dataesr/scientific-paragraphs-categorization huggingface dataset:
K-Means Cluster Analysis (k-MCA) A clustering method, called k-means partitioning, was used to cut the dataset into a training set for model generation and a test set for its validation (Table  3 ). The data set is divided into five clusters. Five compounds are selected randomly, one from each cluster, to form the test set (16f, 17g, 18d, 16b and 17h), while the remaining compounds will form the training set. The last one is the key element to generate the model, and the first one was used to validate it.
Multiple linear regression (MLR) Model generation. The model was elaborated using XLSTAT, statistical software, used as add-on for Excel. MLR equation: pIC50 = -3-0. 59*E Gap +1. 27*HLC-0. 033*PSA-0. 015*DE Statistical parameters: R 2 = 0. 66; R 2 Adj = 0. 60; MSE = 0. 18; P value <10 -4 ; F = 11. 23 For the model above, P value is lower than 0. 0001, which means that taking the risk of 0. 01% by considering the null hypothesis (no effect of the descriptors on the activity) as wrong, therefore, we can assume that the model proposed includes variables with a representative amount of information. The higher values of R 2 and R 2 Adj and the lower value of MSE show that the proposed model has a higher predictive ability and reliability. The existence of multi-collinearity among the descriptors was investigated with a parameter called variance inflation factor (VIF), the highest value is less than ten (VIF = 0.
Y-randomization test Y-scrambling is performed on the training set; it is used to confirm that the developed model was not a result of random correlation between the biological activity and the descriptors. In this analysis, the dataset is permuted; the biological activity values were randomly distributed while the descriptors matrix was unchanged, followed by MLR analysis generating new models  [43]. For each randomization and subsequent MLR analysis, we obtain a new set of values for R 2 Rand and Q 2 Rand  [44]  (Table  ST3 ). If the new QSAR models have lower determination coefficient (R 2 Rand ) and leave one out determination coefficient (Q LOO.
New drugs elaboration In order to suggest new efficient compounds, we must select from the series of compounds used in the present work, those with the highest values of pIC50 (1. 37, 1. 38, 1. 17, 1. 10) corresponding to (16a, 16f, 16j and 16k) respectively. These particles will be the object of structural modification in order to design new molecules; their descriptors' values are determined using the same tools as well as pIC 50 values predicted by MLR model proposed. Furthermore, 24 compounds candidates were designed and their parameters were computed. The leverage values (hi) were computed using Matlab software with the following equation: hi = x i T (X T X) -1 x i (i = 1, 2. n) (S4 Table in S1 File). With: x i represents the proposed compounds descriptors' matrix, X represents the test set descriptors' matrix and X T represents the transpose of the test set descriptors' matrix.
Molecular docking Molecular docking study was carried out with the aim of predicting the best conformation of the HIV-1 protease of both types (mutant and wild), on the one hand; combined to the proposed compound as a new efficient drug candidate (ND), on the other hand; combined to an FDA approved drug called Darunavir (DRV). We selected both types of the HIV-1 protease (WT and MT) as receptors. The structures of the wild type (WT) as well as the mutant type (MT) proteases were downloaded from Protein Data Bank (PDB), their PDP codes are respectively: (4LL3-Structure of wild-type HIV-1 protease in complex with Darunavir) (  The interactions between the ligands (ND & DRV) and the proteases were visualized using Discovery Studio (Table  7 ). Active residues interacting with the ligands (ND & DRV) are also disclosed (S5 Table in S1 File).
In any case, the delay caused by waiting is insignificant compared to the acceleration due to vectorization. Furthermore, we leverage neural networks to provide more accurate initial estimate X (0) so that all samples converge as simultaneously as possible, thereby reducing asynchrony, which we will present in Section 4. Once all algorithms are well vectorized, another problem is how to globally coordinate different subroutines. To this end, we add barrier synchronization to the entry points of stability analysis and phase split calculations in Figure  2 , which can avoid any subroutine connected to it proceeding further until all others terminate and arrive at this barrier. We also optimized the code using TorchScript  [22] , allowing for more efficient execution through algebraic peephole optimizations and fusion of some operations, and more practical asynchronous parallelism without the Python global interpreter lock  [34] , whereby vapor-like and liquid-like estimates are dealt with in parallel in stability analysis.
