Find research datasets worth reusing

opencc-by-4.0Oct 2024View details →

Data for The Disparities and Development Trajectories of Nations in Achieving the Sustainable Development Goals

This dataset provides the source data for Tables and Figures in the main text and the supplementary information, and the code for the main figure of the article.

opencc-by-4.0Apr 2022View details →

Maps of the Sustainable Development Goal (SDG) indicator 15.3.1 with its sub-indicators for the entire Amazon River Basin

Maps of the SDG indicator 15.3.1 adopted by the United Nations Convention to Combat Desertification (UNCCD) together with its sub-indicators for the Amazon River Basin for the period 2001-2020. The sub-indicators are trajectory (or trend), state, and performance. The SDG indicator 15.3.1 was calculated using the procedures described in the second version of the Good Practice Guidance for SDG Indicator 15.3.1. The annual LCLU maps from the MapBiomas project at 30 m spatial resolution and the 16-day MOD13Q1 NDVI and SoilGrids dataset were used as inputs. In addition, annualized maps of drought severity derived from SPI12, SPEI12, and scPDSI are added.      A total of seven GeoTIFF files in Geographic Tagged Image File Format (GeoTIFF) format are provided at 250 m spatial resolution. Coding for the SDG indicator 15.3.1, trajectory, state, and performance. -32768   is ‘No data’ -1 is ‘Degraded’ 0 is ‘Stable’’ 1 is ‘Improvement’ Coding for the drought severity. From 0 (minimum drought severity) to 1 (maximum drought severity).

opencc-by-4.0Jul 2024View details →

Self-reported data for Sustainable Development from people living in rural and remote areas

Anonymous self-reported data from people living in rural/remote areas as part of a research project on Sustainable Development. The data collection has been approved by the University of Technology Sydney (UTS HREC REF NO. ETH24-9191). The version 1.0 of the dataset includes 212 valid answers to 40 core questions (+ additional info) collected in 2024 in Saudi Arabia.

opencc-by-4.0Aug 2021View details →

DOI's with SDG labels on Target level | 1.4M research articles (2009-2020) related to Sustainable Development Goals

Table content: This data set contains 1.4 million publication DOI's related to the <a href="http://metadata.un.org/sdg/">Targets of the Sustainable Development Goals</a> in the period 2009 - 2020. Table dimensions: rows: 1.4 million, columns: 4 / rows: 1.4 million, columns: 180 Table columns: <a href="https://en.wikipedia.org/wiki/Digital_object_identifier">doi</a> | date | <a href="http://metadata.un.org/sdg/ontology#Target">sdg_target</a> | <a href="http://metadata.un.org/sdg/ontology#Goal">sdg_goal</a> / <a href="https://en.wikipedia.org/wiki/Digital_object_identifier">doi</a> | date | <a href="http://metadata.un.org/sdg/ontology#Target">169 sdg_targets</a> | <a href="http://metadata.un.org/sdg/ontology#Goal">17 sdg_goalsl</a> Table formats:  <a href="https://en.wikipedia.org/wiki/Comma-separated_values">.csv</a> | <a href="https://en.wikipedia.org/wiki/Microsoft_Excel">.xlsx</a> | <a href="https://en.wikipedia.org/wiki/Apache_Parquet">.parquet</a> How we made this data: We have made a search on <a href="https://scopus.com">Scopus </a>using the <a href="https://aurora-network-global.github.io/sdg-queries/">Aurora SDG queries version 5</a> for each of the targets, with a limited year range from 2009 till 2020. Good to know: don't be alarmed if you can find a doi that is labeled with more than one target (~16%). This is not a bug, this is a feature... We used 169 queries, one for each target, a publication can appear in more han one result set. Read this <a href="https://zenodo.org/record/4964606/files/Evaluation_on_accuracy_of_mapping_science_to_the_United_Nations__Sustainable_Development_Goals__SDGs__of_the_Aurora_SDG_queries.pdf?download=1">report to learn more about the accuracy</a> of the queries and the data result sets. How can you use this data: You can use this data to 1. quickly match your existing publication lists to this list to see how that your publications are related to the targets of the SDG's. 2. use these as a basis / seed set / gold set to train more advanced text / graph classifiers (after you have extracted title, abstract or even full-text using crossref.org, unpaywall.org, etc) How can you help: <a href="https://sites.google.com/vu.nl/aurora-sdg-research-dashboard/sdg-knowledge-base#h.d2pd3c39k276">Let us know</a> how you use this data. We'll put your project on the list in our <a href="https://sites.google.com/vu.nl/aurora-sdg-research-dashboard/sdg-knowledge-base">SDG matching knowledge base.</a>

