This data set contains the replication data and supplements for the article "Knowing, Doing, and Feeling: A three-year, mixed-methods study of undergraduates’ information literacy development." The survey data is from two samples: - cross-sectional sample (different students at the same point in time) - longitudinal sample (the same students and different points in time)Surveys were distributed via Qualtrics during the students' first and sixth semesters. Quantitative and qualitative data were collected and used to describe students' IL development over 3 years. Statistics from the quantitative data were analyzed in SPSS. The qualitative data was coded and analyzed thematically in NVivo. The qualitative, textual data is from semi-structured interviews with sixth-semester students in psychology at UiT, both focus groups and individual interviews. All data were collected as part of the contact author's PhD research on information literacy (IL) at UiT. The following files are included in this data set: 1. A README file which explains the quantitative data files. (2 file formats: .txt, .pdf)2. The consent form for participants (in Norwegian). (2 file formats: .txt, .pdf)3. Six data files with survey results from UiT psychology undergraduate students for the cross-sectional (n=209) and longitudinal (n=56) samples, in 3 formats (.dat, .csv, .sav). The data was collected in Qualtrics from fall 2019 to fall 2022. 4. Interview guide for 3 focus group interviews. File format: .txt5. Interview guides for 7 individual interviews - first round (n=4) and second round (n=3). File format: .txt 6. The 21-item IL test (Tromsø Information Literacy Test = TILT), in English and Norwegian. TILT is used for assessing students' knowledge of three aspects of IL: evaluating sources, using sources, and seeking information. The test is multiple choice, with four alternative answers for each item. This test is a "KNOW-measure," intended to measure what students know about information literacy. (2 file formats: .txt, .pdf)7. Survey questions related to interest - specifically students' interest in being or becoming information literate - in 3 parts (all in English and Norwegian): a) information and questions about the 4 phases of interest; b) interest questionnaire with 26 items in 7 subscales (Tromsø Interest Questionnaire - TRIQ); c) Survey questions about IL and interest, need, and intent. (2 file formats: .txt, .pdf)8. Information about the assignment-based measures used to measure what students do in practice when evaluating and using sources. Students were evaluated with these measures in their first and sixth semesters. (2 file formats: .txt, .pdf)9. The Norwegain Centre for Research Data's (NSD) 2019 assessment of the notification form for personal data for the PhD research project. In Norwegian. (Format: .pdf)

Abstract

This study examines various dimensions of primary health care delivery in Uganda, using a baseline survey of public and private dispensaries, the most common lower level health facilities in the country.

The survey was designed and implemented by the World Bank in collaboration with the Makerere Institute for Social Research and the Ugandan Ministries of Health and of Finance, Planning and Economic Development. It was carried out in October - December 2000 and covered 155 local health facilities and seven district administrations in ten districts. In addition, 1617 patients exiting health facilities were interviewed. Three types of dispensaries (both with and without maternity units) were included: those run by the government, by private for-profit providers, and by private nonprofit providers, mainly religious.

This research is a Quantitative Service Delivery Survey (QSDS). It collected microlevel data on service provision and analyzed health service delivery from a public expenditure perspective with a view to informing expenditure and budget decision-making, as well as sector policy.

Objectives of the study included: 1) Measuring and explaining the variation in cost-efficiency across health units in Uganda, with a focus on the flow and use of resources at the facility level; 2) Diagnosing problems with facility performance, including the extent of drug leakage, as well as staff performance and availability;
3) Providing information on pricing and user fee policies and assessing the types of service actually provided; 4) Shedding light on the quality of service across the three categories of service provider - government, for-profit, and nonprofit; 5) Examining the patterns of remuneration, pay structure, and oversight and monitoring and their effects on health unit performance; 6) Assessing the private-public partnership, particularly the program of financial aid to nonprofits.

Geographic coverage

The study districts were Mpigi, Mukono, and Masaka in the central region; Mbale, Iganga, and Soroti in the east; Arua and Apac in the north; and Mbarara and Bushenyi in the west.

Analysis unit

• local dispensary with or without maternity unit

Universe

The survey covered government, for-profit and nonprofit private dispensaries with or without maternity units in ten Ugandan districts.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey covered government, for-profit and nonprofit private dispensaries with or without maternity units in ten Ugandan districts.

