Dimensions is the largest database of research insight in the world. It represents the most comprehensive collection of linked data related to the global research and innovation ecosystem available in a single platform. Because Dimensions maps the entire research lifecycle, you can follow academic and industry research from early stage funding, through to output and on to social and economic impact. Businesses, governments, universities, investors, funders and researchers around the world use Dimensions to inform their research strategy and make evidence-based decisions on the R&D and innovation landscape. With Dimensions on Google BigQuery, you can seamlessly combine Dimensions data with your own private and external datasets; integrate with Business Intelligence and data visualization tools; and analyze billions of data points in seconds to create the actionable insights your organization needs. Examples of usage: Competitive intelligence Horizon-scanning & emerging trends Innovation landscape mapping Academic & industry partnerships and collaboration networks Key Opinion Leader (KOL) identification Recruitment & talent Performance & benchmarking Tracking funding dollar flows and citation patterns Literature gap analysis Marketing and communication strategy Social and economic impact of research About the data: Dimensions is updated daily and constantly growing. It contains over 112m linked research publications, 1.3bn+ citations, 5.6m+ grants worth $1.7trillion+ in funding, 41m+ patents, 600k+ clinical trials, 100k+ organizations, 65m+ disambiguated researchers and more. The data is normalized, linked, and ready for analysis. Dimensions is available as a subscription offering. For more information, please visit www.dimensions.ai/bigquery and a member of our team will be in touch shortly. If you would like to try our data for free, please select "try sample" to see our openly available Covid-19 data.Learn more

This dataset contains a collection of 103 research comparisons from the Open Research Knowledge Graph (ORKG) with annotated properties and corresponding research dimensions generated by three different Large Language Models (LLMs). The dataset includes 1,317 papers from 35 diverse research fields, addressing 153 distinct research problems. Each paper is associated with human-annotated ORKG properties, as well as research dimensions generated by GPT-3.5, Llama 2, and Mistral LLMs. The dataset provides a comprehensive evaluation benchmark for comparing the performance of different LLMs in generating research dimensions that align with human-annotated properties.

Dataset columns:

• comparison_id: Unique identifier of the research comparison in the Open Research Knowledge Graph (ORKG)
• contribution_id: Identifier of the individual research contribution (paper) within a comparison
• paper_id: Unique identifier of the research paper
• paper_title: Title of the research paper
• research_field: Field of research associated with the paper
• research_problem: Specific research problem addressed by the paper
• orkg_properties: Human-annotated properties of the paper in the ORKG, representing specific attributes or characteristics of the research contribution
• gpt_dimensions: Research dimensions generated by the GPT Large Language Model (LLM) for the paper
• mistral_dimensions: Research dimensions generated by the Mistral LLM for the paper
• llama2_dimensions: Research dimensions generated by the Llama2 LLM for the paper
• mappings: Mapping of ORKG properties to LLM-generated research dimensions
• alignments: Alignment scores between ORKG properties and LLM-generated research dimensions
• deviations: Deviation scores between ORKG properties and LLM-generated research dimensions
• orkg_gpt_similarity: Cosine similarity score between the embeddings of ORKG properties and GPT-generated research dimensions
• orkg_llama2_similarity: Cosine similarity score between the embeddings of ORKG properties and Llama2-generated research dimensions
• orkg_mistral_similarity: Cosine similarity score between the embeddings of ORKG properties and Mistral-generated research dimensions
• gpt_llama2_similarity: Cosine similarity score between the embeddings of GPT-generated and Llama2-generated research dimensions
• gpt_mistral_similarity: Cosine similarity score between the embeddings of GPT-generated and Mistral-generated research dimensions
• llama2_mistral_similarity: Cosine similarity score between the embeddings of Llama2-generated and Mistral-generated research dimensions