opencc-by-4.0Nov 2022View details →

Sustainable Development Goals (SDG) in citizen science - Dataset

The assignment results of SDGs to CS project descriptions are provided in the following dataset. The analysis was conducted based on data retrieved from the CSTRack database on 2022/09/15.  See further detail about the study in D2.2 section 7.3. Content and grouping:  <ul> <li> The dataset contains the following details: Platform ID (from which platform the CS project descriptions were retrieved),  Project Title (Name of the CS project), SDG assignment results (More details about the assignment technique can be found in D3.2 ‘Web Analytics Toolset and Workbench’ - ESA backend), SDG assignment reported in section 7.2 of D2.2 (which only considered the SDG assignment with the highest similarity). </li> </ul>

opencc-by-4.0Sep 2022View details →

Data for 'Nature's contributions to people and the Sustainable Development Goals in Nepal'

Title: Data for 'Nature's contributions to people and the Sustainable Development Goals in Nepal' Recommended Citation: Adhikari, B., Prescott, G., Urbach, D., Chettri, N., & Fischer, M. (2022). Nature’s contributions to people and the sustainable development goals in Nepal. Environmental Research Letters. https://doi.org/10.1088/1748-9326/ac8e1e Principal Investigator: Markus Fischer (markus.fischer@ips.unibe.ch) Authors: Biraj Adhikari (biraj.adhikari@ips.unibe.ch, ORCID: 0000-0002-4260-8706) Graham W Prescott (graham.prescott.research@gmail.com, ORCID:0000-0001-5123-514X) Davnah Urbach (davnah.payne@ips.unibe.ch, ORCID: 0000-0001-9170-7834) Nakul Chettri (nakul.chettri@icimod.org, ORCID: 0000-0002-3338-8879) Markus Fischer (markus.fischer@ips.unibe.ch, ORCID: 0000-0002-5589-5900) Date of data collection: November 2020 - August 2021 Location of data collection: Kathmandu, Nepal Article title: 'Nature's contributions to people and the Sustainable Development Goals in Nepal' R code available at: https://github.com/biraj-ad/r4dLiteratureReview_GithubRep Data Overview: 1. 'Literature_References.csv' List of 119 peer-reviewed journals and 21 grey literature documents used in the review. Each is assigned a unique identifier ("SN Ref") to link it to the other files. 2. 'Drivers_and_Trends.csv' The "Quote" column is the text from papers which has information on (i) trends in ecosystems or NCPs, and if available (ii) direct and/or indirect drivers causing the trends. The "Nature" column indicates which ecosystem (Forest, Farmland, Freshwater, Grassland, Others, and Directly to NCP), the "NCP" column indicates which NCP (categorized into 18 categories based on the IPBES classification) the text refers to. The "NCP Category" column indicates whether the said NCP is a regulating, material or non-material NCP.    We also categorized direct and indirect drivers based on the IPBES classification (Direct Drivers: Land-use Change, Climate Change, Direct Exploitation, Invasive Alien Species, and Pollution; Indirect Drivers: Institutions and Governance, Demographic and Sociocultural, Economic and Technological).     If there were more than one drivers of change for a particular NCP, we have included them in additional columns. In order to avoid multiple counts for trends of a particular NCP, we introduced the "TrendCount" column. For example, columns 3, 4 and 5 refers to the same trend of decreasing WQN, but has 3 Direct Drivers. Therefore, each TrendCount is given a weight of 0.33 so that the total trend adds upto 1. 3. 'NCP_to_SDG.csv' The "Quote" Column is the text from papers which has information on which NCP is contributing towards which SDG. "Remarks from text" are the authors' own remarks based on the text and the overall context of the article. The contribution of NCPs are classified as positive or negative, and indicated in the column "Effect (pos/neg)" The "NCP" column indicates which NCP (categorized into 18 categories according to IPBES) the text refers to, while the "Contribution to SDG" column indicates which SDG the NCP is contributing towards. The "Ecosystem" column indicates which ecosystem (Forest, Farmland, Freshwater, Grassland, Others) the NCP is being supplied from. Codes for NCPS: HAB (Habitat Creation and Maintenance), POL (Pollination and dispersal of seeds and other propagules), AIR (Regulation of Air Quality), CLI (Regulation of Climate), WQN (Regulation of Freshwater Quantity, Location, and Timing), WQL (Regulation of Freshwater and Coastal Water Quality), SOI (Formation, Protection, and Decontamination of Soils and Sediments), HAZ (Regulation of Hazards and Extreme Events), ORG (Regulation of Organisms Detrimental to Humans), NRG (Energy), FOD (Food and Feed), MAT (Materials and Assistance), MED (Medicinal, Biochemical, and Genetic Resources), INS (Learning and Inspiration), EXP (Physical and Psychological Experiences), IDE (Supporting Identities), and OPT (Maintenance of Options). 4. 'Data_consolidated.xlsx' The above three data files combined into one xlsx document