The sample design was governed by three principles. First, to ensure a degree of homogeneity across sampled facilities, attention was restricted to dispensaries, with and without maternity units (that is, to the health center III level). Second, subject to security constraints, the sample was intended to capture regional differences. Finally, the sample had to include facilities in the main ownership categories: government, private for-profit, and private nonprofit (religious organizations and NGOs). The sample of government and nonprofit facilities was based on the Ministry of Health facility register for 1999. Since no nationwide census of for-profit facilities was available, these facilities were chosen by asking sampled government facilities to identify the closest private dispensary.

Of the 155 health facilities surveyed, 81 were government facilities, 30 were private for-profit facilities, and 44 were nonprofit facilities. An exit poll of clients covered 1,617 individuals.

The final sample consisted of 155 primary health care facilities drawn from ten districts in the central, eastern, northern, and western regions of the country. It included government, private for-profit, and private nonprofit facilities. The nonprofit sector includes facilities owned and operated by religious organizations and NGOs. Approximately one third of the surveyed facilities were dispensaries without maternity units; the rest provided maternity care. The facilities varied considerably in size, from units run by a single individual to facilities with as many as 19 staff members.

Ministry of Health facility register for 1999 was used to design the sampling frame. Ten districts were randomly selected. From the selected districts, a sample of government and private nonprofit facilities and a reserve list of replacement facilities were randomly drawn. Because of the unreliability of the register for private for-profit facilities, it was decided that for-profit facilities would be identified on the basis of information from the government facilities sampled. The administrative records for facilities in the original sample were first reviewed at the district headquarters, where some facilities that did not meet selection criteria and data collection requirements were dropped from the sample. These were replaced by facilities from the reserve list. Overall, 30 facilities were replaced.

The sample was designed in such a way that the proportion of facilities drawn from different regions and ownership categories broadly mirrors that of the universe of facilities. Because no nationwide census of for-profit health facilities is available, it is difficult to assess the extent to which the sample is representative of this category. A census of health care facilities in selected districts, carried out in the context of the Delivery of Improved Services for Health (DISH) project supported by the U.S. Agency for International Development (USAID), suggests that about 63 percent of all facilities operate on a for-profit basis, while government and nonprofit providers run 26 and 11 percent of facilities, respectively. This would suggest an undersampling of private providers in the survey. It is not clear, however, whether the DISH districts are representative of other districts in Uganda in terms of the market for health care.

For the exit poll, 10 interviews per facility were carried out in approximately 85 percent of the facilities. In the remaining facilities the target of 10 interviews was not met, as a result of low activity levels.

Sampling deviation

In the first stage in the sampling process, eight districts (out of 45) had to be dropped from the sample frame due to security concerns. These districts were Bundibugyo, Gulu, Kabarole, Kasese, Kibaale, Kitgum, Kotido, and Moroto.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available:

• District Health Team Questionnaire;
• District Facility Data Sheets;
• Uganda Health Facility Survey Questionnaire;
• Facility Data Sheets;
• Facility Patient Exit Poll Questionnaire.

The survey collected data at three levels: district administration, health facility, and client. In this way it was possible to capture central elements of the relationships between the provider organization, the frontline facility, and the user. In addition, comparison of data from different levels (triangulation) permitted cross-validation of information.

At the district level, a District Health Team Questionnaire was administered to the district director of health services (DDHS), who was interviewed on the role of the DDHS office in health service delivery. Specifically, the questionnaire collected data on health infrastructure, staff training, support and supervision arrangements, and sources of financing.

The District Facility Data Sheet was used at the district level to collect more detailed information on the sampled health units for fiscal 1999-2000, including data on staffing and the related salary structures, vaccine supplies and immunization activity, and basic and supplementary supplies of drugs to the facilities. In addition, patient data, including monthly returns from facilities on total numbers of outpatients, inpatients, immunizations, and deliveries, were reviewed for the period April-June 2000.

At the facility level, the Uganda Health Facility Survey Questionnaire collected a broad range of information related to the facility and its activities. The questionnaire, which was administered to the in-charge, covered characteristics of the facility (location, type, level, ownership, catchment area, organization, and services); inputs (staff, drugs, vaccines, medical and nonmedical consumables, and capital inputs); outputs (facility utilization and referrals); financing (user charges, cost of services by category, expenditures, and financial and in-kind support); and institutional support (supervision, reporting, performance assessment, and procurement). Each health facility questionnaire was supplemented by a Facility Data Sheet (FDS). The FDS was designed to obtain data from the health unit records on staffing and the related salary structure; daily patient records for fiscal 1999-2000; the type of patients using the facility; vaccinations offered; and drug supply and use at the facility.

