.csv Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>outages of gas power plants based on gas power plant temperatures (GW)</li> </ul> </li> <li>threshold_gas_coal63.5GW.csv Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>available capacity of gas and coal, considering outages, assuming gasfield temperatures for gas outages (GW)</li> </ul> </li> <li>threshold_gas_coalPP63.5GW.csv Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>available capacity of gas and coal, considering outages, assuming gas power plant temperatures for gas outages (GW)</li> </ul> </li> </ul> interim/windpower <ul> <li>cfTXh.csv Generated by notebooks/windpower_ERA5_GWA2_const_cap.ipynb Columns: <ul> <li>Capacity factors of simulated Texan wind power (dimensionless)</li> </ul> </li> <li>wpTXh.csv Generated by notebooks/windpower_ERA5_GWA2_const_cap.ipynb Columns:</li> <li>simulated Texan wind power generation (kWh)</li> </ul> output/ <ul> <li>marginal_revenue_coal_10_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_10_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization for coal (bn$/GW winterized) with discount rate of 10% and deficit events up to 2021</li> </ul> </li> <li>marginal_revenue_coal_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of coal (bn$/GW winterized) with discount rate of 5% and deficit events up to 2021</li> </ul> </li> <li>marginal_revenue_coal2020_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_2020_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of coal (bn$/GW winterized) with discount rate of 5% and deficit events up to 2020</li> </ul> </li> <li>marginal_revenue_gas_10_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_10_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of gas (bn$/GW winterized) with discount rate of 10% and deficit events up to 2021</li> </ul> </li> <li>marginal_revenue_gas_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of gas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2021</li> </ul> </li> <li>marginal_revenue_gas2020_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_2020_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of gas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2020</li> </ul> </li> <li>marginal_revenue_wind_north_10_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_10_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of Northern Wind in Texas (bn$/GW winterized) with discount rate of 10% and deficit events up to 2021</li> </ul> </li> <li>marginal_revenue_wind_north_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of Northern Wind in Texas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2021</li> </ul> </li> <li>marginal_revenue_wind_north2020_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_2020_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of Northern Wind in Texas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2020</li> </ul> </li> <li>marginal_revenue_wind_south_10_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_10_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of Southern Wind in Texas (bn$/GW winterized) with discount rate of 10% and deficit events up to 2021</li> </ul> </li> <li>marginal_revenue_wind_south_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of Southern Wind in Texas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2021</li> </ul> </li> <li>marginal_revenue_wind_south2020_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_2020_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of Southern Wind in Texas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2020</li> </ul> </li> <li>sensitivity_analysis_temperature_thresholds_all_LR24temptrend_Hook-8.csv Generated by notebooks/outages_thresholds_sensitivity_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>delta_thresh_temp: change in outage temperature thresholds for all technologies (°C)</li> <li>delta_rec_temp: change in recovery temperature thresholds for all technologies (°C)</li> <li>number_of_events: estimated number of events within 7 decades</li> <li>after2004: years of events after 2004</li> <li>total_loss_TWh: total loss of load (TWh)</li> <li>max_loss_GW: maximum loss of load (GW)</li> <li>mean_loss_GW: average loss og load (GW)</li> <li>mean_revenue_mioUSD: average revenue (M$)</li> <li>rank_2021_event: rank of 2021 event in terms of loss of load</li> </ul> </li> <li>sensitivity_analysis_temperature_thresholds_coal_LR24temptrend_Hook-8.csv Generated by notebooks/outages_thresholds_sensitivity_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>thresh_coal_temp: outage temperature thresholds for coal (°C)</li> <li>rec_coal_temp: recovery temperature thresholds for coal (°C)</li> <li>number_of_events: estimated number of events within 7 decades</li> <li>after2004: years of events after 2004</li> <li>total_loss_TWh: total loss of load (TWh)</li> <li>max_loss_GW: maximum loss of load (GW)</li> <li>mean_loss_GW: average loss og load (GW)</li> <li>mean_revenue_mioUSD: average revenue (M$)</li> <li>rank_2021_event: rank of 2021 event in terms of loss of load</li> </ul> </li> <li>sensitivity_analysis_temperature_thresholds_gas_LR24temptrend_Hook-8.csv Generated by notebooks/outages_thresholds_sensitivity_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>thresh_gas_temp: outage temperature thresholds for gas (°C)</li> <li>rec_gas_temp: recovery temperature thresholds for gas (°C)</li> <li>number_of_events: estimated number of events within 7 decades</li> <li>after2004: years of events after 2004</li> <li>total_loss_TWh: total loss of load (TWh)</li> <li>max_loss_GW: maximum loss of load (GW)</li> <li>mean_loss_GW: average loss og load (GW)</li> <li>mean_revenue_mioUSD: average revenue (M$)</li> <li>rank_2021_event: rank of 2021 event in terms of loss of load</li> </ul> </li> <li>sensitivity_analysis_temperature_thresholds_wind_north_LR24temptrend_Hook-8.csv Generated by notebooks/outages_thresholds_sensitivity_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>thresh_windn_temp: outage temperature thresholds for wind north (°C)</li> <li>rec_windn_temp:recovery temperature thresholds for wind north (°C)</li> <li>number_of_events: estimated number of events within 7 decades</li> <li>after2004: years of events after 2004</li> <li>total_loss_TWh: total loss of load (TWh)</li> <li>max_loss_GW: maximum loss of load (GW)</li> <li>mean_loss_GW: average loss og load (GW)</li> <li>mean_revenue_mioUSD: average revenue (M$)</li> <li>rank_2021_event: rank of 2021 event in terms of loss of load</li> </ul> </li> <li>sensitivity_analysis_temperature_thresholds_wind_south_LR24temptrend_Hook-8.csv Generated by notebooks/outages_thresholds_sensitivity_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>thresh_winds_temp: outage temperature thresholds for wind south (°C)</li> <li>rec_winds_temp:recovery temperature thresholds for wind south (°C)</li> <li>number_of_events: estimated number of events within 7 decades</li> <li>after2004: years of events after 2004</li> <li>total_loss_TWh: total loss of load (TWh)</li> <li>max_loss_GW: maximum loss of load (GW)</li> <li>mean_loss_GW: average loss og load (GW)</li> <li>mean_revenue_mioUSD: average revenue (M$)</li> <li>rank_2021_event: rank of 2021 event in terms of loss of load</li> </ul> </li> </ul>