opencc-by-4.0May 2020View details →

Survey data of "Mapping Research Output to the Sustainable Development Goals (SDGs)"

This dataset contains information on what papers and concepts researchers find relevant to map domain specific research output to the 17 Sustainable Development Goals (SDGs). <a href="https://sustainabledevelopment.un.org/sdgs">Sustainable Development Goals</a> are the 17 global challenges set by the United Nations. Within each of the goals specific targets and indicators are mentioned to monitor the progress of reaching those goals by 2030. In an effort to capture how research is contributing to move the needle on those challenges, we earlier have made an initial classification model than enables to quickly identify what research output is related to what SDG. (This <a href="https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/">Aurora SDG dashboard</a> is the initial outcome as proof of practice.) In order to validate our current classification model (on soundness/precision and completeness/recall), and receive input for improvement, a survey has been conducted to capture expert knowledge from senior researchers in their research domain related to the SDG. The survey was open to the world, but mainly distributed to researchers from the <a href="https://aurora-network.global/">Aurora Universities Network</a>. The survey was open from October 2019 till January 2020, and captured data from 244 respondents in Europe and North America. 17 surveys were created from a single template, where the content was made specific for each SDG. Content, like a random set of publications, of each survey was ingested by a data provisioning server. That collected research output metadata for each SDG in an earlier stage. It took on average 1 hour for a respondent to complete the survey. The outcome of the survey data can be used for validating current and optimizing future SDG classification models for mapping research output to the SDGs. The survey contains the following questions (see inside dataset for exact wording): <ul> <li>Are you familiar with this SDG? <ul> <li>Respondents could only proceed if they were familiar with the targets and indicators of this SDG. Goal of this question was to weed out un knowledgeable respondents and to increase the quality of the survey data.</li> </ul> </li> <li>Suggest research papers that are relevant for this SDG (upload list) <ul> <li>This question, to provide a list, was put first to reduce influenced by the other questions. Goal of this question was to measure the completeness/recall of the papers in the result set of our current classification model. (To lower the bar, these lists could be provided by either uploading a file from a reference manager (preferred) in .ris of bibtex format, or by a list of titles. This heterogenous input was processed further on by hand into a uniform format.)</li> </ul> </li> <li>Select research papers that are relevant for this SDG (radio buttons: accept, reject) <ul> <li>A randomly selected set of 100 papers was injected in the survey, out of the full list of thousands of papers in the result set of our current classification model. Goal of this question was to measure the soundness/precision of our current classification model.</li> </ul> </li> <li>Select and Suggest Keywords related to SDG (checkboxes: accept | text field: suggestions) <ul> <li>The survey was injected with the top 100 most frequent keywords that appeared in the metadata of the papers in the result set of the current classification model. respondents could select relevant keywords we found, and add ones in a blank text field. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.</li> </ul> </li> <li>Suggest SDG related glossaries with relevant keywords (text fields: url) <ul> <li>Open text field to add URL to lists with hundreds of relevant keywords related to this SDG. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.</li> </ul> </li> <li>Select and Suggest Journals fully related to SDG (checkboxes: accept | text field: suggestions) <ul> <li>The survey was injected with the top 100 most frequent journals that appeared in the metadata of the papers in the result set of the current classification model. Respondents could select relevant journals we found, and add ones in a blank text field. Goal of this question was to get suggestions for complete journals we can use to increase the recall of relevant papers in a new classification model.</li> </ul> </li> <li>Suggest improvements for the current queries (text field: suggestions per target) <ul> <li>We showed respondents the queries we used in our current classification model next to each of the targets within the goal. Open text fields were presented to change, add, re-order, delete something (keywords, boolean operators, etc. ) in the query to improve it in their opinion. Goal of this question was to get suggestions we can use to increase the recall and precision of relevant papers in a new classification model.