dataesr/scientific-paragraphs-categorization huggingface dataset:
| Creations and migrations of economic establishments This work is principally based on SIRENE database enriched by other databases from various sources. SIRENE is a disaggregated database that contains all the economic establishments in France and it is provided by the INSEE. We use the SIRENE database for two time periods, the analysis year of 2011 and the comparison year of 2005. The use of the same database for two years allows us to identify the establishments created or migrated during this period. We define as created, an establishment that was not present in the study area in 2005 but is in 2011, so it is created during the period 2005-2011. A relocating establishment is defined as an establishment which was present in the study area both in 2005 and 2011, but whose postal address has changed during this period  (Nguyen et al.
The former ones are considered as creations, while the latter ones are beyond the scope of this paper. If a firm had an economic activity outside the study area before the analysis period and relocates into the study area, our method would identify its economic establishment as a creation. However, it is expected, based on our analysis framework, that migrating firms from outside the study area should behave similarly to the newly established firms since they do not have any previous experience, attachment or knowledge of the area. The advantage of this method is its transferability. It can be applied to any time period and any location for which data is available. The database contains information for each economic establishment like the economic activity, the postal address, the size in number of employees etc.
In our study, we are taking into account the effect of the social environment by introducing into the model the percentages of the population belonging to the 1st quantile (the poorest) and the 5th quantile (the richest) of the revenue of the study area. We have used the DGI database (INSEE), which gives the distribution of fiscal revenues of each zone, for the year 2005. We expect that some sectors will be positive to the 5th quantile while most of the sectors will be negatively influenced by the 1st quantile.
For the centre, the east and west surrounding areas, we found the data from the official site of the metropolitan territorial authority (data. grandlyon. com). For the rest, we used information from reports of the INSEE, from websites for businesses (e. g. lyon-entreprises. com, zonedactivite. com) and from the communes which dispose a EAZ, in order to find its exact location of the zone. It is expected that this variable has a positive effect. In general, these areas are located near transport axes and create localization or diversity effects so controlling for its effect is crucial.
The proximity to transport infrastructure indicators concern the year 2005 and they are calculated using GIS based on spatial data provided by the IGN (National Geographic Institute of France). The generalized peak times by car and PT were estimated by a four-step transport model developed at the LAET (Laboratoire, Aménagement, Economie, Transport -Transport, Urban Planning, Economics Laboratory). The data of the household travel survey of 2006 is used for the calibration of the model and the estimation of the generalized times, because it is the closest to the initial year of our analysis. The population data comes from the general census (RGP) of 2005 provided by the INSEE. The data for the premises' prices comes from the Callon database (Callon, 2005) , which gives an average price per square metre for offices, boutiques, warehouses, and industrial premises for 2005.
-2011 Creation rate Migrations during 2005-2011 Migration rate Manufacturing 2,395 34% 755 11% Construction 6,788 52% 1,460 11% Wholesale 3,935 47% 1,078 13% Retail 6,039 48% 911 7% FIRE 6,050 54% 1,249 10% Front office services 9,981 48% 2,940 14% Back office services 4,130 49% 989 12% Health 4,304 34% 1,741 14% Source: SIRENE database, authors' calculations.
7% 40. 8% 49. 7% 47. 4% 65. 8% 60. 2% 55. 3% 50. 5% 84. 8% Manufacturing 61. 6% Construction 70. 3% Wholesale 79. 0% Retail 72. 9% FIRE 83. 5% Front office 80. 0% services Back office 73. 8% services Health 90. 6% Source: SIRENE database, authors' calculations.
As patients are isolated, we hypothesized that caregiver costs would be negligible. All resources will be observed, collected though the case report forms (CRFs), and valorized over the period between baseline and the 6 months follow-up. Several sources of unit costs will be investigated for this economic study based on HAS recommendations. Production costs through diagnostic-related groups from the Technical Agency for hospital information (ATIH) will be used for inpatient costs (sources: National Health system and hospital databases). For ambulatory care and examinations, national tariffs are recommended (sources: National databases for medical and paramedical acts, National official list prices for drugs and registers of pharmaceutical specialties, National Table for Biology). Differences in health and costs outcomes' means will be tested by using the Student t test (if normally distributed) and differences between distributions tested with the nonparametric Mann-Whitney U test (if means are not normally distributed).