This dataset from Dimensions.ai contains all published articles, preprints, clinical trials, grants and research datasets that are related to COVID-19. This growing collection of research information now amounts to hundreds of thousands of items, and it is the only dataset of its kind. You can find an overview of the content in this interactive Data Studio dashboard: https://reports.dimensions.ai/covid-19/ The full metadata includes the researchers and organizations involved in the research, as well as abstracts, open access status, research categories and much more. You may wish to use the Dimensions web application to explore the dataset: https://covid-19.dimensions.ai/. This dataset is for researchers, universities, pharmaceutical & biotech companies, politicians, clinicians, journalists, and anyone else who wishes to explore the impact of the current COVID-19 pandemic. It is updated daily, and free for anyone to access. Please share this information with anyone you think would benefit from it. If you have any suggestions as to how we can improve our search terms to maximise the volume of research related to COVID-19, please contact us at support@dimensions.ai. About Dimensions: Dimensions is the largest database of research insight in the world. It contains a comprehensive collection of linked data related to the global research and innovation ecosystem, all in a single platform. This includes hundreds of millions of publications, preprints, grants, patents, clinical trials, datasets, researchers and organizations. Because Dimensions maps the entire research lifecycle, you can follow academic and industry research from early stage funding, through to output and on to social and economic impact. This Covid-19 dataset is a subset of the full database. The full Dimensions database is also available on BigQuery, via subscription. Please visit www.dimensions.ai/bigquery to gain access.Más información

This dataset contains data collected during a study "Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems" conducted by Martin Lnenicka (University of Hradec Králové, Czech Republic), Anastasija Nikiforova (University of Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Serbia), Daniel Rudmark (Swedish National Road and Transport Research Institute, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Karlo Kević (University of Zagreb, Croatia), Anneke Zuiderwijk (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain).

As there is a lack of understanding of the elements that constitute different types of value-adding public data ecosystems and how these elements form and shape the development of these ecosystems over time, which can lead to misguided efforts to develop future public data ecosystems, the aim of the study is: (1) to explore how public data ecosystems have developed over time and (2) to identify the value-adding elements and formative characteristics of public data ecosystems. Using an exploratory retrospective analysis and a deductive approach, we systematically review 148 studies published between 1994 and 2023. Based on the results, this study presents a typology of public data ecosystems and develops a conceptual model of elements and formative characteristics that contribute most to value-adding public data ecosystems, and develops a conceptual model of the evolutionary generation of public data ecosystems represented by six generations called Evolutionary Model of Public Data Ecosystems (EMPDE). Finally, three avenues for a future research agenda are proposed.

This dataset is being made public both to act as supplementary data for "Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems ", Telematics and Informatics*, and its Systematic Literature Review component that informs the study.

Description of the data in this data set

PublicDataEcosystem_SLR provides the structure of the protocol

Spreadsheet#1 provides the list of results after the search over three indexing databases and filtering out irrelevant studies

Spreadsheets #2 provides the protocol structure.

Spreadsheets #3 provides the filled protocol for relevant studies.

The information on each selected study was collected in four categories:(1) descriptive information,(2) approach- and research design- related information,(3) quality-related information,(4) HVD determination-related information

Descriptive Information

Article number

A study number, corresponding to the study number assigned in an Excel worksheet

Complete reference

The complete source information to refer to the study (in APA style), including the author(s) of the study, the year in which it was published, the study's title and other source information.

Year of publication

The year in which the study was published.

Journal article / conference paper / book chapter

The type of the paper, i.e., journal article, conference paper, or book chapter.

Journal / conference / book

Journal article, conference, where the paper is published.

DOI / Website

A link to the website where the study can be found.

Number of words

A number of words of the study.

Number of citations in Scopus and WoS

The number of citations of the paper in Scopus and WoS digital libraries.

Availability in Open Access

Availability of a study in the Open Access or Free / Full Access.

Keywords

Keywords of the paper as indicated by the authors (in the paper).

Relevance for our study (high / medium / low)

What is the relevance level of the paper for our study

Approach- and research design-related information

Approach- and research design-related information

Objective / Aim / Goal / Purpose & Research Questions

The research objective and established RQs.

Research method (including unit of analysis)

The methods used to collect data in the study, including the unit of analysis that refers to the country, organisation, or other specific unit that has been analysed such as the number of use-cases or policy documents, number and scope of the SLR etc.

Study’s contributions

The study’s contribution as defined by the authors

Qualitative / quantitative / mixed method

Whether the study uses a qualitative, quantitative, or mixed methods approach?

Availability of the underlying research data

Whether the paper has a reference to the public availability of the underlying research data e.g., transcriptions of interviews, collected data etc., or explains why these data are not openly shared?

