The Shared Socioeconomic Pathways (SSPs) Literature Database, v1, 2014-2019 consists of biographic information, abstracts, and analysis of 1,360 articles published from 2014 to 2019 that used the SSPs. The database was generated from a Google Scholar search, followed by a manual examination of the results for papers that made substantial use of the SSPs. Each paper was then coded along a number of different dimensions, including categories of types of papers or analysis, number of subcategories for SSP Applications and SSP Extensions, particular Shared Socioeconomic Pathways (SSPs) used, particular Representative Concentration Pathways (RCPs) used, and particular SSP-RCP combinations used. Over the past ten years, the climate change research commUnity developed a scenario framework combining alternative futures of climate and society to facilitate integrated research and consistent assessment to inform policy. This framework consists of Shared Socioeconomic Pathways (SSPs), Representative Concentration Pathways (RCPs), and Shared Policy Assumptions (SPAs), which together describe alternative visions of how society and climate may evolve over the coming decades, while providing a framework for combining these pathways in integrated studies. The tracking of the use of this framework in the literature allows for assessment of how it is being used, whether it is achieving its original goals, and what improvements to the framework would benefit future research.

The Sub-global Scenarios that Extend the Global SSP Narratives: Literature Database, Version 1, 2014-2021 consists of 37 columns of bibliographic data, methodological and analytical insights, from 155 articles published from 2014 to 2021 that extended the narratives of global SSPs. Local and regional scale Shared Socioeconomic Pathways (SSPs) have grown largely in addressing Climate Change Impact, Adaptation, and Vulnerability (CCIAV) assessments at sub-global levels. Common elements of these studies, besides their focus on CCIAV, are the use of both quantitative and qualitative elements of the SSPs. To explore and learn from current literature on novel methods and insights on extending SSPs, the sub-global extended SSPs literature database is constructed in the research for analyses. The database was developed in four stages: searches; screening; data extraction; and coding. The search stage incorporated three approaches: using a search string in three academic databases (Scopus, Web of Science Core Collection, ScienceDirect); a targeted search of a specific relevant database (ICONICS); and a targeted selection in Google Scholar of all papers that cited the publication of the global SSP narratives. In the screening step, criteria were assessed for full-text papers for eligibility including relevant typologies, methodologies, and other criteria. Finally, data from eligible papers was extracted and entered in a coding framework in an Excel workbook spreadsheet. The coding framework resulted in 37 columns to systematize coding of data from the 155 papers selected along several different dimensions, including categories of papers or analysis, several subcategories for SSP Applications and SSP Extensions, specific SSPs used, specific Representative Concentration Pathways (RCPs) used, typologies of extensions of qualitative and quantitative SSPs, and the types of models and nature of the extended SSPs.

The Sub-global Scenarios that Extend the Global SSP Narratives: Literature Database, Version 1, 2014-2021 consists of 37 columns of bibliographic data, methodological and analytical insights, from 155 articles published from 2014 to 2021 that extended the narratives of global SSPs. Local and regional scale Shared Socioeconomic Pathways (SSPs) have grown largely in addressing Climate Change Impact, Adaptation, and Vulnerability (CCIAV) assessments at sub-global levels. Common elements of these studies, besides their focus on CCIAV, are the use of both quantitative and qualitative elements of the SSPs. To explore and learn from current literature on novel methods and insights on extending SSPs, the sub-global extended SSPs literature database is constructed in the research for analyses. The database was developed in four stages: searches; screening; data extraction; and coding. The search stage incorporated three approaches: using a search string in three academic databases (Scopus, Web of Science Core Collection, ScienceDirect); a targeted search of a specific relevant database (ICONICS); and a targeted selection in Google Scholar of all papers that cited the publication of the global SSP narratives. In the screening step, criteria were assessed for full-text papers for eligibility including relevant typologies, methodologies, and other criteria. Finally, data from eligible papers was extracted and entered in a coding framework in an Excel workbook spreadsheet. The coding framework resulted in 37 columns to systematize coding of data from the 155 papers selected along several different dimensions, including categories of papers or analysis, several subcategories for SSP Applications and SSP Extensions, specific SSPs used, specific Representative Concentration Pathways (RCPs) used, typologies of extensions of qualitative and quantitative SSPs, and the types of models and nature of the extended SSPs. To provide a literature database tracking the use of the sub-global scenarios consisting of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) that extend the global SSP narratives and framework for climate, socioeconomic, environmental, and other related research.