Data minimization: anonymization versus pseudonymization The GDPR applies to any personal data relating to an identified or identifiable natural person. Article 2 of Act 78-17 of January 6, 1978, known as the French data protection act, clarifies that "in order to determine whether a person is identifiable, all the means available to the data controller or any other person that enable the person to be identified must be considered". Therefore, by definition, personal data and anonymous data are opposed to each other. If there is a link allowing the person to be re-identified, the data is to be considered as personal. The data will be anonymous only when it does not allow the person to be re-identified, including by cross-checking with a new database. This anonymous data is not affected by the regulations on the processing of personal data.
An alternative to these robust anonymization techniques appears to be reinforced minimization, which consists of removing the main identifiers and semi-identifiers (list of HIPAA items in the USA), replacing all the dates by timeframes and generalizing certain variables when possible. A database prepared using this technique should be able to be shared with the scientific community through a third-party platform that guarantees data access and security using processes such as those described by Ohmann et al. [3, 14]. In order to make transparency requirements compatible with personal data protection requirements, in addition to introducing sharing tools, codes of conduct should be put in place to help share health data for scientific research, in accordance with recitals 26, 33 and 50, and pursuant to Articles 14.
Access to medical administrative databases and hospital data warehouses National health data system (SNDS) French health system modernization legislation adopted in January 2016 ratified the creation of a valuable medical administrative database: the SNDS [15]. This is a pseudonymized database containing data on the use of care in private practices and healthcare facilities for the entire French population, as well as data from the hospital program for the medicalization of information systems (PMSI), and from the causes of death registry, and it will soon receive data on disabilities and private health insurance data. These data are henceforth widely available, the quid pro quo being more stringent conditions ensuring data confidentiality and integrity and the traceability of access, as well as feedback to citizens through transparency about the research conducted using these data and the ensuing results.
Regarding access procedures, simplified procedures (RMs) were already introduced in July 2018. If processing does not comply with the RM requirements, approval must be requested from the French data protection authority (Figure  2 ). But the SNDS is above all a formidable stepping stone that will be especially valuable when combined with other sources of clinical, environmental and sociological data. The relaxing of the conditions of use of the directory identification number (NIR [social security number]) should facilitate such combinations, which will have to be processed in very secure environments. In this respect, expectations are high for the "health data hub" prefiguration mission  [16].
The new full-text search and indexing technologies, through semantic enrichment via medical ontologies, make it possible to index and query unstructured textual documents such as medical observations and examination or hospitalization reports, which are a major source of information. These hospital clinical data supplement the data from the national health insurance cross-scheme information system (SNIIRAM), which provide valuable information on the use of care services and the path of patients who have received hospital care (for those patients who did not receive hospital care, diagnoses are not available in this data base). On the scale of a hospital or region, access to these data must be controlled (ethics, regulations, methodology, information to patients, transparency about processing).
The French data protection authority, which issues the necessary approvals, attaches particular importance to data security, information to patients and the governance of these warehouses. The solution implemented at the largest university hospital in France (Paris) and the eHOP project at Rennes university hospital include the creation of a "trusted third-party" structure responsible for analyzing requests, processing them and providing patient data needed for a project, in compliance with ethical standards and regulations. In the near future, these data will be supplemented by data provided by patients (selfquantified data), genetic data (-omics) and data collected routinely (data from monitors, ventilators, electrocardiograms [ECG]). Fast-growing artificial intelligence (AI) technologies will be able to take full advantage of these data, particularly by mining unstructured data in data warehouses.
Real-world studies using healthcare databases, cohorts and clinical trials The possibility of conducting studies using medical administrative databases is already transforming the landscape of clinical research. The Constances cohort, representative of the French population, includes 200,000 volunteers aged 18 to 69, and combines: 1) visits to one of the participating health insurance funds' health examination centers, to have a medical and paraclinical examination and lab tests and, for those aged 45 and over, cognitive and physical tests; 2) a biobank; 3) the use of data from medical administrative databases (national pension fund, health insurance fund, hospitals and healthcare providers); and 4) questionnaires on various topics related to the health status, regularly completed by volunteers (Table  4 )  [17, 18].
The scientific and ethical necessity of data sharing The GDPR must be enforced within the broader framework of the open science movement that is revolutionizing the standards hitherto accepted by scientists by imposing three key scientific values: transparency, openness and reproducibility. Data sharing has an ethical justification. In participating in the studies, the individuals take risks in exchange for uncertain benefits. The result is an implicit agreement whereby the data from these studies must bring the greatest possible benefit to society as a whole. To maximize these benefits, as of January 2019, the International committee of medical journal editors (ICMJE) expects: 1) authors reporting clinical trial results to detail their intentions about sharing de-identified data and associated metadata  [19]  and 2) a data sharing plan to be prepared and recorded before the start of the clinical trial, at the same time as the trial is recorded on a ClinicalTrials.
