dataesr/scientific-paragraphs-categorization huggingface dataset:
For clusterlevel covariates the patterns differ more by group; for example, a high number of doctors is positively associated with the hygienic latrine ownership only in the intervention group indicating a potential synergism between the number of doctors and the presence of sidesupply markets. We have demonstrated that the IPW can be biased in CRTs if the weights are not implemented as described in  Robins et al. (1995)  and a non-independence working correlation structure is chosen. In particular we raised concerns about the package geeM and geepack implemented in R. These concerns apply not only for outcome data in CRTs but also to longitudinal outcome data, when the probability that an observations is missing at a given time may depend on time-varying covariates  (Tchetgen Tchetgen et al.
2012)  measured at other times. The CRTgeeDR package protects against this bias and allows for adjustment in imbalance in baseline covariates in CRTs. The package can accommodate for a wide range of outcome types, link functions, and working correlation structures. The CRTgeeDR package is easy to use and does not require extensive programming. It therefore makes the augmented GEE (AUG) and the Doubly robust (DR) methodology for CRTs  (Prague et al. 2015 ) more accessible to applied researchers. Compared to existing packages for GEE approaches, users only need specify two more attributes: a model for the propensity score linking the missingness indicator (R ij ) with baseline covariates (X ij ) and the treatment (A i ), and a model for the outcome linking the outcome (Y ij ) with baseline covariates (X ij ).
In presence of rMAR outcomes, if one of these two models is correct regarding the true data generation process, our estimator implemented in CRTgeeDR is consistent (unlike GEE and AUG) and generally more efficient than IPW. If the true data generation process linking the missingness (R ij ) and the observed outcomes of other individuals in the same cluster (Y ij , with j = j) is known, the result above is also valid for MAR outcomes. Finally, although the CRTgeeDR package had been designed for CRTs, it can also be used for analysis of correlated longitudinal data from a randomized trial.
Strategic and operational guidelines … The objective of these Study Review and Proposals for implementation is to present all the scientific communities, parliamentarians, scientific publishers and the public in general with practical ways of implementing the new legal provisions introduced by the Digital Republic Act in the field of digital practices for science. following Articles 30 and 38 of the Digital Republic Act (the part known as the so-called Petite Loi, or minor bill, resulting from the first reading in the Senate 1 ) … These Guidelines report the comments on and analysis of Articles 30 and 38 of the Digital Republic Act, which introduced into French law the legal basis for Open Science by creating: the right for scientific publications to be made available after an embargo period (Article 30); the right to explore and mine data for the purposes of public research via an exception to copyright and to the right of database producers (Article 38).
Main orientations: • Create: Create a right to Open Science guaranteeing free access and free reuse of data from public research. • Balance: Redefine the economic balance of the digital science ecosystem. • Secure: Adopt Article 18b (new) of the Bill for a Digital Republic as suggested by the Joint Commission [new Article 38 of the Petite Loi] creating an exception to copyright and the right of database producers in favour of text and data mining for the results of public research (research articles and data), in order to secure automated data-processing practices and reduce the risk of misappropriation. • Compete: Enable French public research to acquire legal and technical resources that are at least equivalent to those of its European and American counterparts, and in line with the international Open Science movement.
2 The concept of TDM covers a range of real-life situations that should be clarified from a legal, technical and economic point of view. There are considerable issues related to TDM and the levers for fostering its use in scientific research as well as more generally, which are taking science into a new era, involving a necessary framework to cover multiple practices. In order to establish firm foundations for this new situation, we propose analysing comparable practices: the way TDM is organised in the United Kingdom and the United States; the framework suggested in projects funded under H2020, and, finally, the practices that already exist in France. What is text and data mining? Data mining is a recent concept that first appeared in 1989 under the name Knowledge Discovery in Databases (KDD), also known in French as Extraction de Connaissances à partir des Données (EDC).
The term "text and data mining" appeared for the first time in the field of marketing at the beginning of the 1990s. This concept, as applied in marketing, is closely linked to the notion of the "one-to-one relationship" (Michael Berry and Gordon Linoff, creators of Data Mining in Marketing), i. e. the customisation of the relationship between a company and its clients. These Guidelines focus on the application of TDM to science, but TDM is practised in many sectors of activity such as, for example 1 : TDM has been adopted in the sciences in the last few years, with the development of open archives such as arXiv or HAL, so as to optimise the exploration of their databases, which are constantly growing in volume. The notion of text and data mining applied to the scientific field is today widely used to designate a variety of tools and activities.
