This dataset complements the book by Gabriela Meier (University of Exeter, UK) and Simone Smala (University of Queensland, Australia):

Meier, G. & Smala, S. (2022). Languages and Social Cohesion: A Transdisciplinary Literature Review. Routledge. https://www.routledge.com/Languages-and-Social-Cohesion-A-Transdisciplinary-Literature-Review/Meier-Smala/p/book/9780367637200. Available from 26 July 2021.

The dataset of 285 references to peer-reviewed articles published in academic journals between 1992 and 2017 (identified systematically following the PRISMA protocol as is explained in the Chapter 3 of the book) is offered here as an EndNote Library to increase transparency and utility of the work we present, analyse and discuss in the book. It is designed to support researchers and other stakeholders to quickly and easily find literature related to themes and sub-themes, as well as by research design. The project described in the book had the aim to answer the question:

In what way are languages associated with social cohesion in academic articles?

As can be seen in the concluding chapter of the book (Chapter 5), this transdisciplinary literature review resulted in a transdisciplinary language and social cohesion framework, which is accompanied by user-friendly tools that can be used to explore the language-and-social cohesion constellation in diverse real-life contexts.

The EndNote Library available here presents the results of our systematic literature search and thematic analysis, which formed the basis of our analysis, discussion and interpretation of the data. In the EndNote Library, the articles are sorted by research design (qualitative, quantitative, and mixed method research, theory articles, case studies, practice reports and literature reviews). Importantly, the EndNote Library is also sorted by the main themes (see below) and respective sub-themes, which correspond to the themes discussed in the book.

Main themes in the book (headings used in the EndNote Library):

A: Social networks and access to resources through languages (social networks and resources) B: Norms related to languages and groups (ideological orientations) C: Languages and a sense of group belonging (belonging to groups) D: Manifestation of linguistic behaviour and social cohesion (practices in education/society) E: Formal language planning and social cohesion (policy and curricula)

The German Library Statistics (DBS) is the national statistics of the German library system and contains statistical key figures. It includes public libraries, scientific libraries, as well as specialized scientific libraries. More information can be found at DBS. This dataset contains the following information on scientific libraries in Bavaria in 2021: Total expenditure, total expenditure, including: Expenditure on printed books, total expenditure, including: Expenditure on current printed periodicals and newspapers, access: printed books, stock: purchased, continuously held, printed magazines and newspapers Note: Due to the pandemic, the data for the reporting years 2020/2021/2022 are only comparable to a limited extent with those of previous years!

Invited talk given by Tim Evans (Imperial College London) at the EPSRC Workshop on "Scaling in Social Systems” held at the Saïd Business School, Oxford on 1st December 2011. Abstract:

The pattern of innovation seen through citations of academic papers has long fascinated academics. It has been known for at least fifty years that the data shows various long tailed distributions. In this talk I will look at some of the features of the data and show how to extract some simple universal patterns. I will discuss some of the implications of the results and some of the further questions it raises. •What is a citation? •What does an individual citation mean? •Is the data perfect? •Why citation count? •If not citation count, what else? •What does this data say about me? •Why h-index? •What is a self-citation? •How else can I use this data? •How will things change?

Tim S. Evans – Mini Biography Tim studied the mixture of quantum field theory and statistical physics in his PhD at Imperial College London. He was supervised by Prof. Ray Rivers who also supervised another speaker, Prof. Luis Bettencourt. Tim then spent time as a researcher at the University of Alberta in Edmonton Canada, before returning to research positions back here at Imperial, latterly as a Royal Society University Research Fellow. He was appointed to a lectureship at Imperial in 1997. Around 2003 he expanded his work on statistical physics to cover at problems in complexity, with a particular interest in network methods. This has included participation in an EU collaboration with social scientists on innovation, ―ISCOM, run in part by Prof. Geoff West (another speaker today). This fuelled his interest in social science applications and started an on going collaboration with an archaeologist.

The statistic contains data on the U.S. book publishing in the category 'sociology/economics' from 2002 to 2013. In 2006, 27,675 books covering socio-economic topics were published in the United States.

The German Library Statistics (DBS) is the national statistics of the German library system and contains statistical key figures. It includes public libraries, scientific libraries, as well as specialized scientific libraries. More information can be found at DBS. This dataset contains the following information on scientific libraries in Bavaria in 2021: Total expenditure, total expenditure, including: Expenditure on printed books, total expenditure, including: Expenditure on current printed periodicals and newspapers, access: printed books, stock: purchased, continuously held, printed magazines and newspapers Note: Due to the pandemic, the data for the reporting years 2020/2021/2022 are only comparable to a limited extent with those of previous years!

This dataset is about books. It has 1 row and is filtered where the book is Sociology and statistics in Britain, 1833-1979. It features 7 columns including author, publication date, language, and book publisher.

'Dataset2' associated with: Who Tweets with Their Location? Understanding the Relationship Between Demographic Characteristics and the Use of Geoservices and Geotagging on Twitter

Luke Sloan and Jeffrey Morgan.