The Sub-global Scenarios that Extend the Global SSP Narratives: Literature Database, Version 1, 2014-2021 consists of 37 columns of bibliographic data, methodological and analytical insights, from 155 articles published from 2014 to 2021 that extended the narratives of global SSPs. Local and regional scale Shared Socioeconomic Pathways (SSPs) have grown largely in addressing Climate Change Impact, Adaptation, and Vulnerability (CCIAV) assessments at sub-global levels. Common elements of these studies, besides their focus on CCIAV, are the use of both quantitative and qualitative elements of the SSPs. To explore and learn from current literature on novel methods and insights on extending SSPs, the sub-global extended SSPs literature database is constructed in the research for analyses. The database was developed in four stages: searches; screening; data extraction; and coding. The search stage incorporated three approaches: using a search string in three academic databases (Scopus, Web of Science Core Collection, ScienceDirect); a targeted search of a specific relevant database (ICONICS); and a targeted selection in Google Scholar of all papers that cited the publication of the global SSP narratives. In the screening step, criteria were assessed for full-text papers for eligibility including relevant typologies, methodologies, and other criteria. Finally, data from eligible papers was extracted and entered in a coding framework in an Excel workbook spreadsheet. The coding framework resulted in 37 columns to systematize coding of data from the 155 papers selected along several different dimensions, including categories of papers or analysis, several subcategories for SSP Applications and SSP Extensions, specific SSPs used, specific Representative Concentration Pathways (RCPs) used, typologies of extensions of qualitative and quantitative SSPs, and the types of models and nature of the extended SSPs.

General Information

The Pop-AUT database was developed for the DISCC-AT project, which required subnational population projections for Austria consistent with the updated Shared Socio-Economic Pathways (SSPs). For this database, the most recent version of the nationwide SSP population projections (IIASA-WiC POP 2023) are spatially downscaled, offering a detailed perspective at the subnational level in Austria. Recognizing the relevance of this information for a wider audience, the data has been made publicly accessible through an interactive dashboard. There, users are invited to explore how the Austrian population is projected to evolve under different SSP scenarios until the end of this century.

Methodology

The downscaling process of the nationwide Shared Socioeconomic Pathways (SSP) population projections is a four-step procedure developed to obtain subnational demographic projections for Austria. In the first step, population potential surfaces for Austria are derived. These indicate the attractiveness of a location in terms of habitability and are obtained using machine learning techniques, specifically random forest models, along with geospatial information such as land use, roads, elevation, distance to cities, and elevation (see, e.g., Wang et al. 2023).

The population potential surfaces play a crucial role in distributing the Austrian population effectively across the country. Calculations are based on the 1×1 km spatial resolution database provided by Wang et al. (2023), covering all SSPs in 5-year intervals from 2020 to 2100.

Moving to the second step, the updated nationwide SSP population projections for Austria (IIASA-WiC POP 2023) are distributed to all 1×1 km grid cells within the country. This distribution is guided by the previously computed grid cell-level population potential surfaces, ensuring a more granular representation of demographic trends.

The base year for all scenarios is 2015, obtained by downscaling the UN World Population Prospects 2015 count for Austria using the WorldPop (2015) 1×1 km population count raster.

In the third step, the 1×1 km population projections are temporally interpolated to obtain yearly projections for all SSP scenarios spanning the period from 2015 to 2100.

The final step involves the spatial aggregation of the gridded SSP-consistent population projections to the administrative levels of provinces (Bundesländer), districts (Bezirke), and municipalities (Gemeinden).

Dashboard

The data can be explored interactively through a dashboard.

Data Inputs

Updated nationwide SSP population projections: IIASA-WiC POP (2023) (https://zenodo.org/records/7921989)

Population potential surfaces: Wang, X., Meng, X., & Long, Y. (2022). Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways. Scientific Data, 9(1), 563.