Finally, at the facility level, an exit poll was used to interview about 10 patients per facility on the cost of treatment, drugs received, perceived quality of services, and reasons for using that unit instead of alternative sources of health care.

Cleaning operations

Detailed information about data editing procedures is available in "Data Cleaning Guide for PETS/QSDS Surveys" in external resources.

STATA cleaning do-files and the data quality reports on the datasets can also be found in external resources.

The file contains supporting data sources for the research paper entitled "QUANTITATIVE EVALUATION OF SUSTAINABLE MARKETING EFFECTIVENESS: A POLISH CASE STUDY" submitted to a selected scientific journal for a prospective publication.

This data set contains the replication data for the article "Knowing and doing: The development of information literacy measures to assess knowledge and practice." This article was published in the Journal of Information Literacy, in June 2021. The data was collected as part of the contact author's PhD research on information literacy (IL). One goal of this study is to assess students' levels of IL using three measures: 1) a 21-item IL test for assessing students' knowledge of three aspects of IL: evaluating sources, using sources, and seeking information. The test is multiple choice, with four alternative answers for each item. This test is a "KNOW-measure," intended to measure what students know. 2) a source-evaluation measure to assess students' abilities to critically evaluate information sources in practice. This is a "DO-measure," intended to measure what students do in practice, in actual assignments. 3) a source-use measure to assess students' abilities to use sources correctly when writing. This is a "DO-measure," intended to measure what students do in practice, in actual assignments. The data set contains survey results from 626 Norwegian and international students at three levels of higher education: bachelor, master's and PhD. The data was collected in Qualtrics from fall 2019 to spring 2020. In addition to the data set and this README file, two other files are available here: 1) test questions in the survey, including answer alternatives (IL_knowledge_tests.txt) 2) details of the assignment-based measures for assessing source evaluation and source use (Assignment_based_measures_assessing_IL_skills.txt) Publication abstract: This study touches upon three major themes in the field of information literacy (IL): the assessment of IL, the association between IL knowledge and skills, and the dimensionality of the IL construct. Three quantitative measures were developed and tested with several samples of university students to assess knowledge and skills for core facets of IL. These measures are freely available, applicable across disciplines, and easy to administer. Results indicate they are likely to be reliable and support valid interpretations. By measuring both knowledge and practice, the tools indicated low to moderate correlations between what students know about IL, and what they actually do when evaluating and using sources in authentic, graded assignments. The study is unique in using actual coursework to compare knowing and doing regarding students’ evaluation and use of sources. It provides one of the most thorough documentations of the development and testing of IL assessment measures to date. Results also urge us to ask whether the source-focused components of IL – information seeking, source evaluation and source use – can be considered unidimensional constructs or sets of disparate and more loosely related components, and findings support their heterogeneity.

Abstract copyright UK Data Service and data collection copyright owner.

This research project aims to provide an in-depth study of the problems encountered in developing quantitative socio-economic indicators for urban and regional policy analysis in Britain. The project consisted of two stages. The data from the first stage of the study are held under SN:3973.
The aims of this part of the study, the second stage, are to address two main objectives, through :
1. The compilation of a meta-database to assess the quality of information sources that are relevant to Local Economic Development (LED), which will provide useful guidelines for future public data compilation practice and will be of use to other researchers in the field.
2. The compilation of an up-to-date database of indicators for LED for the analysis of the following :
what extent the existence of information gaps in public statistical sources affects indicator research on LED;
which particular dimension of LED is most affected;
what other non-public sources of information are available for the measurement of LED;
what are the best data handling techniques to explore the statistical properties of indicators and to carry out preliminary validation analysis of the assembled database;
in what ways could innovative data processing and analysis enhance the interpretation of indicators and maximise the intelligence yielded from the information available;
in what form should indicators be aggregated to gear to the needs of policy-makers;
what are the most appropriate weighting methods to create multivariate indexes;
in what ways do different weighting methods affect the outcome of the final analysis.
Main Topics:

The data file for this study contains 61 variables from the 366 Local Authority Districts in England (before the 1996 local government reorganisation of boundaries). Apart from the LAD (Local Authority District) code and the matching UALAD (Unitary Authority and Local Authority District) code, the other 59 variables are indicators measuring different contributing factors to the process of LED. Eleven key factors were identified, through literature review and a survey of policy-makers, to be important to the process of LED :
locational factors, physical factors, infrastructure, human resources, finance and capital, knowledge and technology, industrial structure, business culture, community image and identity, institutional capacity and quality of life.

