dataesr/scientific-paragraphs-categorization huggingface dataset:
How? The basic starting point for any quantitative analysis of IOs is the selection of a large-N dataset. This initial choice, and many decisions that follow, will largely be determined by theory. Depending on the question one asks about IOs, different datasets will be relevant. In my recent research, for instance, I have been interested in the legal nature of IOs and to study this I used a dataset that measures levels of legal formality  (Roger 2020). This is the "dependent variable" in my analysis. However, in practice, a large number of options are available, and data on IOs can be used as either dependent or independent variables  (Gartzke and Schneider 2013). One of the earliest and most frequently used IO datasets was developed as part of the Correlates of War (COW) project initiated by J.
David Singer  (Wallace and Singer 1970; Pevehouse, Nordstrom, and Warnke 2004). The most recent version of this dataset measures membership in formal bodies and has served as a fundamental resource for IO scholars. Today, however, we also have fairly comprehensive data on features of IOs-their independence, policy scope, voting rules, and so on-and many of their activities too  (Haftel and Thompson 2006; Hooghe, Lenz, and Marks 2019). Once one has selected data that measure the key variable of interest, the task is then to combine this with data on other covariates-democracy, economic openness, national capabilities, and so on. The data that are rel-evant will again be determined by theory. In my case, I needed data that theory suggested might shape the legal design of IOs. To test "power-based" explanations, for instance, it seemed reasonable to measure power using preexisting data on GDP across relevant states.
In addition to such drawbacks, however, there are others more specific to studying IOs. The first is that IO data can sometimes be uneven and difficult to work with. Much depends on the question one asks, but for research that focuses on the "IO" as the unit of analysis, or on particular processes within IOs, it can be difficult to attain the data needed. A key issue arises from the fact that most datasets include samples of IOs or, when more complete, may be structured for cross-country or country-year analysis, i. e. , where the unit of analysis in such studies is a state-level "event" in a given year. State-IO membership data, such as that contained in the COW dataset, is structured in this way and can be integrated fairly easily with other types. But if one is interested in understanding what determines the design of IOs, it will often be necessary to match different datasets and (sometimes) transform existing data to ensure it is structured appropriately.
Because many include different samples of organizations and do not always match up perfectly, this can be challenging. The need to transform data can also mean that research on IOs-of this type-can still be quite costly and time-intensive. Second, even when available, it is important to approach datasets with some caution. There are, for instance, known problems in the COW dataset: duplicates, incorrect dates, missing IOs, and so on. A more fundamental issue arises when the concept underlying the data is problematic, or doesn't align with that presumed by a theory. One example of this comes from data on voting rules. Blake and Payton (2015)  offer comprehensive data on voting arrangements in IOs. But others have questioned how they define these, especially the practice of "consensus" voting.
It can also reveal new puzzles. In practice, the Yearbook of International Organizations has proved to be an invaluable resource for doing this, since it contains descriptions of numerous institutions (Union of International Associations 2009). It has correspondingly served as the starting point for many datasets, COW included. Other resources include the websites, legal documents, and publications of IOs. These can often be useful but may not be available for historical institutions, and can be somewhat uneven in terms of their content. It is also worth noting that they can sometimes give a misleading picture of what happens. A charter may outline a precise set of decision-making procedures and a website may offer helpful descriptions, but practices may differ. To handle this, surveys and survey experiments have increasingly been used to collect data  (Hooghe 2005; Hardt 2018 ).
How? Prior to applying computerized textual analysis, a researcher needs to decide which texts and databases will be analyzed. In the process of text selection, a researcher can either choose to analyze textual databases that already exist or create new databases using tools that enables importing and collecting of data. In most cases, the researcher will be required to validate the texts and prepare them before processing. Preprocessing requires the researcher to first discard words and symbols irrelevant to the analysis-such as punctuation, commonly used words ("stop words"), or numbers. Additionally, the researcher may want to remove words that occur at a low frequency in the texts, as they may be irrelevant to the analysis. Finally, the research may recognize different forms of a certain word by stemming or lemmatiza-tion  (Grimmer and Stewart 2013).
What? To study a population, it is often useful to determine its principles of cohesion. MCA allows researchers to inductively create subgroups of individuals characterized by common properties (or modalities of variables). It is a statistical method that synthesizes a set of categorical variables in one or more two-dimensional spaces (e. g. , paper sheets). It summarizes the information contained in a dataset, by showing how certain modalities of variables are frequently found together within statistical individuals. Symmetrically, the method yields cohesive-in that they share common modalities of variables-subgroups in the statistical population, be it composed of international civil servants, experts, international projects, or even international organizations. Since MCA yields cohesive subgroups that have statistically significant modalities of variables in common, it allows the researcher to determine whether these common properties determine a common behavior.