Acceleration of flash calculation using neural networks To further accelerate flash calculation, we create and train two task-specific neural networks, classifier and initializer. The classifier is used to predict the probability p that a given mixture is stable, i. e. , p = classifier(P, T, z), which where y is either 0 for unstable mixtures or 1 for stable ones. The architecture of the classifier is obtained by tuning hyper-parameters using Tree-Structured Parzen Estimator optimization algorithm  [38]  with Asynchronous Successive Halving algorithm  [39]  as an auxiliary tool to early stop less promising trials. We create a dataset containing 100,000 samples (80% for training and 20% for validation), and then tune the hyper-parameters of the classifier with 150 trials to minimize the loss on the validation set (we use Adam  [40]  as optimizer and the batch size is 512), as shown in Figure  4 (b).
Since the optima are not distributed uniformly in the domain, some functions have different kinds of bias, which can be exploited by an algorithm. The question on how to fairly consider different instance generation mechanisms when making use of function combination is thus highly interlinked with questions about how well performance observed on a set of BBOB instances generalizes. Even with these challenges in mind, there are many potential use cases for these affine function combinations. One aspect in which they can prove useful is in the training of algorithm selection models  [11] , as they can significantly increase the size and variety of training data, which is an important consideration towards testing generalizability. One final aspect in which the benchmark data on these function combinations can be further utilized is by linking it back to the exploratory landscape analysis which inspired their creation.
Acknowledging the biases is a first step, but managing them is part of the research (Olivier de Sardan 1995). Since the ethnographic interview is an engaged interaction, it helps researchers confront their own thoughts and prejudices, thus enriching the whole analysis. Typically researchers can ask questions to actors in the field about a phenomenon occurring in real time. Furthermore it can help shape the overall research question by finding out answers to questions defined prior to the observation or give way to reformulate them. This practice thus avoids misinterpretations and misunderstandings by the researcher. In a nutshell, ethnographic interviews not only allow researchers to further open the box  (Maertens 2016)  but are also part of a larger strategy, combining several forms of data production to validate researchers' interpretation of social phenomena (Olivier de Sardan 1995; see part 5-Combining).
e. , income, education, occupation); (3) professional experience (i. e. , previous jobs, contractual status, job satisfaction); and (4) ideational values and preferences (i. e. , economic/political/religious/moral attitudes). Additionally, datasets can be compared and contrasted to assess the (non)specificity of IO professionals in relation to other populations. For example, to understand the social construction of international peacebuilding, Goetze (2017) used web-based survey (WBS) methods to describe the experiences, professional trajectories, political values, and working environments of UN peacebuilders.
Sampling Techniques Often in IO research, scholars are dealing with spatially dispersed and partly undetermined populations, making sampling a challenge. In our case, our interest in assessing diversity drove us to prioritize a global sample of aid professionals working in diverse locations. But how exactly can we go about sampling? Multiple pathways exist, though we view the following two sampling methods as the most ideal: (1) Quota sampling: Use an available dataset cataloguing relevant organizations, such as the Yearbook of International Organizations dataset (UIA 2019), or a self-made dataset, such as the Humanitarian Organizations Dataset (Egger and Schopper 2022) when a ready-made one is not available to then select a representative sample of organizations. Survey distribution will subsequently be negotiated with each chosen organization.
What? Archives are institutional repositories of documents. Archives can be large, professional institutions, or smaller and more ad hoc. They can be operated by national or regional governments, IOs, nongovernmental organizations (NGOs) or other private associations, or hosted by libraries or museums. When we talk about "the archive" we most often refer to a physical location where documents are stored and where researchers can consult material in reading rooms. However, new technology now means that some documents can be accessed online, either through archives' own websites or in digital collections or databases. This chapter primarily discusses traditional, physical archives, where the majority of documents still reside, but similar considerations apply to research using digital archival collections.
What? Rooted in social sciences, visual methods sit at the intersection of two different paradigms: "[they] are research practices that explicitly use images in various ways including drawing, photography, video, film, and Internet pages. The images are either regarded as a source of data in themselves, or as a way of producing data through their use, or a combination of the two"  (Warren 2009: 566). Researchers who work on images as sources of data act as "image collectors": they analyze the significance and meaning of preexisting images, whether produced by an institution or by previous studies. Their methods include the use of visual archives and databases, visual artifacts, websites, institutional documents, or the study of the architecture of buildings and places. On the contrary, researchers who produce visual data are "image generators": they relate to images explicitly generated for their study, whether by the researchers or in collaboration with research participants.
Visual Corpus The constitution and size of the corpus matter. First, determine the purpose of your research and your selected approach in visual culture. If you are interested in an iconic image (like a logo) or a series of posters for a given campaign, then it will be important to focus on the description and detailed analysis of each image. On the contrary, if you aim to identify visual conventions and the emergence of new representations over space and time, then it is better to select a large visual corpus, so you can compare similarities and differences between images. Dealing with a visual macro-corpus can be impractical, so it can be useful to rely on content analysis softwares such as Atlas. ti that allows you to process through description and classification devices. One recommendation would be to start with a smaller corpus on a short period (e.