Standardisation was applied to the raw data to develop some indicators to enhance interpretation. In some cases, the raw data are expressed as a percentage share of the national sum; in other cases, the case value is expressed as a ratio of the national (English) average value.

This data package includes a quantitative analysis of non-coral communities at sites along a water quality gradient off of Aua, American Samoa in 2022. These datasets were funded by the NOAA Coral Reef Conservation Program (CRCP) Project Number 31303 to study effects of land-based sources of pollution (LBSP) in Aua, American Samoa. In September 2022 Ecosystem Sciences Division (ESD) scientists of the Pacific Islands Fisheries Science Center (PIFSC) flew into American Samoa to survey 18 sites along a water quality gradient off of Aua. Three datasets are provided of the taxa species and counts of benthic foraminifera, benthic microalgae, and macroinvertebrates from samples collected between 9-28 September 2022. Samples were preserved in the field, and brought back to the NOAA Inouye Regional Center (IRC) and analyzed via microscopy. To analyze benthic foraminifera abundance, sediment samples were collected using a small sediment corer (60 ml syringe with the tip removed and a stopper placed there instead). Only the top 3 cm were retained. Under the microscope, benthic foraminifera were picked out of the sediment and identified. To analyze benthic microalgae, microscope slides were deployed on the seafloor at each site for 2 to 3 weeks. The benthic microalgae that settled was fixed and preserved in Lugol's solution and analyzed. Microalgae included diatoms (pennate and centric), dinoflagellates, chlorophyta, and cyanobacteria. To analyze macroinvertebrates: plastic scouring pads were deployed and attached to the substratum with zip-ties for 2 to 3 weeks. The scouring pads were removed after the settlement period, all plastic zip-ties and additional waste were removed from the reef. Macroinvertebrates that settled on the scouring pad were placed into sampling jars and fixed and preserved with 4% formalin, and subsequently analyzed under the microscope. These quantitative non-coral community surveys were one of several surveys conducted at the same sites across Aua reef in September 2022. Other surveys described and archived separately include surveys of water quality, CTD casts, coral demography, benthic imagery/benthic cover, and coral demography. These can be accessed under the 'Related Items' section of the InPort metadata record.

Images were acquired on a Nikon Ti-E system fitted with a Yokogawa CSU-X1 spinning disk head, Hamamatsu Orca Flash 4.0 v2 digital CMOS camera, Perfect Focus system, and a Nikon LU-N4 solid state laser launch (15 mW 405, 488, 561, and 647 nm) using a 100x 1.49 NA Apo TIRF oil-immersion objective. This microscope was controlled through Nikon Elements AR software on a 64-bit HP Z440 workstation.

Quantitative Data from Different Groundwater locations The Data is provided ‘as-is’, without any guarantee of correctness.

Punctual measurements of the Water sources flow Girst and Weissbach. The files in the compressed archive are Tab delimited text files, with ‘.’ as comma separator.

Objectives: In quantitative research, understanding basic parameters of the study population is key for interpretation of the results. As a result, it is typical for the first table (“Table 1”) of a research paper to include summary statistics for the study data. Our objectives are 2-fold. First, we seek to provide a simple, reproducible method for providing summary statistics for research papers in the Python programming language. Second, we seek to use the package to improve the quality of summary statistics reported in research papers.

Materials and Methods: The tableone package is developed following good practice guidelines for scientific computing and all code is made available under a permissive MIT License. A testing framework runs on a continuous integration server, helping to maintain code stability. Issues are tracked openly and public contributions are encouraged.

Results: The tableone software package automatically compiles summary statistics into publishable formats such...

Although rapid and relatively accurate graphical techniques exist for interpreting the depth to magnetic sources, these have now been largely supplanted by a wide range of computerised forward modelling routines capable of giving detailed estimates of source geometry as well as the source depth. Computer routines which have automated the depth estimation process also exist; however, these require considerable judgement on the part of the user, as they can give misleading results.