Reuse of data • The RMs define the possibility of secondary use, with information to the patient. This issue of informing the patient can arise prospectively, when planning a study, to anticipate future uses, or when a database created earlier is reused. The recommendation is to ensure that the initial information given to the patient takes into consideration subsequent studies. In order to minimize its limitations, while preserving the confidentiality of the data, the initial information on reuse should focus on how to access the data rather than on the topic of subsequent studies: who can access the data? What data (what kind of anonymization)? In what ways: controlled access (via a request analyzed by a scientific committee), possibility of aggregating non-downloadable data, sharing after anonymization? Which repository will be used, and what is its governance? Thus, RM-004 could be updated with another change to the individual information: "When the study is based on clear criteria that the data subject has been made aware of in advance regarding the use of his/her data according to the purposes, data access methods and persons who have access, and the data protection measures applied.
" • Promoting the participation of patient representatives in the governance of repositories allowing secondary use of data. • Promoting incentives for data sharing: it seems important for study value indicators to include this sharing, the secondary publications resulting from this sharing, the number of accesses to a database, etc. The GDPR maintains the requirement for patients to be individually informed about the secondary use of their personal data, but there are possibilities of exceptions, especially if the provision of such information proves impossible or would require disproportionate efforts, or if it would seriously compromise the achievement of processing objectives. Insofar as failing to provide information to patients makes the research ineligible for the reference methodologies (RM), these situations are analyzed on a case by case basis by the French data protection authority, based on the file provided by the applicant, when applying for approval (Chapter IX of the data protection act).
Recommendations concerning the fluidity of administrative procedures • It is important to continue developing reference methodologies and avoid limiting them to specific entities (such as RM 005 which applies to health facilities, and de facto excludes universities and other public research bodies). • It is important to propose a framework for defining public interest, a necessary condition for data processing for research purposes in France. • Datasheets must be created to help researchers who want to access the various data warehouses, especially those of the SNDS, including an inventory of existing databases and methods of access.
Improving technical aids • A section is expected to be introduced into the RMs on how to send individual data to peer reviewers of scientific journals to provide secure sites corresponding to these specifications for researchers. • The INDS directory, which records studies conducted using the SNDS, should meet the World health organization's (WHO) requirements for registries  [27] , which are mandatory by law and for publishing. This would avoid authors having to register their studies a second time in a compliant registry approved by biomedical journal editors. These registries must meet several criteria and include the minimum 20 items  [28]. • Some studies require clinical study patients' details to be registered, for example to reimburse costs. However, the French data protection authority recommends complete separation between databases containing personal health data and those containing personal data for reimbursement.
This total separation also concerns university hospital sponsors. University hospitals could declare as processors the administrative services that have patients' contact details for reimbursement purposes. It must be specified whether this organization can fit into the framework of RMs. • The use of SNDS data is governed by a clear regulatory and technical framework. These requirements also apply to studies or data processed from these studies ("extensions"), which complicates the cascading reuse of data from the SNDS. Legislation, as it stands, may be interpreted restrictively, whereby the SNDS may be used for studies, research or evaluations, which would prohibit the creation of data warehouses matched with the SNDS. In addition, these restrictions are no longer relevant when SNIIRAM data used in the extensions become minimal.
subjects and an analysis of the impact on their privacy. The new elements introduced in July 2018 concern in particular the obligation (except in specific cases provided for by the regulations) for the data controller to appoint a DPO, the possibility for a subcontractor of the data controller (processor) to process data directly identifying the data subjects (reimbursement of costs, payments, text message sent to complete a questionnaire), the possibility of processing the Department where the data subject lives, sharing data with independent experts or researchers responsible for re-analyzing the data, particularly at the request of scientific journal editors (technical solution only allowing the data to be read, without being able to extract personal data). Data from extensions of the National health data system (SNDS) must be used in accordance with the requirements of the SNDS provided for in the French public health code.
Shima Volume 13 Number 1 2019 -105 - The objectives of this article are twofold. First, to present briefly the methods implemented to analyse the images of islands produced by television. Second, to introduce how the authors used the audiovisual tool in order to disseminate new representations of islands. Rather than focus on representations specific to the Îles du Ponant, specific attention will be paid to how this approach may be beneficial for Island Studies: ie how can researchers use the media, especially television, to improve knowledge of islands and to promote positive socioeconomic dynamics? We will begin by questioning the role that media, notably television, play in the field of Island Studies. We will then show how we used television reports as data to question representations of island development.