How? To conduct MCA, scholars first need to create a database of the statistical individuals or organizations, with statistical units in rows and properties in columns. Information about properties is then gathered through the Internet (mainly LinkedIn and academic CV) and interviews (when database consists of individuals). Scholars distribute these pieces of information in groups of variables (in columns) that provide a point of view on the population under study, built according to existing literature, ethnography, and interviews. The modalities of variables must be qualitative and concern about the same number of individuals each time. The very description of individuals therefore stems from research choices: a statistical individual may be described by a potentially infinite number of variables.
The variables finally retained are those the researcher assumes to produce effects on individuals' behaviors. Variables that are taken into account and their modalities should be justified and relevant regarding the population studied. The decision to keep or exclude a variable, or to categorize it in a certain way, is a deeply scientific question-as opposed to a technical question-that can totally change the results. On the graph, each group of variables is finally represented with a different type of point. In my work on the OECD, I crafted a database of the 109 experts who signed the OECD reports on health by coding some of their properties with qualitative variables. Information gathered on each expert is distributed into five groups of variables (in columns) distributed into nineteen active variables counting fifty-four modalities and twenty-four additional variables (not taken into account in the MCA) with a total of seventy-two modalities.
For example, since internationalized trajectories or the ability to speak several languages had been outlined as a powerful asset to work in IOs, I decided, in my research, to construct a group of variables on the "cosmopolitan capital" retained by each individual. Each variable included in the analysis is indeed a hypothesis on the structure of the population. Once the dataset is constructed, MCA can be run thanks to different platforms such as the package FactoMineR  (Lê et al. 2008 ) from the statistical software R. 1 Applied to the database, MCA produces a table that gives, for each modality of variable, its contribution to the production of axes. 2 It also produces another table that gives the positions of modalities of variables and the positions of individuals on the graph. Then it plots a two-dimensional graph that must be improved using other packages such as ggplot2, showing the modalities of variables on a plane.
In figure  9 , individuals (in grey) and the most contributing properties (in black) have been represented on the same graph. X axis explains 13. 8 percent of the total variance of the database, and the second one 8. 9 percent. On the western part of the graph, one can see a set of properties that describe the deprivation of academic capital: people on this side of the space have signed the most recent OECD reports on health but have no PhD ("phd_no") and no publication in central academic journals ("central_publi-"). On the opposite part of the graph (eastern part), people have published many articles in very central academic journals ("central_publi++"), are part of many journals' reading committees ("reading_committee+"), and they generally hold a PhD in economics ("phd_eco"). They have signed the first OECD reports on health.
What Challenges? The main challenge of MCA lies both in the construction of the database and the data collection. Most of the time, for individuals, data is collected on the Internet, or through interviews and archives, but with potential biases that must be taken into account, and missing data. R package FactoMineR presents valuable tools to manage such biases. That said, the main challenge stems from the construction of the categories retained in the database that must be carefully justified. Once the MCA has been performed, interpretation of each axis may be tricky. It requires a good knowledge of the population, of the variables taken into account in the database, and a careful reading of the plot. Finally, one must note that statistical software to conduct MCAs can be quite difficult to handle.
Kseniya Oksamytna Data on individual IO officials are important for researchers investigating issues like the political economy of IOs, autonomy and expertise of international bureaucracies, and IO performance and accountability. However, not all IOs provide comprehensive information on all relevant characteristics of their employees. When such data exist, they are often limited in scope (see box t-Navigating Human Resource Statistics). Researchers should carefully consider whether building their own database of individual IO officials' characteristics is worth the investment of resources. Building a database of IO officials' characteristics should begin with an assessment of the existing data or ongoing data collection efforts by other researchers. If the data are indeed unavailable, the next step would typically involve a pilot project to determine the viability of the approach and the scope of the data.
Researchers might reassess the project's viability if they discover that information is classified, incomplete, hard to find, or difficult to categorize. If the pilot is successful, comprehensive data collection can begin. Since information on IO officials' characteristics usually requires data collection rather than interpretation, intercoder reliability is not a significant issue, but members of the research team need to be on the same page about what information should be recorded and in what format. For example, in UN peacekeeping, data on the nationality, gender, and fatalities of military peacekeepers are readily available. There is less information on civilian peacekeeping officials or senior mission leaders, so  Bove, Ruggeri, and Ruffa (2020)  built a database of the characteristics of civilian and military leaders of UN peacekeeping operations, such as nationality, tenure, and prior experience in a similar post.
