These are synthetically generated unit and area level population and sample data that can be used for testing model-based unit-level small area methods. To prevent disclosure issues the datasets have been generated by repeated (Monte-Carlo) sampling of real EU-SILC (Survey of Income and Living Conditions) data in Austria. The data include geographical identifies and can be used for fitting unit-level (Battese-Harter and Fuller type) models and area level models (Fay-Herriott- type) models. The datasets are part of the R package emdi. Examples of the use of the data can be found in the emdi manual available via https://cran.r-project.org/web/packages/emdi/emdi.pdf and in Kreutzmann et al. (2019)

Kreutzmann, A. K., Pannier, S., Rojas-Perilla, N., Schmid, T., Templ, M., & Tzavidis, N. (2019). The R package emdi for the estimation and mapping of regional disaggregated indicators. Journal of Statistical Software, 91(7). https://doi.org/10.18637/jss.v091.i07

Reliable statistics are crucial for policy relevant research. Small Area Estimation (SAE) methods generate robust reliable and consistent statistics at geographical scales for which survey data are either non-existent or too sparse to provide direct estimates of acceptable accuracy. The last decade has seen a rapid increase in the use of SAE. Statistical agencies and Governmental organisations are actively developing their own suites of estimates. In the UK the Office for National Statistics (ONS) has responded to user demands by producing estimates of average household income for wards and using SAE to answer queries from local authorities, policy advisers and government departments. The Welsh Assembly Government (WAG) is actively seeking to develop capacity for SAE. Public Health England produces SAEs of adolescent smoking and chronic kidney disease. Initial demands for small area statistics are now shifting to requirements for more complex statistics that extend beyond averages and proportions to encompass estimates of statistical distributions, multidimensional indicators (e.g. inequality and deprivation indicators) and methods for replacing the Census and adjusting Census results for undercount. These developing requirements pose significant methodological and applied real-world challenges. These challenges are deepened by different methodological approaches to SAE remaining largely unconnected, locked in disciplinary silos. The technical presentation of SAE also impedes more widespread uptake by social scientists and understanding by users. The proposed programme of work aims to (a) develop novel SAE methodologies to better serve the needs of users and producers of SAE (b) bridge different methodological approaches to SAE, (c) apply SAE for answering substantive questions in the social sciences and (d) 'Mainstream' SAE within the quantitative social sciences through the creation of methodologically comprehensive and accessible resources. The project comprises three work packages of methodological innovative research designed to deepen the understanding of SAE and achieve the aforementioned aims. The project will capitalise on a cross-disciplinary research team drawn together through an NCRM methodological network and reflecting a large part of the SAE expertise in the UK. Through long-standing collaborations with national and international agencies in the UK, Mexico and Brazil, which are placed at the centre of the project, we enjoy access to individual level secondary survey and Census data. Collaboration with key SAE users will ensure that the project remains relevant to user needs and that methodologies are used for expanding the set of small area statistics currently available. The involvement of international experts ensures the quality and relevance of the research. Substantive outputs will include SAEs of attributes of interest to users, including income, inequality, deprivation, health, ethnicity and a realistic pseudo-Census dataset for use by other researchers. The project will advance knowledge across disciplines in the social sciences including social statistics, applied economics, human geography and sociology. It will additionally impact on the production of official and Census statistics. The project is committed to adding value to NCRM's training and capacity building activities by developing new resources.

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 2001: 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

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.

Data analysed in Alis et al., "Quantifying regional differences in the length of Twitter messages" Fields ------tweet id: retrieve tweet by passing this id to the REST APImlen: length of message, in characterswlen: length of message, in wordsmratio: proportion of message

This dataset is about books and is filtered where the book is Sociology and statistics in Britain, 1833-1979, featuring 7 columns including author, BNB id, book, book publisher, and ISBN. The preview is ordered by publication date (descending).

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 academic libraries in Bavaria in 2013: 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

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. Full selection of the journals in the 2013 Social Science Citation Index in the category "sociology"; All articles from 5 selected journals in 2012 and 2013.