Images as Data If you plan to analyze images, the first step aims at identifying institutional archives, either physical or digital, by establishing a list of major IOs related to your topic (see also box l-Visual Archives). To find out visual databases, you have three options: whether the organization has a library or documentation center, archive units, or a media center. Many visual databases are either unrecognized or underused by researchers interested in international relations-such as the UNHCR 1  or the ICRC. 2  Many of them are open to the public, but if not you should seek whether there is an exception for scholars. In many cases, a visit to the physical archives or library in the headquarters could be useful. Although using online research engines or databases is helpful, sometimes they do not include everything.
Therefore it is also rewarding to contact librarians working in these organizations, 3  so they can help you identify specific collections and get familiar with the databases. The secondary literature can also be useful (especially in history, ethnography, political sciences, or international relations) to help locate visual materials previously used or mentioned by researchers. Make sure you find visual databases with sufficient information about image producers (e. g. , personal biographies, mandate framework with the IO), 4  the context of images, their dissemination, and possibly the targeted public.
e) Describing the instruments, procedures, and practices of data collection (questionnaires, manuals, response rate), data treatment (databases, metadata, methods of checking data "quality"), and data production, publication, and presentation (use of inferential statistics, estimates, figures, tables. ); as well as the weight in the statistical decisions of managers, subject experts, statisticians, and computer programmers. f ) Investigating the biographies, profiles, networks, discourses, interests, and positions of the actors-NGOs and government members, IO officials, experts, trade unionists, and so onparticipating in the quantification process (personal files, correspondence, interviews). g) Considering other data producers and users in order to understand whether an IO quantification is "influential" and/or "influenced.
Layers of Subjectivity The production process of dataviz implies choices that frame the final discourse: 1. Obvious but often overlooked, by deciding to transform information into dataviz, the authors choose to highlight a specific issue. 2. This issue holds several dimensions (equivalent to columns in the dataset). The example of migrations is telling. We could either choose raw numbers of migrants (flows, balances) or indirect calculated ratios (share of total, trend over time) or qualitative aspects (status, gender, nationality). 3. Each type of dataviz conveys a specific argument. In its FT Visual Vocabulary table, the Financial Times' visual and data journalism team distinguishes core functions through ad hoc designs: deviation, correlation, ranking, distribution, change over time, magnitude, part-to-whole, spatial, and flow.
How? Various ways exist for measuring IO budgets, revenue, and expenditures. For more recent periods, myriad datasets exist with varying levels of detail, accuracy, and coverage. Common, but not necessarily reliable and valid, sources are the Yearbook of International Organizations 1 (includes annual budget figures but without clear-cut budget definitions or expenditure data); the OECD-DAC databases 2 (not always comprehensive and limited time coverage); the financial statistics provided by UNSCEB for the UN system 3 (with consistency issues going back in time); and individual IOs' websites with 1. https://uia. org/yearbook 2. https://stats. oecd. org/Index. aspx?DataSetCode=MULTISYSTEM 3. https://www. unsceb. org/content/un-system-financial-statistics downloadable budget sheets (not necessarily consistent over time).
Recently, the International Aid Transparency Initiative (IATI) has started providing additional data, especially at the project level 4  but also by agency. 5 Often the level of detail in these datasets is insufficient or definitional terms (i. e. , what exactly is included in the numbers provided) are unclear, rendering primary research of official documents necessary. The most reliable and detailed documents for this purpose are audited financial statements. Most IOs produce these, and they can usually be accessed directly from IOs or from relevant national ministries. Annual reports can also be useful but not all include detailed financial data. However, they often provide more contextual information than audited financial statements, which can help improve validity. When working with preexisting databases, it is good practice to check them against official documents to validate both accuracy and measurement, and to ensure that the data appropriately reflect the research question posed.
Understanding what is included in IO budget documents and datasets at different points in time is key for validity of time series, panel, and crosssectional analyses. For example, studying EU finances may require different budget data for different questions: data on commitment appropriations in the EU budget can answer questions about general political priorities, while payment appropriations determine how much member states have to pay into the budget and thus why net contributors care about these in particular. Measuring EU budget outturn, namely actual expenditures, is more interesting for research on budget implementation or on the influence of the European Commission and of national bureaucracies on which beneficiaries ultimately receive EU funds. These examples from the EU are mirrored in other IOs.