opencc-by-4.0Apr 2024View details →

European Investment Bank Projects in ACP, OCT, Africa, Asia, and Latin America (1957-2024)

This dataset offers a comprehensive analysis of European Investment Bank (EIB) projects in Africa, the Caribbean, and the Pacific (ACP) regions, Overseas Countries and Territories (OCT), Asia, and Latin America, spanning from 1975 to 2023. The dataset includes information on 2,558 projects; each entry in the dataset includes key project details such as the project’s sector, date of signature, and financial commitments. All numbers are in 2015 euros.

opencc-by-4.0Aug 2019View details →

Zambezi dataset to "WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus"

This is the dataset used in the HESS publication "<a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus</a>" The dataset describes the water-energy-food nexus of the Zambezi River Basin used as input to the <a href="https://github.com/RaphaelPB/WHAT-IF">WHAT-IF model</a>. The file Data_Organization.pdf, summarizes the available data. For more info look at the <a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">publication</a> and/or <a href="https://github.com/RaphaelPB/WHAT-IF">Github</a>.

opencc-by-4.0Oct 2024View details →

Ferry et al. 2024 - Prey that is attractive but not repelled by predators suggests an asymmetric investment in the encounter-avoid-escape sequence. - R Code and Datasets

R code for formating data and running PAMMs for all different combinations of predator-prey. Data of camera trap observation. Data of environmental variable associated to camera trap sites.

opencc-by-4.0Dec 2022View details →

Meta-analysis on necessary investment shifts to reach net zero pathways in Europe

This is the code and the data necessary to reproduce the six main figures and the t-test presented in the supplementary information of the publication "Meta-analysis on necessary investment shifts to reach net zero pathways in Europe". DOI: 10.1038/s41558-022-01549-5

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.

edi44/100

Return on Investment Metrics for Data Repositories in Earth and Environmental Sciences

Despite a growing recognition of the importance of data to the economy and to science, investment in repositories to manage and disseminate that data in easily accessible and understandable ways is scarce. Keeping repository services active and up-to-date for a long time period is difficult due to this funding situation. As a result, repositories must continually provide proof of their value, their Return on Investment (ROI) to their sponsors; yet doing so has always been difficult, problematic and not always successful. In this work, an analysis of approaches for assessing the ROI of several scientific data repositories has identified various techniques that repositories use to report on the impact and value of their data products and services. A survey of selected repositories rated the set of metrics identified and rated each by its importance as well as the ease with which the metric could be measured. The discussion is broken down into considerations for calculating costs, perceived value of repositories and suggested metrics that would allow a repository to calculate an ROI. The authors, representatives of environmental data repositories, concluded that easily obtainable data use metrics, such as data downloads, etc., have limited value while more informative analyses would require additional resources.