</li> </ul> </li> </ul> In the dataset root you'll find the following folders and files: <ul> <li>/00-survey-input/ <ul> <li>This contains the survey questions for all the individual SDGs. It also contains lists of EIDs categorised to the SDGs we used to make randomized selections from to present to the respondents.</li> </ul> </li> <li>/01-raw-data/ <ul> <li>This contains the raw survey output. (Excluding privacy sensitive information for public release.) This data needs to be combined with the data on the provisioning server to make sense.</li> </ul> </li> <li>/02-aggregated-data/ <ul> <li>This data is where individual responses are aggregated. Also the survey data is combined with the provisioning server, of all sdg surveys combined, responses are aggregated, and split per question type.</li> </ul> </li> <li>/03-scripts/ <ul> <li>This contains scripts to split data, and to add descriptive metadata for text analysis in a later stage.</li> </ul> </li> <li>/04-processed-data/ <ul> <li>This is the main final result that can be used for further analysis. Data is split by SDG into subdirectories, in there you'll find files per question type containing the aggregated data of the respondents.</li> </ul> </li> <li>/images/ <ul> <li>images of the results used in this README.md.</li> </ul> </li> <li>LICENSE.md <ul> <li>terms and conditions for reusing this data.</li> </ul> </li> <li>README.md <ul> <li>description of the dataset; each subfolders contains a README.md file to futher describe the content of each sub-folder.</li> </ul> </li> </ul> In the /04-processed-data/ you'll find in each SDG sub-folder the following files.: <ul> <li>SDG-survey-questions.pdf <ul> <li>This file contains the survey questions</li> </ul> </li> <li>SDG-survey-questions.doc <ul> <li>This file contains the survey questions</li> </ul> </li> <li>SDG-survey-respondents-per-sdg.csv <ul> <li>Basic information about the survey and responses</li> </ul> </li> <li>SDG-survey-city-heatmap.csv <ul> <li>Origin of the respondents per SDG survey</li> </ul> </li> <li>SDG-survey-suggested-publications.txt <ul> <li>Formatted list of research papers researchers have uploaded or listed they want to see back in the result-set for this SDG.</li> </ul> </li> <li>SDG-survey-suggested-publications-with-eid-match.csv <ul> <li>same as above, only matched with an EID. EIDs are matched my Elsevier's internal fuzzy matching algorithm. Only papers with high confidence are show with a match of an EID, referring to a record in Scopus.</li> </ul> </li> <li>SDG-survey-selected-publications-accepted.csv <ul> <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe represent this SDG. (TRUE=accepted)</li> </ul> </li> <li>SDG-survey-selected-publications-rejected.csv <ul> <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe not to represent this SDG. (FALSE=rejected)</li> </ul> </li> <li>SDG-survey-selected-keywords.csv <ul> <li>Based on our previous result set of papers, we presented researchers the keywords that are in the metadata of those papers, they selected keywords they believe represent this SDG.</li> </ul> </li> <li>SDG-survey-unselected-keywords.csv <ul> <li>As "selected-keywords", this is the list of keywords that respondents have not selected to represent this SDG.</li> </ul> </li> <li>SDG-survey-suggested-keywords.csv <ul> <li>List of keywords researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li>SDG-survey-glossaries.csv <ul> <li>List of glossaries, containing keywords, researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li>SDG-survey-selected-journals.csv <ul> <li>Based on our previous result set of papers, we presented researchers the journals that are in the metadata of those papers, they selected journals they believe represent this SDG.</li> </ul> </li> <li>SDG-survey-unselected-journals.csv <ul> <li>As "selected-journals", this is the list of journals that respondents have not selected to represent this SDG.</li> </ul> </li> <li>SDG-survey-suggested-journals.csv <ul> <li>List of journals researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li>SDG-survey-suggested-query.csv <ul> <li>List of query improvements researchers suggest to use to find papers related to this SDG</li> </ul> </li> </ul> Cite as: <blockquote> Survey data of "Mapping Research output to the SDGs" by Aurora Universities Network (AUR) <a href="http://doi.org/10.5281/zenodo.3798385">doi:10.5281/zenodo.3798385</a> </blockquote> Attribute as: <blockquote> Survey data of "Mapping Research output to the SDGs" by Aurora Universities Network (AUR); Alessandro Arienzo (UNA); Roberto Delle Donne (UNA); Ignasi Salvadó Estivill (URV); José Luis González Ugarte (URV); Didier Vercueil (UGA); Nykohla Strong (UAB); Eike Spielberg (UDE); Felix Schmidt (UDE); Linda Hasse (UDE); Ane Sesma (UEA); Baldvin Zarioh (UIC); Friedrich Gaigg (UIN); René Otten (VUA); Nicolien van der Grijp (VUA); Yasin Gunes (VUA); Peter van den Besselaar (VUA); Joeri Both (VUA); Maurice Vanderfeesten (VUA); is licensed under a Creative Commons Attribution 4.0 International License. <a href="https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/">https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/</a> </blockquote>