What Challenges? The various definitions of what a budget is in different IOs is the starting point for much confusion. Many researchers do not clarify which aspect of IO budgets-revenues, expenditures, projects, and so on-they study, which can obscure what the data actually measures and the extent to which different studies are comparable. The central challenge, however, regards the availability, quality, and comparability of budget data. 6 Constructing a dataset with reliable financial data going past the last decade or so can be fraught with difficulties. While websites of IOs have improved in recent years, with some even making downloadable financial datasets available, finding official figures can be hard unless there exists a dedicated and up-to-date IO budget website. 6. For the UN system see Dag Hammarskjöld Foundation and UN MPTF Office (2018, chapter 3).
One difficulty is simply locating final and authoritative figures. For the UN core budget, the official final figures are decided in a GA resolution. However, complete budget and staff figures as well as political priorities are only found in the draft budget documents. Nowhere are both consolidated into a single public document. In the case of UNHCR, detailed donor contributions are published in audited financial statements while detailed expenditures at headquarters or field level are inconsistently reported over time. Some official expenditure databases include only certain expenditure types, such as project spending, but not all other institutional spending. Contributions from the same donors but to different funds of an IO may be reported in multiple locations; private donations may come from "private" foundations closely linked to governments but are not reported as state support; and other nonstate revenue may only be recorded in aggregate formats, limiting more fine-grained analyses.
Scholars have also developed quantitative tools to count the number and type of IOs. Thanks to these research efforts, IO scholars can now use a variety of databases on IOs that provide characteristics such as membership and institutional design. More recently, scholars have started using quantitative information to study other sets of actors aside from states, such as IO staff and leadership (see box t-Navigating Human Resource Statistics and box u-Building Databases on Individuals) and civil society organizations. In addition, quantitative methods are increasingly used to research IO documents such as recorded speeches and reports (see chapter 17-Computerized Text Analysis). Quantitative methods allow scholars to achieve various research objectives. First and foremost, with numbers, scholars can provide a systematic description of phenomena over time and across many units.
Hence it may be useful to explore how the data varies temporally and spatially. In a third step, we use the available data from UNHCR to determine to what extent authoritarian regimes, human rights violations, and armed conflicts cause displacement using linear regression. As the name indicates, we assume a (log) linear relationship between these variables and displacement. Data for these variables comes from the vdem dataset  (Coppedge et al. 2019 ), 4 the Political Terror Scale  (Gibney et al. 2019 ), 5 and the UCDP Armed Conflict Dataset (v 19. 1)  (Gleditsch et al. 2002; Pettersson, Högbladh, and Öberg 2019). In addition to low and high intensity, we distinguish between interstate and internal conflicts. 6 Finally, we control for GDP per capita and population (World Bank 2019) and add country and year fixed effects.
These statistics are graphically presented on the website and can be downloaded. This database is very useful to make comparisons between UN organizations. However, since they are presented by organizations and by characteristics, they cannot be used for cross-sectional analyses such as studies that address the composition in terms of age and gender. Another limitation of this set of HR data is that they are limited to staff with fixed-term contracts of one year of more. In addition to the above-mentioned limitations, it must be noted that the available data on UN personnel is limited to a small number of sociodemographic attributes (i. e. , age, gender, nationality). In order to collect more human resource data such as education, professional background, and social origins, researchers should use other data collection tools such as conducting biographic interviews (see chapter 6-Biographic Interviews) and create their own database (see box u-Building Databases on Individuals).
What? "Large-N data," here, refers to datasets that quantify IOs, specific features of IOs, or IO activities. These come in several forms. Time-series datasets contain observations about a single unit across time-for instance, how membership in a single IO has changed. Cross-sectional datasets contain observations across many units, often at a single point in time-i. e. , variation in membership across all "currently active" IOs. Panel data combine information of both types-i. e. , how membership across IOs has changed over time. In practice, a range of datasets exist providing different information about IOs, and these can be found either in standalone files online or in the replication files for particular papers. A few of these are discussed later. A scholar may also, of course, collect original data through observations or experiments.
However structured or attained, large-N datasets are widely used for important descriptive purposes. Liesbet  Hooghe et al. (2019)  have, for instance, collected data on the authority of IOs, showing how this has varied throughout the postwar period. But such datasets are also frequently used to make causal inferences by employing them in "quantitative analyses. " In the case of Hooghe et al. , they use their data to explain the growth of IO authority. Different statistical models will typically be used to do this, which depend on the kind of dataset one is working with. Specific models have been developed to handle time-series data or panel data, variables that are continuous or limited in different ways, and so forth. Scholars often develop expertise in working with particular models and make use of different software packages to execute them.