Analysing television representations of the Îles du Ponant In order to analyse the television representations of the Îles du Ponant, we contacted the Institut National de l'Audiovisuel (INA) 7 and compiled a database comprising 21,285 reports dealing with the islands between 1945 and 2015. Among these documents, 60% were from news and current affairs reports. The main feature of the corpus is thus its volume. Rather than undertake a comprehensive analysis of a specific item, this study attempts to obtain a global vision of the representations of islands presented on television. What analytical methods are privileged and what are the interests of such an approach? 4 It is worth noting that only the article titles and abstracts were read, not the entire article. It is thus possible that some articles focusing on the representations of islands are not contained in this inventory.
Shima Volume 13 Number 1 2019 -108 - The primary interest of such a corpus is to conduct quantitative studies based on the information provided in the notes of each television report. Incorporated into a database, these notes make it possible to characterise the reports by identifying their chronological distribution, the channels that produced them, and the periods when they were broadcast. This enables us to give a precise response to different assumptions, such as the assumption that the media perceive islands as highly attractive. It is notable in the latter regard that despite the time, cost and logistic constraints associated with reporting islands, there were more than three reports per day in 2015. The database also allowed us to observe how the volume of reports were distributed across islands.
Standardised patients are people recruited from local populations and extensively trained to portray a Lancet Glob Health 2019; 7: e633-43 predetermined and scripted medical condition to health care providers. In India, standardised patients were first used to understand primary care provider practice in rural and urban India,  16, 17  and their use has since expanded to a variety of settings and health conditions. Standardised patients are increasingly considered the gold standard for assessing the practice of healthcare providers in low income and middleincome countries. 18, 19 ] [22] [23] [24]  To understand gender differences in quality of tuber culosis care, we use the same data from our publication on quality of tuberculosis care in the private sector of two Indian cities. 23  In that study, we documented wide variation in quality of care across providers that was poor ly explained by location and education level, although it was highly responsive to case presentation.
Acknowledgments This study was funded by  Grand Challenges Canada , the  Bill & Melinda Gates Foundation  ( OPP1091843 ), and the  Knowledge for Change Program at the World Bank. SS is supported by the  Center for Operational Research, The Union (Paris, France ). RS acknowledges support from a grant from the  Bill & Melinda Gates Foundation  through the  Arcady Group  ( OPP1154665 ) and a  Doris Duke Clinical Scientist Development Award. MP is a recipient of a Tier 1 Canada Research Chair from  Canadian Institutes of Health Research. We thank  Puneet Dewan ,  Sameer Kumta ,  Shibu Vijayan ,  Sirisha Papineni ,  Nita Jha ,  Vaibhav Saria , and  Amrita Daftary  for useful input on implementation, analysis, and interpretation;  Rajan Singh , Purshottam,  Chinar Singh ,  Geeta, Devender ,  Varun Kumar ,  Anand Kumar ,  Babloo , and  Charu Nanda , who supervised and implemented the ISERDD field work; all the standardised patients for their dedication and hard work; and  Caroline Vadnais  for excellent administrative support.
INTRODUCTION The funding of research in public teaching hospitals in France evolved to an activity based system in 2006. Gradually, the funding system was enlarged to include the other types of hospital, i. e. cancer centres, public non-teaching, not-for-profit private, public military hospitals, and, later, for-profit private hospitals  [1, 2]. The funding of research uses bibliometric indicators such as number of publications, journal Impact Factor (IF)  [3]. The public teaching hospital of Lille developed software (Système d'Interrogation, de Gestion et d'Analyse des Publications Scientifiques  [SIGAPS] ) to identify the publications where at least one author is affiliated to a hospital. This is then used to share ministerial funding (MERRI) of more than 800 million euros per year among eligible institutions.
SIGAPS database In the SIGAPS database, journals are classified into six categories based on the IF distribution within each research discipline, as defined by the JCR Science Edition  [7]. The top 10% are category A (scored 8), those above the third quartile not including the top 10% are category B (scored 6), those above the second quartile are C (scored 4), those above the first quartile are D (scored 3), and those below the first quartile are E (scored 2). Other journals not in the JCR, but which are indexed in PubMed, are categorised NC (scored 1). In PubMed, journals are indexed according to a procedure. The director of the National Library of Medicine (NLM) decides whether to index a journal, based on a review of the scientific quality and scientific policy of the NLM. The NLM Literature Selection Technical Review Committee (LSTRC) reviews and assesses the quality of the journals, while the NLM Board determines the assortment of journals that will be indexed, depending on the relevance to their respective journals.