Their experience dem-onstrates the advantages and costs of this approach: building the database of UN peacekeeping leaders entailed reading numerous UN press releases announcing leaders' appointments, official secretary-general's letters, and media reports for recording leaders' names, nationalities, and start and end months of service. In deciding whether to construct a new database, researchers should weigh potential payoffs against the investment of resources. The main advantage of constructing such databases is that they allow systematic research into questions that could previously be examined only through single or comparative case studies. The creation of a database on peacekeeping leaders, for example, has enabled an analysis of the role of cultural distance between peacekeeping leaders on the one hand and troops or the population on the other hand  (Bove, Ruggeri, and Ruffa 2020) , factors that affect peacekeeping leaders' appointments  (Oksamytna, Bove, and Lundgren 2021) , and the UN Secretariat's efforts to hold leaders accountable for underperformance  (Lundgren, Oksamytna, and Bove 2022).
What? As the "science of interactions"  (Maoz 2011: 6) , SNA is particularly wellsuited for the study of IOs, both with regard to their internal structures and their embeddedness in external networks. After early applications in sociology and anthropology, network analysis grew popular in the late 1960s and early 1970s across a broad range of disciplines. In international relations, it only began to appear in the late 1990s  (Hafner-Burton et al. 2009) , reflecting an increased focus on cooperation and networks. This development has been accelerated in the digital age by the growing availability of online databases and archival sources and the skyrocketing computing power of modern computers, which today allow researchers to store and analyze vast datasets. SNA is based on the premise that actors are not detached from their social context, nor solely driven by their individual characteristics, autonomous from the influence of other actors.
Today, most IOs make huge quantities of data freely available online, providing researchers with an easily accessible goldmine of information, including meeting and voting records, preparatory documents, activity reports, and treaty and case-law databases. Depending on the research objectives, the subsequent data analysis can focus on the overall network, on the position of entities within the network, on subgroups, or on a combination thereof. Network level measures include for example density and centralization, the former indicating the overall level of connections between the nodes, the latter the extent to which a network is focused on specific nodes. Figure  10  proposes sample network graphs showing (1) a centralized, sparse network, (2) a decentralized, dense network, and (3) a fragmented network.
Individuals and MCA A look back at the early IO literature (see  Alger 1970)  suggests that a few systematic studies already early on focused on individuals. Thus  Keohane (1969)  relied on similar sources as those discussed by Oksamytna (box u-Building Databases on Individuals) and Badache (box t-Navigating Human Resource Statistics). In many traditions of IR research, however, such a focus on individuals appeared meaningless. Nevertheless, some pathbreaking work on negotiators by  Jacobson et al. (1983)  illustrated how the behavioral revolution also was of relevance for IR scholars generally and IO researchers more specifically. As much of this individual-level data is quite plentiful, quantitative methods used in the social sciences experiencing the behavioral revolution were deployed to great effect.
Moreover, evaluating materials' typicality is difficult without some view across the whole archive. This view could come from deep and long-standing engagement with an archive and the organization that produced it. Yet when initially exploring an archive, perhaps as part of a new project, researchers may not have this luxury. To address these challenges, social scientists increasingly turn to computational methods  (Grimmer and Stewart 2013). On the one hand, these approaches offer unrivalled strengths in efficiency and consistency. Unlike human coders, they can reliably evaluate many texts against many criteria without tiring. Meanwhile, by simplifying texts to their minimal features, they can identify patterns across the whole dataset that might not be readily apparent through sequential periods of reading, such as the positivity or negativity of texts.
This provides a window onto how a given word or phrase is used in the documents, and a way to gain further qualitative access for either interpretation or disambiguation (for example, between asylum as used in either forced migration or mental health settings and environment to refer to political contexts rather than ecological terms). Our working example involves analyzing a corpus of ninety-three annual reports produced by the International Labour Organization (ILO) between 1919 and 2015. These documents, made available online through the ILO's digital archive, comprise more than eighteen million words. 3  Our unit of analysis are individual reports, meaning that each year is represented by one document. While we did not divide the dataset into subcorpora (such as by decade), depending on the research question and types of archive materials, this is an option worth considering.