Shapefiles: data.gv.at

WorldPop 2015: WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00647

Version

This is version 1.0, built upon the Review-Phase 2 version of the updated nationwide SSP population projections (IIASA-WiC POP 2023). Once these projections are revised, this dataset will be accordingly updated.

File Organization

The SSP-consistent population projections for Austria are accessible in two formats: .csv files for administrative units (provinces = Bundesländer, districts = Politische Bezirke, municipalities = Gemeinden) and 1×1 km raster files in GeoTIFF and NetCDF formats. All files encompass annual population counts spanning from 2015 to 2100.

The Global One-Eighth Degree Population Base Year and Projection Grids Based on the Shared Socioeconomic Pathways, Revision 01, data set consists of global urban, rural, and total population data for the base year 2000, and population projections at ten-year intervals for 2010-2100 at a resolution of one-eighth degree (7.5 arc-minutes), consistent both quantitatively and qualitatively with the SSPs. Spatial demographic data are key inputs for the analysis of land use, energy use, and emissions, as well as for the assessment of climate change vulnerability, impacts, and adaptation. The SSPs are developed to support future climate and global change research and the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

The Global One-Eighth Degree Urban Land Extent Projection and Base Year Grids by SSP Scenarios, 2000-2100 consists of global SSP-consistent spatial urban land fraction data for the base year 2000 and projections at ten-year intervals for 2010-2100 at a resolution of one-eighth degree (7.5 arc-minutes). Spatial urban land projections are key inputs for the analysis of land use, energy use, and emissions, as well as for the assessment of climate change vulnerability, impacts and adaptation. This data set presents a set of global, spatially explicit urban land scenarios that are consistent with the Shared Socioeconomic Pathways (SSPs) to produce an empirically-grounded set of urban land spatial distributions over the 21st century. A data-science approach is used exploiting 15 diverse data sets, including a newly available 40-year global time series of fine-spatial-resolution remote sensing observations from the Landsat satellite series. The SSPs are developed to support future climate and global change research, the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), along with Special Reports.

The OECD Programme for International Student Assessment (PISA) surveys collected data on students’ performances in reading, mathematics and science, as well as contextual information on students’ background, home characteristics and school factors which could influence performance. This publication includes detailed information on how to analyse the PISA data, enabling researchers to both reproduce the initial results and to undertake further analyses. In addition to the inclusion of the necessary techniques, the manual also includes a detailed account of the PISA 2006 database and worked examples providing full syntax in SPSS.

We use historical data for the land-based passenger (in passenger-kilometers (km)) across 38 countries and freight transport (in tonne-km) for 43 countries between 1990 and 2018 from the Transport Outlook of the International Transport Forum (ITF) transport database, to investigate the key drivers of transport energy demand source: ITF. (2019). ITF Transport Outlook 2019. ITF Transport Outlook 2019. https://www.oecd-ilibrary.org/transport/itf-transport-outlook-2019_transp_outlook-en-2019-en

We collect the historical socioeconomic variables from the World Bank’s global open data bank source: World Bank. (2020). Data Bank: World Development Indicators. https://databank.worldbank.org/source/world-development-indicators

For this scenario analysis, we rely on the shared socioeconomic pathways (SSPs) from the IIASA database (Riahi et al., 2017). source: Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., … Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, 153–168. https://doi.org/10.1016/j.gloenvcha.2016.05.009 Available Online: https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about

The lack of data disaggregated by country and end-use sector in countries of interest was a significant drawback in the data collection process. We make a crucial assumption in this modeling exercise that historical demand profiles in developing countries track the global average per capita transport trends. Therefore, the resulting estimates are indicative and must be interpreted within this analysis's scope given the future is unknown and highly uncertain.

The Global 1-km Downscaled Population Base Year and Projection Grids Based on the Shared Socioeconomic Pathways, Revision 01, data set consists of global urban, rural, and total populaton for the base year 2000, and population projections at ten-year intervals for 2010-2100 at a resolution of 1-km (about 30 arc-seconds), consistent both quantitatively and qualitatively with the SSPs. This 1-km data set is a downscaled version of the one-eighth degree (7.5 arc-minutes) data published in Jones and O'Neill (2016). The downscaling methods were published in Gao (2017). Spatial demographic data are key inputs for the analysis of land use, energy use, and emissions, as well as for the assessment of climate change vulnerability, impacts, and adaptation. The SSPs are developed to support future climate and global change research and the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

What does the data show?