opencc-by-4.0Oct 2024View details →

Data - Sustainable Development Key to Limiting Climate Change-Driven Wildfire Damages

This repository contains the data and scripts required to reproduce the results of the manuscript "Sustainable Development Key to Limiting Climate Change-Driven Wildfire Damages" submitted to the Environmental Research Climate Journal (ERCL).  Brief description of project This project has two main goals: <ol> <li>Examine the key factors influencing global economic wildfire damages </li> <li>Projecting future damages under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP370)</li> </ol> Repository structure <ul> <li>/data directory: contains the data to reproduce the regression analyses and plot the figures presented in the manuscript <ul> <li>/data/historical: contains the historical (training) data that was used for fitting the linear regression model </li> <li>/data/ssp: contains the SSP projection data for all predictors, as well as the projected model output for future wildfire damages</li> <li>/data/source: contains all raw data used in this study</li> </ul> </li> <li>/scripts directory: contains the python scripts to run the regression model and to plot the figures presented in the manuscript <ul> <li>/scripts/linregress: contains the scripts for running the linear regression model and to conduct various model validation steps <ul> <li>run_linregress.py: script to run the linear regression model </li> <li>run_nonlinregress.py: script to run the nonlinear models (preliminary)</li> <li>run_plm.py: script to run the supplementary panel regression model (plm)</li> <li>run_gdp_linregress.py: script to run the alternative linear regression model using absolute damages as outcome variable and GDP as additional independent predictor</li> <li>inspect_model.py: script to conduct model validation</li> </ul> </li> <li>/scripts/plotting: contains the scripts to plot all figures presented in the manuscript <ul> <li>plot_map_y_X_hist.py: script to plot Figure 1 (world maps of historical wildfire damage and predictors used in this study)</li> <li>plot_residual_plots.py: script to plot Figure 2 (residual and partial residual plots of the fitted regression model)</li> <li>plot_beta_coef_model_prediction.py: script to plot Figure 3 (standardized beta coefficients of the fitted regression model and the scatterplots for reported vs. model-estimated wildfire damages)</li> <li>plot_predictor_ssp_timeseries_global.py: script to plot Figure 4 (time-series of the SSP projections of the predictors)</li> <li>plot_map_X_ssp.py: script to plot Figure 5 (world maps of predictor values for the three SSPs explored in this study)</li> <li>plot_ssp_damage_projection_by_region.py: script to plot Figure 6 (projected wildfire damages under the three SSPs and for the six IPCC AR6 regions)</li> <li>plot_ssp_damage_projection_per_predictor.py: script to plot Figure 7 (time-series of global mean projected wildfire damage with all predictors changing and only individual predictors changing)</li> <li>plot_ssp3_ssp1_difference.py: script to plot Figure 8 (time-series of mean avoided wildfire damage in SSP126 compared to SSP370)</li> <li>SI_plot_ssp_damage_projection_lin_vs_nonlin.py: script to plot Figure S1 (comparison of time-series of mean projected wildfire damage with the linear and nonlinear models)</li> <li>SI_plot_ssp_damage_projection_xterm.py: script to plot Figure S2 (comparison of time-series of mean projected wildfire damages using models with and without interaction terms)</li> <li>SI_plot_beta_coef_pop_wui.py: script to plot Figure S3 (same as Figure 3 but for the model using pop_wui instead of PDforest)</li> <li>SI_plot_ssp_population.py: script to plot Figure S4 (population projection under the three SSP scenarios)</li> <li>SI_plot_ssp_map_pop_wui.py: script to plot Figure S5 (world maps of the pop_wui predictor under three SSP scenarios)</li> <li>SI_plot_ssp_map_damage.py: script to plot Figure S6 (world maps of projected wildfire damages under the three SSP scenarios and for the years 2030, 2050 and 2070)</li> <li>SI_plot_ssp_damage_projection_pop_wui.py: script to plot Figure S7 (comparison of the time-series of projected wildfire damage using pop_wui vs PDforest as predictor)</li> <li>SI_plot_predictor_ssp_trend_by_dev_region.py: script to plot Figure S8 (time-series of the SSP projections of the predictors by developmental regions)</li> </ul> </li> </ul> </li> </ul>

opencc-by-4.0Apr 2024View details →

Supporting Material: Scientific Literature on the Sustainable Development Goals (SDGs). Scopus - May 2022

This is a supplementary dataset for an article analysing the scientific literature related to the Sustainable Development Goals (SDGs) using Scopus-indexed journals.  Data were retrieved in May 2022. Scopus was searched in the Title, Abstract, and Keywords fields  looking for each of the 17 SDGs (search query example: TITLE-ABS-KEY (“SDG1” or "SDG 1"). The dataset includes the following information for each of the 4808 scientific publication: ID: an identificatory alphanumerical number given by the authors Primary SDG: the main SDG the document focus on (MULTIPLE in case of more than one, ALL in case of all the SDGs) Year: Year of publication Title: title of the publication Abstract: abstract of the publication Index keywords: keywords of the publication

Figure 1 in Development and objectives of the PHYCOMORPH European Guidelines for the Sustainable Aquaculture of Seaweeds (PEGASUS)

Figure 1: Seaweed aquaculture to meet the goals of the European bioeconomy strategy (© Michele Barbier, based on EC documentation, 2018, source photos: iStock, © roxyminder #94394792; Fotolia_110024322_Subscription_XXL_© Countrypixel.jpg).