openCC (other)Feb 2019View details →

opencc-by-4.0Jan 2024View details →

Investment Readiness Level - Self-assessment - BIOBEC

This document will provide you with some guidelines on how the investment readiness level assessment was conducted for each of the BBECs, which is part of Economic Requirements for BBEC task to identify the financial needs of the BBECs and to develop a tailored financial plan for each of them The overall goal is to provide innovative pathways and strategies with regard to access to financial resources, to encourage the economic sustainability of the implementation and maintenance phase of the BBECs, and eventually ensure their lifelong continuation of them. By using the self-assessment, individuals and organizations can gain a better understanding of the current state of innovative education centers, as well as identify areas for improvement or potential opportunities for growth. This can help individuals and organizations make more informed decisions about which innovative education centers to invest in, or how to improve existing education centers in order to maximize their impact on the bio-based economy.   A)   Competence and structure of the BBEC: This dimension assesses the competency and structure of the leadership team and staff of the innovative education center. The questions in this dimension are designed to evaluate the level of expertise and experience of the CEO, board members, and employees, as well as the overall structure and organization of the education center.   B)    Markets: This dimension evaluates the market potential of the innovative education center. The questions in this dimension are designed to assess the size and growth potential of the target market, as well as the unique value proposition that the education center offers to its target market.   C)   Talent: This dimension assesses the quality and availability of talent within the innovative education center. The questions in this dimension are designed to evaluate the education center's strategy for attracting and retaining top talent, as well as its culture of learning and professional development.   D)   Technology landscape: This dimension evaluates the education center's use of technology in its teaching practices and curriculum. The questions in this dimension are designed to assess the education center's access to technology tools and hardware, as well as its plan for integrating technology into its teaching practices.   E)    Financials: This dimension assesses the financial sustainability of the innovative education center. The questions in this dimension are designed to evaluate the education center's business model, as well as its projected revenue, expenses, and profits.   F)     Economic: This dimension evaluates the potential economic impact of the innovative education center on the local community. The questions in this dimension are designed to assess the education center's ability to create jobs and increase productivity, as well as its potential risks or challenges.

dryad40/100

Sensory weighting reflects changing patterns of visual investment during ecological divergence in Heliconius butterflies

Integrating information across sensory modalities enables animals to orchestrate a wide range of complex behaviours. The relative importance placed on one sensory modality over another reflects the reliability of cues in a particular environment and corresponding differences in neural investment. As populations diverge across environmental gradients, the reliability of sensory cues may shift, favouring divergence in neural investment and sensory weighting. During their divergence across closed-forest and forest-edge habitats, Heliconius butterflies H. cydno and H. melpomene evolved distinct brain morphologies, with the former investing more in vision. Molecular and anatomical data suggest selection drove these changes, but their behavioural effects remain uncertain. We hypothesised that divergent investment in neuropils may alter sensory weighting during behavioural tasks. To address this, we trained individuals in an associative learning experiment using multimodal colour and odour cues. When positively rewarded stimuli were presented in conflict pairing positively trained colour with negatively trained odour, and vice-versa, H. cydno prioritised visual cues more strongly than H. melpomene. Hence, differences in sensory weighting may evolve early during divergence and are predicted by patterns of neural investment. These findings, alongside other examples, imply that differences in sensory weighting stem from sensory investment as adaptations to local sensory environments.

opencc-zeroApr 2024View details →

opencc-by-4.0Feb 2022View details →

Equitable Access to Residential (EQUATOR) EV Charging: Optimal Investments in EV charging

This folder comprises Julia codes produced to determine optimal investments in EV charging for Equitable Access to Residential (EQUATOR) EV Charging project. The file descriptions are below:   1. Generator.csv: contains generator data for all the generators in the Manhattan power network 2. Node.csv: contains the load data for each bus in the Manhattan power network 3. Line.csv: contains technical line parameters for all the lines in the Manhattan power network 4. NetworkDataType.jl and NetworkLoad.jl: Julia files to process csv data and design the Manhattan power grid 5. Justice_LL.jl: Justice modeling of the power utility 6. Justice_Case1_fixed.jl: Julia file for determining optimal investments in EV charging in Manhattan.

opencc-by-4.0Mar 2022View details →

Data for research article "CCS investment – fiddling while the planet burns"

This data set contains electricity systems and technology data for the UK, Poland, Texas, Wyoming, South Korea, and Indonesia. This data was used for modeling and analysis for the research article titled 'CCS investment – fiddling while the planet burns', authored by Yoga Wienda Pratama and Niall Mac Dowell from Imperial College London. In this work, we modeled and optimised the systems using Electricity Systems Optimisation framework (DOI: 10.5281/zenodo.1048943) that was developed General Algebraic Modeling System (GAMS). Data for this study is therefore provided in .gdx format that is suitable for GAMS. Procedure to reproduce this study is discussed in the article. Further questions can be addressed to Yoga Wienda Pratama (y.pratama18@imperial.ac.uk) or Niall Mac Dowell (niall@imperial.ac.uk).