Life expectancy at birth (years) from the UK Climate Resilience Programme UK-SSPs project. The data is available for each Office for National Statistics Local Authority District (ONS LAD) shape simplified to a 10m resolution.

The data is available for the end of each decade. This dataset contains SSP1, SSP2, SSP3, SSP4 and SSP5. For more information see the table below.

Indicator

Health

Metric

Life expectancy at birth

Unit

Years

Spatial Resolution

LAD

Temporal Resolution

Decadal

Sectoral Categories

N/A

Baseline Data Source

ONS 2018

Projection Trend Source

Stakeholder process

What are the naming conventions and how do I explore the data?

This data contains a field for the year at the end of each decade. A separate field for 'Scenario' allows the data to be filtered, e.g. by scenario 'SSP3'.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to 2020 values.

What are Shared Socioeconomic Pathways (SSPs)?

The global SSPs, used in Intergovernmental Panel on Climate Change (IPCC) assessments, are five different storylines of future socioeconomic circumstances, explaining how the global economy and society might evolve over the next 80 years. Crucially, the global SSPs are independent of climate change and climate change policy, i.e. they do not consider the potential impact climate change has on societal and economic choices.

Instead, they are designed to be coupled with a set of future climate scenarios, the Representative Concentration Pathways or ‘RCPs’. When combined together within climate research (in any number of ways), the SSPs and RCPs can tell us how feasible it would be to achieve different levels of climate change mitigation, and what challenges to climate change mitigation and adaptation might exist.

Until recently, UK-specific versions of the global SSPs were not available to combine with the RCP-based climate projections. The aim of the UK-SSPs project was to fill this gap by developing a set of socioeconomic scenarios for the UK that is consistent with the global SSPs used by the IPCC community, and which will provide the basis for further UK research on climate risk and resilience.

Useful links: Further information on the UK SSPs can be found on the UK SSP project site and in this storymap.Further information on RCP scenarios, SSPs and understanding climate data within the Met Office Climate Data Portal.

Estimated GDPs by 1/12-degree grids during 1850—2100 by 10 year intervals. In the estimation, national GDP data (past data until 2010; future projection under SSPs after 2020) is downscaled considering spatial and economic interactions among cities, urban growth patterns compatible with SSPs, and other auxiliary geographic data (land cover, road network, etc.). For the estimation methods, see Murakami et al. (in prep)Murakami, D., Yoshida, Y., Yamagata, Y. (in prep) Gridded GDP projections compatible with the five SSPs (Shared Socioeconomic Pathways).

The dataset provides annual water withdrawal and consumption estimates for Europe at a spatial resolution of 5 arcminutes, covering the period from 2020 to 2100 for four SSPs (1, 2, 3, and 5). Below, we outline the procedure used to downscale the population projections to a 5-arcminute resolution and describe the main equations applied to project water withdrawal and consumption under different SSPs.

The development of the high-resolution (5 arcminute) projected water withdrawal and consumption for Europe follows the methodology outlined by Wada et al. (2011a, 2011b). This new release incorporates new projections for population, GDP per capita, and urbanization patterns from the latest SSP database (v3.0.1; available at https://data.ece.iiasa.ac.at/ssp/). Since this update is still in progress as of August 25^th, 2024, some necessary input data are sourced from an earlier version of the SSP data (SSP 2013, see Table 1). All data and methods used to generate the results provided in this dataset are described in the Readme - Data and Methods file.

Table 1: Data availability in different versions of the SSP database as of August 25^th 2024.