This study contains script files to create teaching versions of Understanding Society: Waves 1-3, the new UK household panel survey. Specifically, the user can focus on individual waves, or can create a panel survey dataset for use in teaching undergraduates and postgraduates. Core areas of focus are attitudes to voting and political parties, to the environment, and to ethnicity and migration. Script files are available for SPSS, STATA and R. Individuals wishing to make use of this resource will need to apply separately to the UK data archive for access to the original datasets: http://discover.ukdataservice.ac.uk/catalogue/?sn=6614 &type=Data%20catalogue

This dataset provides bibliographical information for 571 English language sociological research articles that empirically study actors' expectations, aspirations and perceptions of the future. This text corpus is the basis for an analysis published in "Beckert and Suckert, 2020, The Future as A Social Fact. The Analysis of Perceptions of the Future in Sociology, In: Poetics, online first".

The MEDIATIZED EU project aims to study how the media discourses are constructed to foster or hamper the European project and how they resonate among the public by focusing on the elite-media-public triangle. The research was conducted in seven target countries: Ireland, Belgium, Estonia, Spain, Portugal, Hungary and Georgia.

This dataset is part of the integration of the MEDIATIZED EU project research data into the EU’s Open Research Data Pilot. In accordance with the Data Management Plan, public opinion survey data were deemed suitable for being openly shared through ORDP to be accessible and of use to other academic researchers in Europe and worldwide. Quantitative data derived from surveys was deemed suitable, with the only concerns being the heterogeneous nature of some of the survey questions in each target country.

The aim of the population surveys was to investigate public opinion about the media and elites in their country and the EU and how they interpret elite and media discourses on Europeanisation and European integration. The merged database allows the project participants and other researchers to compare their national research results with phenomena in other participating countries.

This dataset contains a subset of integrated survey data including those survey questions where comparative data was available. The final deliverable contains this subsection of the survey data which has been weighted and cleaned, in .SAV and .XLS formats, and provides the requisite codebook for the dataset.

For more on the MEDIATIZED EU project, visit our website at mediatized.eu or view our CORDIS profile at: https://cordis.europa.eu/project/id/101004534

This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under grant agreement no 101004534. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

The XML tagging resources provided here support discourse analysis of research papers using the Information-Argument-Rhetorical Structure framework used in Dr Wei-Ning Cheng's thesis. The Information-Argument-Rhetorical Structure framework specifies 3 layers of discourse analysis: 1. Information structure analysis 2. Argument structure analysis 3. Rhetorical structure analysis The framework was derived from an analysis of the Abstract, Introduction and Literature Review sections of 30 sociology, mechanical engineering and bioscience research papers (10 each). The framework was applied to an additional 100 sociology research papers, and refined. Further work on mechanical engineering and bioscience papers are planned. A major step in the discourse analysis is to tag text spans (usually noun phrases, clauses and single words) in the text (in XML format) with XML tags that reference elements in the Information-Argument-Rhetorical Structure framework, using an XML editor software (e.g., oXygen XML editor).

A collection of over 75 charts and maps presenting key statistics on the farm sector, food spending and prices, food security, rural communities, the interaction of agriculture and natural resources, and more.

How much do you know about food and agriculture? What about rural America or conservation? ERS has assembled more than 75 charts and maps covering key information about the farm and food sectors, including agricultural markets and trade, farm income, food prices and consumption, food security, rural economies, and the interaction of agriculture and natural resources.

How much, for example, do agriculture and related industries contribute to U.S. gross domestic product? Which commodities are the leading agricultural exports? How much of the food dollar goes to farmers? How do job earnings in rural areas compare with metro areas? How much of the Nation’s water is used by agriculture? These are among the statistics covered in this collection of charts and maps—with accompanying text—divided into the nine section titles.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Ag and Food Sectors and the Economy Land and Natural Resources Farming and Farm Income Rural Economy Agricultural Production and Prices Agricultural Trade Food Availability and Consumption Food Prices and Spending Food Security and Nutrition Assistance For complete information, please visit https://data.gov.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for sociology and applied statistics in the U.S.

OVERVIEW

This data file, compiled from multiple online sources, presents 2013–2017 publication counts—articles, articles in high-impact journals, books, and books from high-impact publishers—for 2,132 professors and associate professors in 426 U.S. departments of sociology. It also includes information on institutional characteristics (e.g., institution type, highest sociology degree offered, department size) and individual characteristics (e.g., academic rank, gender, PhD year, PhD institution).

The data may be useful for investigations of scholarly productivity, the correlates of scholarly productivity, and the contributions of particular individuals and institutions. Complete population data are presented for the top 26 doctoral programs, doctoral institutions other than R1 universities, the top liberal arts colleges, and other bachelor's institutions. Sample data are presented for Carnegie R1 universities (other than the top 26) and master's institutions.

USER NOTES

Please see our paper in Scholarly Assessment Reports, freely available at https://doi.org/10.29024/sar.36 , for full information about the data set and the methods used in its compilation. The section numbers used here refer to the Appendix of that paper. See the References, below, for other papers that have made use of these data.