Period under investigation

Period (or moment) in which the study was conducted (e.g., January 2021-March 2022)

Use of theory / theoretical concepts / approaches? If yes, specify them

Does the study mention any theory / theoretical concepts / approaches? If yes, what theory / concepts / approaches? If any theory is mentioned, how is theory used in the study? (e.g., mentioned to explain a certain phenomenon, used as a framework for analysis, tested theory, theory mentioned in the future research section).

Quality-related information

Quality concerns

Whether there are any quality concerns (e.g., limited information about the research methods used)?

Public Data Ecosystem-related information

Public data ecosystem definition

How is the public data ecosystem defined in the paper and any other equivalent term, mostly infrastructure. If an alternative term is used, how is the public data ecosystem called in the paper?

Public data ecosystem evolution / development

Does the paper define the evolution of the public data ecosystem? If yes, how is it defined and what factors affect it?

What constitutes a public data ecosystem?

What constitutes a public data ecosystem (components & relationships) - their "FORM / OUTPUT" presented in the paper (general description with more detailed answers to further additional questions).

Components and relationships

What components does the public data ecosystem consist of and what are the relationships between these components? Alternative names for components - element, construct, concept, item, helix, dimension etc. (detailed description).

Stakeholders

What stakeholders (e.g., governments, citizens, businesses, Non-Governmental Organisations (NGOs) etc.) does the public data ecosystem involve?

Actors and their roles

What actors does the public data ecosystem involve? What are their roles?

Data (data types, data dynamism, data categories etc.)

What data do the public data ecosystem cover (is intended / designed for)? Refer to all data-related aspects, including but not limited to data types, data dynamism (static data, dynamic, real-time data, stream), prevailing data categories / domains / topics etc.

Processes / activities / dimensions, data lifecycle phases

What processes, activities, dimensions and data lifecycle phases (e.g., locate, acquire, download, reuse, transform, etc.) does the public data ecosystem involve or refer to?

Level (if relevant)

What is the level of the public data ecosystem covered in the paper? (e.g., city, municipal, regional, national (=country), supranational, international).

Other elements or relationships (if any)

What other elements or relationships does the public data ecosystem consist of?

Additional comments

Additional comments (e.g., what other topics affected the public data ecosystems and their elements, what is expected to affect the public data ecosystems in the future, what were important topics by which the period was characterised etc.).

New papers

Does the study refer to any other potentially relevant papers?

Additional references to potentially relevant papers that were found in the analysed paper (snowballing).

Format of the file.xls, .csv (for the first spreadsheet only), .docx

Licenses or restrictionsCC-BY

For more info, see README.txt

Established researchers by (ANZSRC) 2-Digital level Field of Research. A researcher is ``established'' for the purposes of this table by having a publication history of more than 15 years.

Nano Dimensions, an Israel-based company producing 3D printers, spent almost 9.9 million U.S. dollars on research and development in the 2020 fiscal year. This represents an increase of around 22 percent compared with the previous fiscal year.

Financial overview and grant giving statistics of Dimensions Educational Research Foundation

This study includes event summaries derived from The Conflict and Peace Data Bank (COPDAB) Project (see also CONFLICT AND PEACE DATA BANK (COPDAB), 1948-1978 [ICPSR 7767]). Part 1 contains yearly summaries of events directed by one international actor toward another. There are both conflict and cooperation summaries, including measures of the frequency and intensity of events, and a measure of the dimension of interaction, which combines frequency and intensity. Event summaries are included only for dyads involving the following political entities as actors and targets: Algeria, Canada, China, Cyprus, Federal Republic of Germany, German Democratic Republic, Egypt, France, Greece, India, Indonesia, Iran, Iraq, Israel, Italy, Japan, Jordan, Kuwait, Lebanon, Libya, Morocco, Pakistan, Palestine Liberation Organization, Saudi Arabia, Sudan, Syria, Tunisia, Turkey, United Kingdom, United States, and Soviet Union. Data are recorded for each dyad for each year between 1948-1973. Part 2 contains domestic event summaries for the same 31 political entities. The variables measure frequency, intensity, and dimension of interaction (frequency and intensity) for both conflictive and cooperative domestic events. Data are recorded by year for each entity. In Part 3, a variable records the total number of international events initiated by each actor in each year, while a second variable calculates this yearly total as a percentage of all events initiated by the same actor during the 26-year period. Part 4 provides similar information, but with the dyad actor-target as a unit of analysis. One variable records the total number of events initiated by the actor toward the target over the whole time period, while a second variable calculates the number of events directed at one target as a percentage of the events directed at all targets.