We updated the global urbanization level from 2015 to 2100 under the SSPs. We provide two projections generated from two data sources (i.e., World Urbanization Prospects (WUP, the 2018 revision) and World Bank (WB)) with time steps of 5 years and 1 year, respectively. The updated dataset of urbanization level has a potential to be widely applied to the study of future socioeconomic development and climate change. The data projected based on the WUP (2018 revision) database are stored under the 'WUP2018' folder, and the data predicted based on the WB database are stored under the 'WB' folder. The urbanization level and uncertainty for each country and area are stored in ‘.xls’ files named starting with different 'SSPs', and the files ending with ‘SD’ represent the standard deviation of the projections. In 'WUP2018' folder and 'WB' folder, we also provided files named ‘the 100% urbanization level countries and areas’ contains countries and areas in which urbanization levels were 100% in 2010 or 2009 and assumed their urbanization levels will stay in 100% in the future.

What does the data show?

The data shows projections of population age structure (thousands of people per age class) from the UK Climate Resilience Programme UK-SSPs project. The data is available for each Office for National Statistics Local Authority District (ONS LAD) shape simplified to a 10m resolution.

The age structure is split into 19 age classes e.g. 10-14 and is available for the end of each decade. For more information see the table below.

This dataset contains only SSP2, the 'Middle of the Road' scenario.

Indicator

Demography

Metric

Age Structure

Unit

Thousands per age class

Spatial Resolution

LAD

Temporal Resolution

Decadal

Sectoral Categories

19 age classes

Baseline Data Source

ONS 2019

Projection Trend Source

IIASA

What are the naming conventions and how do I explore the data?

This data contains a field for the year at the end of each decade. A separate field for 'Age Class' allow the data to be filtered e.g. by age class '10-14'.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to 2020 values.

What are Shared Socioeconomic Pathways (SSPs)?

The global SSPs, used in Intergovernmental Panel on Climate Change (IPCC) assessments, are five different storylines of future socioeconomic circumstances, explaining how the global economy and society might evolve over the next 80 years. Crucially, the global SSPs are independent of climate change and climate change policy, i.e. they do not consider the potential impact climate change has on societal and economic choices.

Instead, they are designed to be coupled with a set of future climate scenarios, the Representative Concentration Pathways or ‘RCPs’. When combined together within climate research (in any number of ways), the SSPs and RCPs can tell us how feasible it would be to achieve different levels of climate change mitigation, and what challenges to climate change mitigation and adaptation might exist.

Until recently, UK-specific versions of the global SSPs were not available to combine with the RCP-based climate projections. The aim of the UK-SSPs project was to fill this gap by developing a set of socioeconomic scenarios for the UK that is consistent with the global SSPs used by the IPCC community, and which will provide the basis for further UK research on climate risk and resilience.

Useful links:

Further information on the UK SSPs can be found on the UK SSP project site and in this storymap. Further information on RCP scenarios, SSPs and understanding climate data within the Met Office Climate Data Portal.

The Global Population Projection Grids Based on Shared Socioeconomic Pathways (SSPs), Downscaled 1-km Grids, 2010-2100 consists of global spatial population projections at a resolution of 1-km (about 30 arc-seconds) for urban, rural, and total population, and at ten-year intervals for 2010-2100. The projections are consistent both quantitatively and qualitatively with the Shared Socioeconomic Pathways (SSPs). This data set is a downscaled version of the Global Population Projection Grids Based on SSPs, v1 (2010-2100), published in Jones and O'Neill (2016). The downscaling methods were published in Gao (2017). Spatial demographic projections are key inputs for the analysis of land use, energy use, and emissions, as well as for the assessment of climate change vulnerability, impacts, and adaptation. The SSPs are developed to support future climate and global change research and the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). This data set is distributed in GeoTIFF and netCDF formats.

The dataset was generated using a statistical fixed-effects model calibrated on historical data. It includes annual projections under six distinct SSP-RCP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, and SSP5-8.5). For each scenario, the dataset provides future trajectories for key national agricultural management inputs—including nitrogen application rates, irrigation extents, and mechanization levels—and the resulting projected cropping shares.