The first step involves organizing and tagging the texts in preparation for analysis. This is a nonneutral process, involving choices about types of metadata to attach to the text, the most important units of analysis, and even which software or programming environments to use. We used the Sketch Engine  (Kilgarriff et al. 2014) , an online platform for lexicographic and linguistic analysis, but these techniques are not software-specific and can be implemented in different computing environments and packages. Regardless of the software or tools used to complete the quantitative analysis, we advise recording all the decisions made (and settings used) during the process, if one is not automatically produced. This not only supports reproducible and transparent research for future scholars but also jogs one's memory upon returning to the project.
Data analysis. Before analyzing the data, the conditions and outcome need to be transformed into set memberships, either as crisp or fuzzy sets  (Ragin 2008) , through calibration by using set-membership thresholds from theoretical/substantive grounds. However, calibration can also be done using thresholds based on the distribution of the dataset, particularly for exceptional instances where no theoretical basis exists in the literature. Once the data is calibrated, the next step is building a truth table showing the logically possible configurations with their corresponding number of observations. Thereafter, a necessity analysis should be performed to identify whether a specific condition is always producing the outcome of interest. A sufficiency analysis follows by minimizing the truth table to ascertain whether conditions or combinations of conditions constantly produce the outcome.
Although the analyses can be done manually, it is recommended to minimize the truth table using a software such as fs/QCA, QCA packages for R or Stata, and Tosmana. In my own research, I primarily use fs/QCA because of its more user-friendly interface than other applications. Importantly, a best practice is to perform necessity and sufficiency analyses for both presence and absence of the outcome to determine that configurations showing presence of the outcome are not subsets of configurations reflecting absence of the outcome, and vice versa. Sometimes, truth tables have unobserved rows, known as counterfactuals. QCA allows researchers to account for counterfactuals in the analysis. If we want to include counterfactuals during the analysis, we could include both easy (i. e. , consistent with theoretical knowledge and empirical evidence; for instance, the unobserved rows follow the theoretical expectations of having no cases that should exemplify the configuration) and difficult (i.
Second, SFC also benefits large-N analyses, since the results from the SFC applications could be used for case selection and further theory-testing endeavors. The prime methodology used to analyze large-N data covers the whole range of (usually regression-based) statistical models. More recently, new methodological approaches such as quantitative Social Network Analysis (SNA) and quantitative text analysis have gained ground in IO research. However, quantitative studies still rely on a few larger datasets that have not necessarily been designed to understand intra-IO dynamics (such as the Correlates of War project) or have known inconsistencies (such as the United Nations System Chief Executives Board for Coordination data). Where there is systematic data on organizational structures and dynamics, it is often limited to one or two organizations.
It suggests a strong bias in favor of the Global North, a tendency that comparative research might reveal in other IOs as well. Furthermore, our studies on another international-regional organization, the European Central Bank (ECB), indicate many shared properties, especially regarding masculine domination, academic qualifica-tions in economics, and professional careers in financial institutions, that structure the social space of financial-monetary IOs' leaders (i. e. ,  Lebaron and Dogan 2018). A properly configured prosopographic database also enables geometric data analysis (GDA) for the visualization of connections and relations between individuals, the multiplicity of configurations and specific dimensions that structure a field (i. e. ,  Lebaron 2000; 2008; 2010; Lebaron and Dogan 2016; 2018).
Analysis The analysis starts during data collection and continues through coding, by noting preliminary observations and avenues for further inquiry. A prosopographical database allows quantitative analysis, while it is also possible to provide answers to research questions without using statistical tools. In general, quantifying the results has many benefits such as objectifying descriptions by numbers that can serve as proof for arguments. In our research, we start by an exploratory data analysis before applying more complex methods. Frequency or pivot tables examined together with charts allow researchers to statistically observe the population and the general properties of their social space. Qualitative methods such as biographical case studies focusing on the most representative or marginal individuals are also very useful.
In multicenter MRI studies, pooling the imaging data can introduce site-related variabilities and can therefore bias the subsequent analyses. To harmonize the intensity distributions of brain MR images in a multicenter dataset, unsupervised deep learning methods can be employed. Here, we developed a model based on cycle-consistent adversarial networks for the harmonization of T1-weighted brain MR images. In contrast to previous works, it was designed to process threedimensional whole-brain images in a stable manner while optimizing computation resources. Using six different MRI datasets for healthy adults (n=1525 in total) with different acquisition parameters, we tested the model in (i) three pairwise harmonizations with site effects of various sizes, (ii) an overall harmonization of the six datasets with different age distributions, and (iii) a traveling-subject dataset.