TDM and the law The consecration of a legal right to TDM via an exception Twofold exception. Article 38 of the Act (Petite Loi) establishes a right to text and data mining by introducing an exception to copyright and to the right of database producers, under the following terms: The Intellectual Property Code is modified as follows: 1° After the second subparagraph of 9° of Article L. 122-5, a 10° shall be inserted as follows: "10° Digital copies or reproductions made from a lawful source, in view of the exploration of texts and data for public research needs included in or associated with scientific results for the needs of public research, excluding any commercial purpose. A decree lays down the conditions under which the exploration of texts and data is implemented, as well as the terms for storage and communication of the files produced on conclusion of the research activities for which they were produced; these files constitute the research data"; 2° After 4° of Article L.
342-3, a 5° shall be inserted as follows: "5° Digital copies or reproductions of the database made by a person with lawful access, in view of text and data mining included in or associated with scientific results in a research framework, excluding any commercial purpose. The storage and communication of technical copies resulting from processing, on conclusion of the research activities for which they were produced, are carried out by organisations appointed by decree. Other copies or reproductions are destroyed. " Lack of a definition. The text does not define the concept of data exploration or mining. When both terms are used in the same text, a comment is called for: the use of the term "exploration" of texts and data in the first part of the text, introducing an exception to copyright, and then of "mining" (fouille) in the second part, creating an exception to the right of the database producer, may raise difficulties of interpretation.
The technological objects necessary for TDM To mobilise text-and data-mining practices, researchers need: input data; processing tools; output data, when processing tools are applied to the input data. The raw material: Input data. Technically speaking, TDM could be described as a process for the extraction of knowledge from selected texts and data, based on mechanisms for the identification of hitherto unknown structures that are scientifically valid and exploitable. Each scientific field (astrophysics, molecular biology, sociology of organisations, marine geology, linguistics, etc. ) has developed its own arsenal of resources and techniques for data collection: sensors, probes, satellites, sequencers, cameras, digitisation, simulation, chemical analysis, and so on, which lead to the accumulation of huge datasets (Big Data).
Overall scientific production is also experiencing spectacular growth due to the increase in scientific activity around the world, exacerbated by pressure to publish. Global annual production of scientific publishing has progressed significantly; for example, between 1996 and 2012 it rose from 1,134,000 to 2,250,000 articles per year on Elsevier's Scopus database. 17 However, "despite its rapid growth, publication is not increasing in proportion to the production of research data": more than 90% of data remain stored on local hard disks and are consequently not shared. The process of scientific analysis requires the use of research data in the broadest sense, concerning both scientific publications and the raw data, and including tables, images, statistical data, sounds, other bodies of text, and in general all the information needed by the researcher.
To do this, depending on the types of data and the proposed objectives, TDM is used alongside techniques from descriptive statistics, data analysis (exploratory statistics) or informatics (artificial intelligence). Text mining uses the same techniques as data mining but it is first necessary to process the textual data with language technologies to make them compatible with the methods used in data mining. The use of data structures adapted to the properties of the texts and semantic algorithms is specific to text mining. The data-analysis methods used (factorial, discriminant, principal components, multiple components, correspondences, etc. ) bring out the different internal dimensions of the datasets, revealing the parameters that show how the data is organised. The classification methods (clustering, unsupervised learning) can identify groups of elements.
At IRSTEA, scientists from the TETIS UMR use innovative TDM solutions in the framework of research conducted in collaboration between IRSTEA, INRA and AgroParisTech: exploring scientific articles to identify new research themes by the enrichment of semantic resources (termino-ontological resources for each speciality, thesauruses, etc. ) or improved tools for epidemiological surveillance in animals. In addition, TDM can identify relationships within heterogeneous datasets in very voluminous corpora that include both scientific and non-scientific texts (datasets and databases, images, etc. ), which has led to the discovery of new and complementary knowledge (https://tetis. teledetection. fr/index. php/fr/). The GESTE UMR (IRSTEA, ENGEES), in Strasbourg (http://geste. engees. eu/), currently has scientists working on the problem of emerging pollutants in water and changes in the behaviour of individuals and artisans to reduce emissions of these pollutants.
This is a major issue in terms of health and the environment, still insufficiently understood in terms of either the risks or the solutions. The use of TDM will enable risk-mapping, in both its scientific and societal aspects, clarification of chronology, and the identification of sub-themes as well as key institutions and actors. 21 INRIA has also developed GROBID (GeneRation Of BIbliographic Data),  22  an automatic learning (or machine learning) tool available in open source. This application can extract, analyse and restructure scientific publications in raw formats (e. g. PDF) into a Text Encoding Initiative (TEI) format. Other tools. Software such as Alceste 23 (developed by the company IMAGE and the CNRS) and Calliope 24 (developed by Astefo) are specialised in lexical analysis. The Gargantext project provides an example of such an analysis.