This layer shares SEDAC's population projections for U.S. counties for 2020-2100 in increments of 5 years, for each of five population projection scenarios known as Shared Socioeconomic Pathways (SSPs). This layer supports mapping, data visualizations, analysis and data exports. Before using this layer, read: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview by Keywan Riahi, Detlef P. van Vuuren, Elmar Kriegler, Jae Edmonds, Brian C. O’Neill, Shinichiro Fujimori, Nico Bauer, Katherine Calvin, Rob Dellink, Oliver Fricko, Wolfgang Lutz, Alexander Popp, Jesus Crespo Cuaresma, Samir KC, Marian Leimbach, Leiwen Jiang, Tom Kram, Shilpa Rao, Johannes Emmerling, Kristie Ebi, Tomoko Hasegawa, Petr Havlik, Florian Humpenöder, Lara Aleluia Da Silva, Steve Smith, Elke Stehfest, Valentina Bosetti, Jiyong Eom, David Gernaat, Toshihiko Masui, Joeri Rogelj, Jessica Strefler, Laurent Drouet, Volker Krey, Gunnar Luderer, Mathijs Harmsen, Kiyoshi Takahashi, Lavinia Baumstark, Jonathan C. Doelman, Mikiko Kainuma, Zbigniew Klimont, Giacomo Marangoni, Hermann Lotze-Campen, Michael Obersteiner, Andrzej Tabeau, Massimo Tavoni. Global Environmental Change, Volume 42, 2017, Pages 153-168, ISSN 0959-3780, https://doi.org/10.1016/j.gloenvcha.2016.05.009. From the 2017 paper: "The SSPs are part of a new scenario framework, established by the climate change research community in order to facilitate the integrated analysis of future climate impacts, vulnerabilities, adaptation, and mitigation. The pathways were developed over the last years as a joint community effort and describe plausible major global developments that together would lead in the future to different challenges for mitigation and adaptation to climate change. The SSPs are based on five narratives describing alternative socio-economic developments, including sustainable development, regional rivalry, inequality, fossil-fueled development, and middle-of-the-road development. The long-term demographic and economic projections of the SSPs depict a wide uncertainty range consistent with the scenario literature." According to SEDAC, the purpose of this data is: "To provide subnational (county) population projection scenarios for the United States essential for understanding long-term demographic changes, planning for the future, and decision-making in a variety of applications." According to Francesco Bassetti of Foresight, "The SSP’s baseline worlds are useful because they allow us to see how different socioeconomic factors impact climate change. They include: a world of sustainability-focused growth and equality (SSP1); a “middle of the road” world where trends broadly follow their historical patterns (SSP2); a fragmented world of “resurgent nationalism” (SSP3); a world of ever-increasing inequality (SSP4);a world of rapid and unconstrained growth in economic output and energy use (SSP5).There are seven sublayers, each with county boundaries and an identical set of attribute fields containing projections for these seven groupings across the five SSPs and nine decades.Total PopulationBlack Non-Hispanic PopulationWhite Non-Hispanic PopulationOther Non-Hispanic PopulationHispanic PopulationMale PopulationFemale Population Methodology: Documentation for the Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, v1 (2020 – 2100) Data currency: This layer was created from a shapefile downloaded April 18, 2023 from SEDAC's Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, v1 (2020 – 2100) Enhancements found in this layer: Every field was given a field alias and field description created from SEDAC's Data Dictionary downloaded April 18, 2023. Citation: Hauer, M., and Center for International Earth Science Information Network - CIESIN - Columbia University. 2021. Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/dv72-s254. Accessed 18 April 2023. Hauer, M. E. 2019. Population Projections for U.S. Counties by Age, Sex, and Race Controlled to Shared Socioeconomic Pathway. Scientific Data 6: 190005. https://doi.org/10.1038/sdata.2019.5. Distribution Liability: CIESIN follows procedures designed to ensure that data disseminated by CIESIN are of reasonable quality. If, despite these procedures, users encounter apparent errors or misstatements in the data, they should contact SEDAC User Services at +1 845-465-8920 or via email at ciesin.info@ciesin.columbia.edu. Neither CIESIN nor NASA verifies or guarantees the accuracy, reliability, or completeness of any data provided. CIESIN provides this data without warranty of any kind whatsoever, either expressed or implied. CIESIN shall not be liable for incidental, consequential, or special damages arising out of the use of any data provided by CIESIN.