The Dimensions API’s flexible query syntax and native json results format integrate well with data science workbenches such as Jupyter notebooks. (Jupyter notebook and HTML rendering files attached).

This dataset was used as a data corpus for a bibliometric analysis with the title "Unveiling Openness in Energy Research: A Bibliometric Analysis Focusing on Open Access and Data Sharing Practices".

The CSV file (2024-12-06_OpenAlex_API_download_works_Energy_Germany_(2013-2023)) was collected on December 6^th, 2024, by using the OpenAlex API and search criteria: OpenAlex field "Energy", continent “Europe”, country “Germany”, and publication years 2013 – 2023. Based on this file, two sample files were extracted - one by subfield (2024-12-06_OpenAlex_API_dwonload_works_Energy_Germany_(2013-2023)_sampled_by_subfield) and another by year group (2024-12-06_OpenAlex_API_download_works_Energy_Germany_(2013-2023)_sampled_by_year_group).

This dataset was collected and used to answer the following research questions:

- What percentage of energy research publications are OA? How do the types (gold, green, etc.) of these publications differ?

- Are there notable differences in OA and data sharing practices in different subfields of energy research?

- How commonly are datasets for energy studies shared? What are the primary repositories used?

- What kind of data sharing or publication practices are widespread? How has this evolved over the last decade?

Data and code belonging to the manuscript: The availability and completeness of open funder metadata - Case study for publications funded by the Dutch Research Council

Abstract: Research funders spend considerable efforts collecting information on outcomes of the research they fund. To help funders track publication output associated with their funding, Crossref initiated FundRef in 2013, enabling publishers to register funding information using persistent identifiers. However, it is hard to assess the coverage of funder metadata because it is unknown how many articles are the result of funded research and therefore should include funder metadata.

In this paper we looked at 5,004 publications reported by researchers to be the result of funding by a specific funding agency: the Dutch Research Council NWO. Only 67% of these articles contain funding information in Crossref, with a subset acknowledging NWO as funder name and/or Funder IDs linked to NWO (53% and 45%, respectively).

Web of Science (WoS), Scopus and Dimensions are all able to infer additional funding information from funding statements in the full text of the articles. Funding information in Lens largely corresponds to that in Crossref, with some additional funding information likely taken from PubMed.

We observe interesting differences between publishers in the coverage and completeness of funding metadata in Crossref compared to proprietary databases, highlighting potential to increase the quality of open metadata on funding.

This dataset contains the following files:

DOIs_unique_CR_Lens_Wos_Scopus_Dim.csv - Dataset of unique DOIs (n= 5,004) with collected information from Crossref and presence/absence of funder information in Lens, Web of Science, Scopus and Dimensions

NWO_funder_names_Crossref.txt - List of funder name variants for NWO found in Crossref

Google_Apps_Script.js - Google Apps Script for retrieving information from Crossref and processing Dimensions results

DOI_cleaning.R - R script for cleaning DOIs

Dataset of the manuscript "What is local research? Towards a multidimensional framework linking theory and methods". In this research article we propose a theoretical and empirical framework of local research, a concept of growing importance due to its far-reaching implications for public policy. Our motivation stems from the lack of clarity surrounding the increasing yet uncritical use of the term in both scientific publications and policy documents, where local research is conceptualized and measured in many ways. A clear understanding of it is crucial for informed decision-making when setting research agendas, allocating funds, and evaluating and rewarding scientists. Our twofold aim is (1) to compare the existing approaches that define and measure local research, and (2) to assess the implications of applying one over another. We first review the perspectives and measures used since the 1970s. Drawing on spatial scientometrics and proximities, we then build a framework that splits the concept into several dimensions: locally informed research, locally situated research, locally relevant research, locally bound research, and locally governed research. Each dimension is composed of a definition and a methodological approach, which we test in 10 million publications from the Dimensions database. Our findings reveal that these approaches measure distinct and sometimes unaligned aspects of local research, with varying effectiveness across countries and disciplines. This study highlights the complex, multifaceted nature of local research. We provide a flexible framework that facilitates the analysis of these dimensions and their intersections, in an attempt to contribute to the understanding and assessment of local research and its role within the production, dissemination, and impact of scientific knowledge.