It can be used to analyse texts according to a paradigm other than complete words: "It smees that teh oredr of the leettrs in a word has no imtorpance. The frist and the last leettr must be in the rghit plcae. The rest can be in a total disrodre and can stlil be read with no proelbm. This menas that we don't raed each leettr in itslef, but the word as a whole. A chagne in the frmae of rference and we trnasopse the rselut to the text itslef: the orede of the words is unmiprtoant compared to the conetxt, which cuonts much mroe: countertexted with Gargantext. "  25  Weka is one of the most widely used TDM applications in the world. It was originally developed by the University of Waikato in New Zealand, and enables data to be viewed and analysed rapidly. Finally, many commercial software applications have been produced by companies, such as RightsDirect,  26 KNIME 27  or RapidMiner.
28 ientific data and articles, which are the primary source of information for analysis by TDM, may be imported from platforms for the sharing of scientific and technical information (such as digital libraries, databases, open archives, search engines, etc. ). Many other platforms for sharing scientific and technical information are listed by the CNRS and described in order to promote initiatives for pooling knowledge and free access to scientific production. 30 Results. Once the TDM process has been applied to the datasets, a result is generated automatically. This result is presented differently depending on the analytical technique used, which depends in turn on the analytical approach chosen by the researcher ("user-generated content"). The result is new knowledge. This can be of several different types and exploited in several different ways: recommendation systems seek to predict behaviour by analysing and filtering the information contained in the data; in science, the links identified between data items are used to recommend new research hypotheses.
The technical operations behind TDM It is also important to understand the technical processes underlying every processing operation in TDM. Atilf. In a note dated 14 May 2014, Jean-Marie Pierrel, representing the University of Lorraine (acting on behalf of the Conference of University Presidents), who is • • • • • • • • • • responsible for the development of value-added services as part of ISTEX and Director of Atilf (Analysis and Computer Processing of the French Language), explained the technical operations carried out by the laboratories in the framework of the development of TDM. In his note, Mr Pierrel makes a distinction between two phases:.
Reduced research costs and more numerous discoveries The economic potential of TDM lies primarily in the reduction of research costs and the possibility of more numerous discoveries. By reducing processing costs and saving time, TDM makes it possible to produce many more new research articles, thus enriching databases, etc. in a "virtuous circle". The Jisc, 35 a British organisation in charge of digital services for research, has estimated that the use and development of TDM for research would increase the productivity of public research without additional cost. TDM would provide the equivalent of 4. 7 million extra hours of research work per year, for the whole of the United Kingdom. In the same vein, TDM enables the development of interdisciplinary research activities. Corpora could be constructed covering a range of subjects, to give access to the latest progress in biochemistry for a biological study, or to reveal historical changes in sociological analyses.
" http://www. assemblee-nationale. fr/14/projets/pl3318-ei. asp What issues does this raise for the work of science? TDM clearly enables scientists to broaden their approach via the automated analysis of scientific literature and all kinds of data; it also makes it easier to organise and structure research projects in France and to rationalise the decision-making processes for setting priorities and allocating budgets; it creates new avenues for the exploitation of research output; TDM can also contribute to public decision-making. Analysing scientific publications and data 2 TDM enthusiasts are especially interested in scientific literature, articles from journals, monographs, theses and reports. All this information expressed in natural language, which is rather unstructured when compared with quantitative data, offers vast and varied reserves.
9 This way of reasoning has been turned on its head: correlations revealed between aspects of the observations concentrated in the datasets suggest hypotheses from which to establish theoretical models of behaviour. Of course, this inductive reasoning does not guarantee the conclusion and can lead to multiple successive iterations. Abductive reasoning, involving logical inference from observations to theory, focuses on the simplest and most probable hypotheses and has been described as "inference to the best explanation" 1. Inference engines using artificial intelligence are often based on this pragmatic abductive approach, which has proved so fruitful for developing TDM software. The use of TDM and the organisation of knowledge sharing, together with the changes in scientific methods, will enable research communities to build new research projects and discover new topics.