Data for Figures 2-5. This dataset is associated with the following publication: Shanks, O., A. Diedrich, M. Sivaganesan, J. Willis, and A. Shrifi. Quantitative fecal source characterization of urban municipal storm sewer system outfall ‘wet’ and ‘dry’ weather discharges. WATER RESEARCH. Elsevier Science Ltd, New York, NY, USA, 259: 121857, (2024).

Wikipedia is the largest and most read online free encyclopedia currently existing. As such, Wikipedia offers a large amount of data on all its own contents and interactions around them, as well as different types of open data sources. This makes Wikipedia a unique data source that can be analyzed with quantitative data science techniques. However, the enormous amount of data makes it difficult to have an overview, and sometimes many of the analytical possibilities that Wikipedia offers remain unknown. In order to reduce the complexity of identifying and collecting data on Wikipedia and expanding its analytical potential, after collecting different data from various sources and processing them, we have generated a dedicated Wikipedia Knowledge Graph aimed at facilitating the analysis, contextualization of the activity and relations of Wikipedia pages, in this case limited to its English edition. We share this Knowledge Graph dataset in an open way, aiming to be useful for a wide range of researchers, such as informetricians, sociologists or data scientists.

There are a total of 9 files, all of them in tsv format, and they have been built under a relational structure. The main one that acts as the core of the dataset is the page file, after it there are 4 files with different entities related to the Wikipedia pages (category, url, pub and page_property files) and 4 other files that act as "intermediate tables" making it possible to connect the pages both with the latter and between pages (page_category, page_url, page_pub and page_link files).

The document Dataset_summary includes a detailed description of the dataset.

Thanks to Nees Jan van Eck and the Centre for Science and Technology Studies (CWTS) for the valuable comments and suggestions.

Activity data for small molecules are invaluable in chemoinformatics. Various bioactivity databases exist containing detailed information of target proteins and quantitative binding data for small molecules extracted from journals and patents. In the current work, we have merged several public and commercial bioactivity databases into one bioactivity metabase. The molecular presentation, target information, and activity data of the vendor databases were standardized. The main motivation of the work was to create a single relational database which allows fast and simple data retrieval by in-house scientists. Second, we wanted to know the amount of overlap between databases by commercial and public vendors to see whether the former contain data complementing the latter. Third, we quantified the degree of inconsistency between data sources by comparing data points derived from the same scientific article cited by more than one vendor. We found that each data source contains unique data which is due to different scientific articles cited by the vendors. When comparing data derived from the same article we found that inconsistencies between the vendors are common. In conclusion, using databases of different vendors is still useful since the data overlap is not complete. It should be noted that this can be partially explained by the inconsistencies and errors in the source data.

This dataset presents a compendium of field-based earthworm data sources and associated meta-data from across the United Kingdom and Ireland (‘Worm source’). These were compiled up to 2021 and include 257 data sources, the earliest dating back to 1891. Source meta-data covers the type of quantitative earthworm data (i.e. incidence, abundance, biomass, taxa), methodological details (e.g. sampling method/s, location/s, whether sampled plots were natural or experimental, sampling year/s), and environmental information (e.g. habitat/land-use, inclusion of climate data and basic soil properties). Data sources were collected through literature searches on Web of Science and Google Scholar, as well as directly from original authors/data holders where possible. The data sources were compiled with the aim of gathering quantitative data on earthworm species and populations to develop earthworm abundance and niche models, and toward a modelling framework for earthworm impacts on soil processes. This work is part of the Soil Organic Carbon Dynamics (SOC-D) project funded by the NERC UK-SCAPE programme (Grant reference NE/R016429/1). Full details about this dataset can be found at https://doi.org/10.5285/1a1000a8-4e7e-4851-8784-94c7ba3e164f

Data from: Effects of input data sources on species distribution model predictions across species with different distributional ranges

https://doi.org/10.5061/dryad.qfttdz0jm

To perform and replicate this study, this dataset provides all needed files (as tables) to fit SDMs: i) the Iberian bird species occurrences at 10km UTM square as a response or dependent variable; ii) the geographic layers of environmental information at 10km UTM square for the Iberian Peninsula as predictors or independent variables, such as climate data, ecosystem functioning attributes (EFAs) and the combined climate and EFA data. The dataset is provided by four **.csv* files named as:

1) The_Iberian_bird_species_occurrences_dataset_10km.csv

2) CHELSA_bioclimate_variables_IP10km.csv

3) MODIS_EVI-based_EFAs_IP10km.csv

4) Combined_bioclimate_EFA_dataset_IP10km.csv

Recommended citation for this dataset: Arenas-Castro, S. et al. (2024), Data from: Effects ...