The Computer Administered Panel Study (CAPS) collected demographic, personality, attitudinal, and other social psychological data from annual samples of University of North Carolina undergraduates from 1983 through 1988. Respondents spent 60 to 90 minutes per week for 20 weeks during the academic year answering questions via computer terminals. In their comparison of demographic and academic variables, researchers found few significant differences between respondents and the general undergraduate population. This dataset measures five dimensions of religiosity, which consist of a 21-item scale, adapted from Faulkner and DeJong (1966).

This dataset includes information on quality control and data management of researchers and data curators from a social science organization. Four data curators and 24 researchers provided responses for the study. Data collection techniques, data processing strategies, data storage and preservation, metadata standards, data sharing procedures, and the perceived significance of quality control and data quality assurance are the main areas of focus. The dataset attempts to provide insight on the RDM procedures that are being used by a social science organization as well as the difficulties that researchers and data curators encounter in upholding high standards of data quality. The goal of the study is to encourage more investigations aimed at enhancing scientific community data management practices and guidelines.

Percentage New Research Collaborations by Disciplinary Intersection. The colour of each pixel represents the level of new researcher-researcher co-authorship pairings seen on COVID-19 papers compared with pre-COVID. Each researcher in the pairing has a principal research categorisation and the combination of these two determines the relevant pixel. The intensity of the colour in the pixel corresponds to the proportion of new collaborations to existing collaborations in the relevant subject pairing. There is a 10 researcher cut-off for each pixel to prevent flagging low volume collaboration signals clouding the picture.

This dataset was compiled as part of a study investigating the trends, volume, and patterns of research data production and archiving in Kenya, using Dimensions.ai, a global research information platform. The study adopts an informetric approach to examine how Kenyan institutions, authors, and disciplines contribute to global research data and how such outputs are archived, cited, and accessed.

Data Source:

• Extracted from the Dimensions.ai database using filters for:
• Country: Kenya
• Publication Years 1952 to 2023
• Research Output Types: Datasets and linked publications
• Fields of Research: All disciplines

Contents and Variables:

The dataset includes:

• Title and DOI of dataset/publication
• Year of publication
• Author(s) and affiliation(s)
• Institutional contributors
• Source or repository (e.g., Figshare, Dryad, institutional repository)
• Type of data (raw, processed, software, code, etc.)
• Citation counts
• Altmetric indicators (if available)
• Research field/classification

Sample Size:

• A total of 45,737 datasets were retrieved and analysed, representing datasets linked to researchers or institutions affiliated with Kenya.

While work on the relationship between social media use and adolescent mental health has allowed for some progress, research in this area is still relatively new and shows mixed evidence. This is partly the consequence of a rapidly changing field, resulting in conceptualisation and measurement issues that hinder progress. Given the need for robust conceptualisation, the present study included five focus groups with a total of 26 adolescents aged 11-15 in Northwest England, to understand their experiences, motivations, and perceptions of social media use, relating to mental health and wellbeing. Reflexive thematic analysis was used to analyse the transcripts. We developed three themes and 14 sub-themes. Young people discussed key motivations for using social media (theme 1) relating to social connections, keeping up-to-date, mood management, the ‘default’ activity, freedom to express and develop myself, and fitting in. They shared some of the benefits and positive experiences of social media use (theme 2) such as feeling connected, validation and reassurance, and enjoyment and supporting a sense of self, and finally, they talked about negative experiences of social media use (theme 3), including platform risks, loss of control, social conflict, social comparison, and self-presentation management. Our findings have contributed to our understanding of the salient dimensions and language to inform the development of an adolescent social media experience measure related to mental health.

This file contains the dimensions of a nested coaxial waveguide feed antenna for radio telescope that covers two closely spaced octave bands.