ORs:odds ratios 95%CI: 95% confidence interval

Update — December 7, 2014. – Evidence-based medicine (EBM) is not working for many reasons, for example: 1. Incorrect in their foundations (paradox): hierarchical levels of evidence are supported by opinions (i.e., lowest strength of evidence according to EBM) instead of real data collected from different types of study designs (i.e., evidence). http://dx.doi.org/10.6084/m9.figshare.1122534 2. The effect of criminal practices by pharmaceutical companies is only possible because of the complicity of others: healthcare systems, professional associations, governmental and academic institutions. Pharmaceutical companies also corrupt at the personal level, politicians and political parties are on their payroll, medical professionals seduced by different types of gifts in exchange of prescriptions (i.e., bribery) which very likely results in patients not receiving the proper treatment for their disease, many times there is no such thing: healthy persons not needing pharmacological treatments of any kind are constantly misdiagnosed and treated with unnecessary drugs. Some medical professionals are converted in K.O.L. which is only a puppet appearing on stage to spread lies to their peers, a person supposedly trained to improve the well-being of others, now deceits on behalf of pharmaceutical companies. Probably the saddest thing is that many honest doctors are being misled by these lies created by the rules of pharmaceutical marketing instead of scientific, medical, and ethical principles. Interpretation of EBM in this context was not anticipated by their creators. “The main reason we take so many drugs is that drug companies don’t sell drugs, they sell lies about drugs.” ―Peter C. Gøtzsche “doctors and their organisations should recognise that it is unethical to receive money that has been earned in part through crimes that have harmed those people whose interests doctors are expected to take care of. Many crimes would be impossible to carry out if doctors weren’t willing to participate in them.” —Peter C Gøtzsche, The BMJ, 2012, Big pharma often commits corporate crime, and this must be stopped. Pending (Colombia): Health Promoter Entities (In Spanish: EPS ―Empresas Promotoras de Salud).