The data file is a single Excel file with five worksheets: Sampling, Articles, Books, Individuals, and Departments. Each worksheet has a simple rectangular format, and the cells include just text and values—no formulas or links. A few general notes apply to all five worksheets.

• The yellow column headings represent institutional (departmental) data. The blue column headings represent data for individual faculty.

• iType is institution type, as described in section A.2—TopR (top research universities), R1 (other R1 universities), OD (other doctoral universities), M (master's institutions), TopLA (top liberal arts colleges), or B (other bachelor's institutions). nType provides the same information, but as a single-digit code that is more useful for sorting the rows; 1=TopR, 2=R1, 3=OD, 4=M, 5=TopLA, and 6=B.

• Inst is a four-digit institution code. The first digit corresponds to nType, and the last three digits allow for alphabetical sorting by institution name. Indiv is a one- or two-digit code that can be used to sort the individuals by name within each department. The Inst, nType, and Indiv codes are consistent across the five worksheets.

• For binary variables such as Full professor and Female, 1 indicates yes (full professor or female) and 0 indicates no (associate professor or male).

The five worksheets represent five distinct stages in the data compilation process. First, the Sampling worksheet lists the 1,530 base-population institutions (see section A.3) and presents the characteristics of the faculty included in the data file. Each row with an entry in the Individual column represents a faculty member at one of the 426 institutions included in the data set. Each row without an entry in the Individual column represents an institution that either (a) did not meet the criteria for inclusion (section A.1) or (b) was not needed to attain the desired sample size for the R1 or M groups (section A.3).

The Articles worksheet includes the data compiled from SocINDEX, as described in section A.6. Each row with an entry in the Journal column represents an article written by one of the 2,132 faculty included in the data. Each row without an entry in the Journal column represents a faculty member without any article listings in SocINDEX for the 2013–2017 period. (Note that SocINDEX items other than peer-reviewed articles—editorials, letters, etc.—may be listed in the Journal column but assigned a value of 1 in the Excluded column and a value of 0 in the Article credit and HI article credit columns. We assigned no credit for items such as editorial and letters, but other researchers may wish to include them.) The N and i columns represent, for each article, the number of authors (N) and the faculty member's place in the byline (i), as described in section A.8. The CiteScore and Highest percentile columns were used to identify high-impact journals, as indicated in the HI journal column. The Article credit and HI article credit columns are article counts, adjusted for co-authorship.

The Books worksheet includes data compiled from Amazon and other sources, as described in section A.7. Each row with an entry in the Book column represents a book written by one of the 2,132 faculty. Each row without an entry in the Book column represents a faculty member without any book listings in Amazon during the 2013–2017 period. The publication counts in the Books worksheet—Book credit and HI book credit—follow the same format as those in the Articles worksheet.

The Individuals worksheet consolidates information from the Articles and Books worksheets so that each of the 2,132 individuals is represented by a single row. The worksheet also includes several categorical variables calculated or otherwise derived from the raw data—Years since PhD, for instance, and the three corresponding binary variables. We suspect that many data users will be most interested in the Individuals worksheet.

The Departments worksheet collapses the individual data so that each of the 426 institutions (departments) is represented by a single row. Individual characteristics such as Female and Years since PhD are presented as percentages or averages—% Female and Avg years since PhD, for instance. Each of the four productivity measures is represented by a departmental total, an average (the total divided by the number of full and associate professors), a departmental standard deviation, and a departmental median.

Data sharing is key for replication and re-use in empirical research. Scientific journals can play a central role by establishing data policies and providing technologies. In this study factors of influence for data sharing are analyzed by investigating journal data policies and author behavior in sociology. The websites of 140 journals from sociology were consulted to check their data policy. The results are compared with similar studies from political science and economics. For five selected journals with a broad variety all articles from two years are examined to see if authors really cite and share their data, and which factors are related to this.

and are the total number of nodes and links, respectively, is the network reciprocity denoted by Eq. (2), and denotes the network sparsity.

The empirical social sciences largely rely on the collection and analysis of research data. In recent years, several recommendations on the more open sharing of research data have been published. These recommendations aim at making science more transparent and replicable. In reality, however, many important research datasets are still not accessible. The project investigates how different factors influence the data sharing behavior of the authors of research papers in sociology and political sciences. It starts with an analysis of journal attributes and the articles published by selected journals to show how authors deal with their data. Second, a survey among the authors is conducted based on the Theory of Planned Behavior. This shows how personal characteristics are related to authors’ data sharing behavior.

Data extracted from the user, event and group profile for delegates in the 9th R users conference in Spain

Statistics from the paper: Are scholarly articles disproportionately read in their own country? An analysis of Mendeley readersby Mike Thelwall and Nabeil Maflahi

This dataset is about books. It has 1 row and is filtered where the book is Data in sociology. It features 7 columns including author, publication date, language, and book publisher.