Support for public decision-making TDM can help improve decision-making. In the public sector, it would facilitate the development of evidence-based policies (EBP). EBP is a policy approach based on the empirical analysis of situations. This practice, which consists in analysing large factual databases for decision-making purposes, was originally developed in the medical field (evidence-based medicine) at the beginning of the 1990s. 9 This approach then spread to • • other areas of public decision-making, such as the protection of the environment and security. In the United Kingdom, these empirical judgements started to take an important role in public life under the government of Tony Blair. A User Guide was even drawn up by the Overseas Development Institute (ODI) in order to disseminate these practices.
TDM structures and research centres in other countries United Kingdom: The National Centre for Text Mining In the United Kingdom, the National Centre for Text Mining (NaCTeM) is a body financed by the state 1 and overseen by the University of Manchester. It was set up in 2004, initially to address the needs of the university community. NaCTeM was set up to develop tools and services related to text mining. 2  It offers textmining services, software developed by NaCTeM teams or by others, seminars and workshops on TDM, tutorials and demonstrations, as well as publications resulting from text mining. Although this site was initially intended to assist the academic world in data mining, it now has a much wider scope and audience, including industry. 3 eM proposes to develop tailor-made tools and services to meet the needs of researchers in the academic or industrial worlds, when those available on the website are not suitable in the context of a particular project.
4 An example of the services proposed by NaCTeM is FACTA+, which stands for Finding Associated Concepts with Text Analysis in the biomedical field: 5 NaCTeM strives to develop fully interoperable software tools, as the lack of interoperability is one of the greatest challenges facing researchers. 6 The NaCTeM website does not list only the services and tools it has developed itself. TDM services and tools developed by third parties are also listed. NaCTeM also oversees projects 7 relating to text and data mining and lists national and international events on the subject. 8 eM was the first national centre devoted to text and data mining. 9.
12 The ICSU created the World Data System in 2008  13  to promote and facilitate the exchange of data between the members of the WDS. The ICSU World Data System aims to make the transition from separate data centres to a global and interoperable data system, which would incorporate emerging technologies and the new data sciences. The mission of the ICSU WDS is to promote fair and universal access to reliable and good quality scientific information and data, in all disciplines. 14 In September 2016, the ICSU WDS had 98 members. 15 In order to become a member, each candidate must be certified, 16 in particular regarding the criteria of integrity and confidentiality:  17  "As part of the process of developing WDS, a certification procedure for evaluating candidates for membership was developed by the Scientific Committee to ensure the trustworthiness of WDS Members in terms of authenticity, integrity, confidentiality and availability of data and services.
" 14 "Data-Sharing Principles", a sort of ethics charter, were drawn up, and include the principle of respect for the data source and its integrity:  18  "Data, metadata, products, and information should be fully and openly shared, subject to national or international jurisdictional laws and policies, including respecting appropriate extant restrictions, and in accordance with international standards of ethical research conduct. Data, metadata, products, and information produced for research, education, and public-domain use will be made available with a minimum time delay and free of charge, or for no more than the cost of dissemination, which may be waived for lower-income user communities to support equity in access. All who produce, share, and use data and metadata are stewards of those data, and have responsibility for ensuring that the authenticity, quality, and integrity of the data are preserved, and respect for the data source is maintained by ensuring privacy where appropriate, and encouraging appropriate citation of the dataset and original work and acknowledgement of the data repository.
21 The European Commission's strategy in favour of free access to research data is laid out in a report (Open Research Data Pilot 22 ), published in December 2013. According to this document, Horizon 2020 projects must make every effort to enable third parties to access, mine, exploit, reproduce and disseminate the data from their research (free of charge for users). The proportion of research projects on TDM among the projects funded by the European Commission has been calculated following a TDM analysis. Among more than 30,000 projects funded by the European Commission during the period 2007-2016 (FP7 and H2020), approximately 2. 90% were related to TDM (n=885 projects). 25 Some projects (0. 38%, n=115) deal specifically with TDM. They all have the terms "Text Mining", "Data Mining" or "Text and Data Mining" in their descriptions.
• The PREDON project The PREDON project (for PREservation de DONnées, or Preservation of Data), an offshoot of the CNRS's MASTODONS programme seeking to encourage cooperation between different disciplines around the concept of "Big Data", was set up by a small group of researchers from the IN2P3. 28 Its mission is to federate all French initiatives in the field of the preservation of scientific data. The project proposes a new approach based on the scientific, technical and organisational capacity of research units, international partnerships and major computing centres. The PREDON group works closely with similar French and international initiatives, especially with the International Committee for Future Accelerators (ICFA) panel for the preservation of data in high energy physics. In 2014, the PREDON Working Group produced a summary document ("Scientific Data Preservation 2014") briefly describing the contributions of the participants in the workshops it has organised.