We monitor and process economic data and financial indicators across 200+ global markets, covering inflation trends, bankruptcy filings, and consensus estimates with 100+ key data points for macroeconomic analysis, risk modeling, and investment strategies.

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Prior to statistical analysis of mass spectrometry (MS) data, quality control (QC) of the identified biomolecule peak intensities is imperative for reducing process-based sources of variation and extreme biological outliers. Without this step, statistical results can be biased. Additionally, liquid chromatography–MS proteomics data present inherent challenges due to large amounts of missing data that require special consideration during statistical analysis. While a number of R packages exist to address these challenges individually, there is no single R package that addresses all of them. We present pmartR, an open-source R package, for QC (filtering and normalization), exploratory data analysis (EDA), visualization, and statistical analysis robust to missing data. Example analysis using proteomics data from a mouse study comparing smoke exposure to control demonstrates the core functionality of the package and highlights the capabilities for handling missing data. In particular, using a combined quantitative and qualitative statistical test, 19 proteins whose statistical significance would have been missed by a quantitative test alone were identified. The pmartR package provides a single software tool for QC, EDA, and statistical comparisons of MS data that is robust to missing data and includes numerous visualization capabilities.

Demographic information of focus group participants.

pone.0295499.t004 - “No sufro, estoy bien/I am not suffering, so I am doing OK”: A mixed method exploration of individual and network-level factors and Type 2 Diabetes Mellitus (T2DM) among Mexican American adults in New York City

The datasets are part of the study titled "A web-based Delphi multi-criteria group decision-making framework for renewable energy project development processes." The study aims to outline and implement the web-based Delphi Multi-criteria Group Decision Making (Delphi-MGDM) Framework, which is intended to facilitate top-level group decision-making for renewable energy project development and long-term strategic direction setting. The datasets include: (1) the weights of criteria obtained from judgments of the experts, (2) the summary of criteria scores, (3) the comparison table dataset, and (4) the full report of the Visual PROMETHEE. “Criteria Weighing Dataset” is obtained from the judgment of experts using the AHP-Online System created by Klaus D. Goepel (available at https://bpmsg.com/ahp/ahp.php). On a pairwise comparison basis, we asked the experts to make their opinion on four (4) criteria and then the sixteen (16) sub-criteria in three rounds. The group weights after the third round are considered the final weights of criteria and sub-criteria. To rank RES using MCDA, we used the data from the literature and the Philippines’ DOE for all ten quantitative sub-criteria. However, there are six qualitative sub-criteria, so we asked the opinion of experts on how solar, wind, biomass, and hydropower are performing in each criterion based on their knowledge and expertise. This time, we used a self-derived questionnaire and as a summary of this process, we produced the “Scoring of Options Dataset.” We got the average, minimum and maximum values of the scores to make data for the ranking in three cases (realistic, pessimistic, and optimistic). "Comparison table" dataset is composed of comparison tables for the three cases. Table A reflects the data for realistic case in which we use the averages of the qualitative inputs from experts, the averages of quantitative data obtained in ranges, and the actual value of data not given in ranges. Table B reflects the data for the optimistic case. For qualitative data, we used the minimum value of the sub-criteria to be minimized and maximum value for sub-criteria to maximized. For quantitative data in ranges, we used the minimum value of cost sub-criteria and maximum value of benefit sub-criteria. We estimated fictitious data for some quantitative data not given in ranges. Table C reflects the data for the pessimistic case. We used the same concept with Table B, but with opposite choices. For instance, we used the maximum value of cost sub-criteria and minimum value of benefit sub-criteria for quantitative data. Finally, we used Visual PROMETHEE (available at http://www.promethee-gaia.net/vpa.html) to rank renewable energy sources. The "Visual PROMETHEE Full Report" dataset is the actual report exported from the Visual PROMETHEE application – containing a partial and complete ranking of RES.