Figure 3 in Development and objectives of the PHYCOMORPH European Guidelines for the Sustainable Aquaculture of Seaweeds (PEGASUS)

Figure 3: Different European legislation with implications for seaweed aquaculture (© Michele Barbier).

Figure 2 in Development and objectives of the PHYCOMORPH European Guidelines for the Sustainable Aquaculture of Seaweeds (PEGASUS)

Figure 2: The development of sustainable seaweed aquaculture in Europe faces a number of challenges: market size, potential environmental impact, and preservation of local genetic diversity, the need to intensify research – both fundamental and applied, regulation of food quality, heavy metals or alien species, and cultivation constraints ranging from automation to issues of epiphytism (© Michele Barbier).

Figure 4 in Development and objectives of the PHYCOMORPH European Guidelines for the Sustainable Aquaculture of Seaweeds (PEGASUS)

Figure 4: Actions promoting the preservation of European marine biodiversity (© Michele Barbier, source photo © freepick.com).

opencc-by-4.0Nov 2021View details →

Toward consumer-centric sustainability development model: A reverse logistics multi-criteria decision-making analysis datasets

Toward consumer-centric sustainability development model: A reverse logistics multi-criteria decision-making analysis datasets

opencc-by-4.0Jun 2022View details →

A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case Study of SDG 13: Climate Action

This data set pertains to the following research article: Purnell, P.J. (2022) A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case Study of SDG 13 – Climate Action. arXiv:2201.02006

opencc-zeroJun 2024View details →

Dataset: iShares MSCI Global Sustainable Development Goals ETF (SDG) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

opencc-by-4.0Dec 2002View details →

Fig. 11. A–L in Urban and Peri-urban small and medium-size Enterprise Development for sustainable Vegetable Production and Marketing Systems

Fig. 11. A–L. Mamatia retracta (Popov). A. Dorsal valve RM Br133828, exterior, × 40. B. Dorsal valve RM Br133829, interior, × 50. C. Ventral valve RM Br133830, exterior, × 32. D. Dorsal valve RM Br133831, interior, × 27. E, H, I, K. Ventral valve RM Br133832, exterior (E, × 75), oblique posterior view (H, × 40), oblique lateral view (I, × 75), detail of larval shell (K, × 162). F. Ventral valve RM Br133833, oblique lateral view, 62. G, J. Ventral valve RM Br133834, interior (G, × 45) and detail of apical process (J, × 195). L. Ventral valve RM Br133835, detail of larval shell, × 150. All specimens from the Tremadoc chalcedonites, Wysoczki.

opencc-by-4.0Dec 2002View details →

Fig. 6. A–N in Urban and Peri-urban small and medium-size Enterprise Development for sustainable Vegetable Production and Marketing Systems

Fig. 6. A–N. Siphonotretella popovi sp. nov. A, N. Dorsal valve RM Br133791, exterior (A, × 26), detail of spines (N, × 100). B. Dorsal valve RM Br133792, exterior, × 32. C. Holotype, ventral valve RM Br133793, exterior, × 26. D, G, L. Dorsal valve RM Br133794, oblique posterior view (D, × 30), exterior (G, × 30), detail of larval shell (L, × 80). E, J. Dorsal valve RM Br133795, exterior (E, × 40) and detail of larval shell (J, × 120). F, H, I, K. Ventral valve RM Br133796, oblique lateral view (F, × 26), oblique posterior view (H, × 32), detail of larval shell and pedicle opening (I, × 80), detail of larval shell and pedicle opening (K, × 90). M. Dorsal valve RM Br133797, interior, × 40. O. Ventral valve RM Br133798, interior, × 23. All specimens from the Tremadoc chalcedonites, Wysoczki.