1. Misinterpretations New technologies or concepts are difficult to understand in the beginning, it doesn’t matter their simplicity, we need to get used to new tools aimed to improve our professional practice. Probably the best explanation is here in these videos (credits to Antonio Villafaina for sharing these videos with me). English https://www.youtube.com/watch?v=pQHX-SjgQvQ&w=420&h=315 Spanish https://www.youtube.com/watch?v=DApozQBrlhU&w=420&h=315 ----------------------- Hypothesis: hierarchical levels of evidence based medicine are wrong Dear Editor, I have data to support the hypothesis described in the title of this letter. Before rejecting the null hypothesis I would like to ask the following open question:Could you support with data that hierarchical levels of evidence based medicine are correct? (1,2) Additional explanation to this question: – Only respond to this question attaching publicly available raw data.– Be aware that more than a question this is a challenge: I have data (i.e., evidence) which is contrary to classic (i.e., McMaster) or current (i.e., Oxford) hierarchical levels of evidence based medicine. An important part of this data (but not all) is publicly available. References
2. Ramirez, Jorge H (2014): The EBM challenge. figshare. http://dx.doi.org/10.6084/m9.figshare.1135873
3. The EBM Challenge Day 1: No Answers. Competing interests: I endorse the principles of open data in human biomedical research Read this letter on The BMJ – August 13, 2014.http://www.bmj.com/content/348/bmj.g3725/rr/762595Re: Greenhalgh T, et al. Evidence based medicine: a movement in crisis? BMJ 2014; 348: g3725. _ Fileset contents Raw data: Excel archive: Raw data, interactive figures, and PubMed search terms. Google Spreadsheet is also available (URL below the article description). Figure 1. Unadjusted (Fig 1A) and adjusted (Fig 1B) PubMed publication trends (01/01/1992 to 30/06/2014). Figure 2. Adjusted PubMed publication trends (07/01/2008 to 29/06/2014) Figure 3. Google search trends: Jan 2004 to Jun 2014 / 1-week periods. Figure 4. PubMed publication trends (1962-2013) systematic reviews and meta-analysis, clinical trials, and observational studies.
   Figure 5. Ramirez, Jorge H (2014): Infographics: Unpublished US phase 3 clinical trials (2002-2014) completed before Jan 2011 = 50.8%. figshare.http://dx.doi.org/10.6084/m9.figshare.1121675 Raw data: "13377 studies found for: Completed | Interventional Studies | Phase 3 | received from 01/01/2002 to 01/01/2014 | Worldwide". This database complies with the terms and conditions of ClinicalTrials.gov: http://clinicaltrials.gov/ct2/about-site/terms-conditions Supplementary Figures (S1-S6). PubMed publication delay in the indexation processes does not explain the descending trends in the scientific output of evidence-based medicine. Acknowledgments I would like to acknowledge the following persons for providing valuable concepts in data visualization and infographics:
4. Maria Fernanda Ramírez. Professor of graphic design. Universidad del Valle. Cali, Colombia.
5. Lorena Franco. Graphic design student. Universidad del Valle. Cali, Colombia. Related articles by this author (Jorge H. Ramírez)
6. Ramirez JH. Lack of transparency in clinical trials: a call for action. Colomb Med (Cali) 2013;44(4):243-6. URL: http://www.ncbi.nlm.nih.gov/pubmed/24892242
7. Ramirez JH. Re: Evidence based medicine is broken (17 June 2014). http://www.bmj.com/node/759181
8. Ramirez JH. Re: Global rules for global health: why we need an independent, impartial WHO (19 June 2014). http://www.bmj.com/node/759151
9. Ramirez JH. PubMed publication trends (1992 to 2014): evidence based medicine and clinical practice guidelines (04 July 2014). http://www.bmj.com/content/348/bmj.g3725/rr/759895 Recommended articles
10. Greenhalgh Trisha, Howick Jeremy,Maskrey Neal. Evidence based medicine: a movement in crisis? BMJ 2014;348:g3725
11. Spence Des. Evidence based medicine is broken BMJ 2014; 348:g22
12. Schünemann Holger J, Oxman Andrew D,Brozek Jan, Glasziou Paul, JaeschkeRoman, Vist Gunn E et al. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies BMJ 2008; 336:1106
13. Lau Joseph, Ioannidis John P A, TerrinNorma, Schmid Christopher H, OlkinIngram. The case of the misleading funnel plot BMJ 2006; 333:597
14. Moynihan R, Henry D, Moons KGM (2014) Using Evidence to Combat Overdiagnosis and Overtreatment: Evaluating Treatments, Tests, and Disease Definitions in the Time of Too Much. PLoS Med 11(7): e1001655. doi:10.1371/journal.pmed.1001655
15. Katz D. A-holistic view of evidence based medicinehttp://thehealthcareblog.com/blog/2014/05/02/a-holistic-view-of-evidence-based-medicine/ ---

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

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.

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

This research explored what happens when social media data mining becomes ordinary and is carried out by organisations that might be seen as the pillars of everyday life. The interviews on which the transcripts are based are discussed in Chapter 6 of the book. The referenced book contains a description of the methods. No other publications resulted from working with these transcripts.

This dataset is about books and is filtered where the book is Data in sociology, featuring 7 columns including author, BNB id, book, book publisher, and ISBN. The preview is ordered by publication date (descending).

ORs: odds ratios 95%CI: 95% confidence interval

The dataset consists of content published in groups of Russian universities on the social media VKontakte. The dataset contains posts and comments from 9,215 university publics from June 2022 to August 2023.

'International library of sociology and social reconstruction is a book series. It includes 199 books, written by 147 different authors.

Qualitative research among rare disease patients and advocates about their views on the international sharing of data and biospecimens for research.

A newer version of this dataset is available at https://doi.org/10.7802/2284
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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.

Revised dataset to original publication:

Hertwig, Alexandra (2019): Higher Education Research: A Compilation of Journals and Abstracts 2018. Kassel: INCHER-Kassel. DOI: 10.17906/INCHER.0004.

Datasets to volume 2013 to 2018 might vary with regard to covered journals of the original publication. The datasets include journals and respective publication data providing persistent identifiers, explicitly.

The Research Information Service (RIS) of INCHER-Kassel, Germany provides annual compilation of academic journals since 2013. The datasets allow for further evaluation of single or multiple volumes. For more information on original publications and available datasets please visit INCHER’s RIS websites.

http://www.uni-kassel.de/einrichtungen/en/incher/risspecial-research-library/ris-documents.html