The MUSIC-OpRA dataset offers valuable insights into the representation of uncertainty in scientific literature across various domains. Researchers and practitioners can use this dataset to study and analyze the variations of uncertainty expressions in scholarly discourse.

This dataset contains sentences extracted from open access articles in a wide range of fields, covering both Science, Technology, and Medicine (STM); and Social Sciences and Humanities (SSH) and annotated with respect to uncertainty in science. The dataset is derived from PubMed, Scopus, Web of Science (WoS). It has been produced as part of the ANR InSciM (Modelling Uncertainty in Science) project.

The sentences were annotated by two independent annotators following the annotation guide proposed by Ningrum and Atanassova (2024). The annotators were trained on the basis of an annotation guide and previously annotated sentences in order to guarantee the consistency of the annotations.

Each sentence was annotated as expressing or not expressing uncertainty (Uncertainty and No Uncertainty).
Sentences expressing uncertainty were then annotated along five dimensions: Reference , Nature, Context , Timeline and Expression.

The dataset is provided in CSV format. The columns in the table are as follows:

• sentence_id: A unique internal identifier for each sentence.
• journal_name: The name of the journal in which the article was published.
• sampling_technique: Sampling method used to select the sentence. Two approaches were employed:
  • CueMapping: Sentences were randomly selected based on occurrences of uncertainty cues from pre-defined lists (Bongelli et al., 2019; Chen et al., 2018; Hyland, 1996).
  • Manual: Sentences were manually extracted by identifying uncertainty and non-uncertainty expressions in a subset of articles (two randomly selected articles per journal).
• article_title: The title of the article from which the sentence was extracted.
• document_id: The URL where the article is published.
• publication_year: The year the article was published.
• sentence: The text of the sentence.
• uncertainty: '1' if the sentence expresses uncertainty, and '0' otherwise.
• reference, nature, context, timeline, expression: annotations of the type of uncertainty according to the annotation framework proposed by Ningrum and Atanassova (2023). The annotation of each dimension in this dataset are in numeric format rather than textual. The mapping betwen textual and numeric labels is presented in the Table below.

For a more comprehensive understanding of the construction of the dataset, including the selection of journals, sampling procedure, and the annotation methodology, see Ningrum and Atanassova (2023); and Ningrum and Atanassova (2024).

References

Bongelli, R., Riccioni, I., Burro, R., & Zuczkowski, A. (2019). Writers’ uncertainty in scientific and popular biomedical articles. A comparative analysis of the British Medical Journal and Discover Magazine [Publisher: Public Library of Science]. PLoS ONE, 14 (9). https://doi.org/10.1371/journal.pone.0221933

Chen, C., Song, M., & Heo, G. E. (2018). A scalable and adaptive method for finding semantically equivalent cue words of uncertainty. Journal of Informetrics, 12 (1), 158–180. https://doi.org/10.1016/j.joi.2017.12.004

Hyland, K. E. (1996). Talking to the academy forms of hedging in science research articles [Publisher: SAGE Publications Inc.]. Written Communication, 13 (2), 251–281. https://doi.org/10.1177/0741088396013002004

Ningrum, P. K., & Atanassova, I. (2023). Scientific Uncertainty: An Annotation Framework and Corpus Study in Different Disciplines. 19th International Conference of the International Society for Scientometrics and Informetrics (ISSI 2023). https://doi.org/10.5281/zenodo.8306035

Ningrum, P. K., & Atanassova, I. (2024). Annotation of scientific uncertainty using linguistic patterns. Scientometrics. https://doi.org/10.1007/s11192-024-05009-z

State capacity is a core concept in political science research, and it is widely recognized that state institutions exert considerable influence on outcomes such as economic development, civil conflict, democratic consolidation, and international security. Yet, researchers across these fields of inquiry face common problems involved in conceptualizing and measuring state capacity. In this article, we examine these conceptual issues, identify three core dimensions of state capacity, and develop the expectation that they are mutually supporting and interlinked. We then use Bayesian latent variable analysis to estimate state capacity at the conjunction of indicators related to these dimensions. We find strong interrelationships between the three dimensions and produce a new, general-purpose measure of state capacity with demonstrated validity for use in a wide range of empirical inquiries. It is hoped that this project will provide effective guidance and tools for researchers studying the causes and consequences of state capacity.