GENERAL INFORMATION

Title of Dataset: A dataset from a survey investigating disciplinary differences in data citation

Date of data collection: January to March 2022

Collection instrument: SurveyMonkey

Funding: Alfred P. Sloan Foundation

SHARING/ACCESS INFORMATION

Licenses/restrictions placed on the data: These data are available under a CC BY 4.0 license

Links to publications that cite or use the data:

Gregory, K., Ninkov, A., Ripp, C., Peters, I., & Haustein, S. (2022). Surveying practices of data citation and reuse across disciplines. Proceedings of the 26th International Conference on Science and Technology Indicators. International Conference on Science and Technology Indicators, Granada, Spain. https://doi.org/10.5281/ZENODO.6951437

Gregory, K., Ninkov, A., Ripp, C., Roblin, E., Peters, I., & Haustein, S. (2023). Tracing data:
A survey investigating disciplinary differences in data citation. Zenodo. https://doi.org/10.5281/zenodo.7555266

DATA & FILE OVERVIEW

File List

• Filename: MDCDatacitationReuse2021Codebook.pdf
  Codebook
• Filename: MDCDataCitationReuse2021surveydata.csv
  Dataset format in csv
• Filename: MDCDataCitationReuse2021surveydata.sav
  Dataset format in SPSS
• Filename: MDCDataCitationReuseSurvey2021QNR.pdf
  Questionnaire

Additional related data collected that was not included in the current data package: Open ended questions asked to respondents

METHODOLOGICAL INFORMATION

Description of methods used for collection/generation of data:

The development of the questionnaire (Gregory et al., 2022) was centered around the creation of two main branches of questions for the primary groups of interest in our study: researchers that reuse data (33 questions in total) and researchers that do not reuse data (16 questions in total). The population of interest for this survey consists of researchers from all disciplines and countries, sampled from the corresponding authors of papers indexed in the Web of Science (WoS) between 2016 and 2020.

Received 3,632 responses, 2,509 of which were completed, representing a completion rate of 68.6%. Incomplete responses were excluded from the dataset. The final total contains 2,492 complete responses and an uncorrected response rate of 1.57%. Controlling for invalid emails, bounced emails and opt-outs (n=5,201) produced a response rate of 1.62%, similar to surveys using comparable recruitment methods (Gregory et al., 2020).

Methods for processing the data:

Results were downloaded from SurveyMonkey in CSV format and were prepared for analysis using Excel and SPSS by recoding ordinal and multiple choice questions and by removing missing values.

Instrument- or software-specific information needed to interpret the data:

The dataset is provided in SPSS format, which requires IBM SPSS Statistics. The dataset is also available in a coded format in CSV. The Codebook is required to interpret to values.

DATA-SPECIFIC INFORMATION FOR: MDCDataCitationReuse2021surveydata

Number of variables: 94

Number of cases/rows: 2,492

Missing data codes: 999 Not asked

Refer to MDCDatacitationReuse2021Codebook.pdf for detailed variable information.

The Groundswell Spatial Population and Migration Projections at One-Eighth Degree According to SSPs and RCPs, 2010-2050, data set provides a baseline population distribution for 2010 and projections from 2020 to 2050, in ten-year increments, of population distribution and internal climate-related and other migration. The projections are produced using the NCAR-CIDR Spatial Population Downscaling Model developed by the CUNY Institute for Demographic Research (CIDR) and the National Center for Atmospheric Research (NCAR). The model incorporates assumptions based on future development scenarios (Shared Socioeconomic Pathways or SSPs) and emissions trajectories (Representative Concentration Pathways or RCPs). The SSPs include SSP2, representing a middle-of-the road future, and SSP4, representing an unequal development future. Climate models using low and high emissions scenarios, RCP2.6 and RCP8.5, then drive climate impact models on crop productivity and water availability from the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). Sea-level rise impacts in the coastal zone are estimated to be 1 meter under RCP2.6 and 2 meters under RCP8.5, to account for potential storm surge or coastal flooding. Three scenarios are generated, a pessimistic reference scenario combining SSP4 and RCP8.5, a more climate-friendly scenario combining SSP4 and RCP2.6, and a more inclusive development scenario combining SSP2 and RCP8.5, and each scenario represents an ensemble of four model runs combining different climate impact models. The modeling work was funded and developed jointly with The World Bank, and covers most World Bank client countries, with reports released in 2018 and 2021 that address different regions and provide full methodological details.