dataesr/scientific-paragraphs-categorization huggingface dataset:
the placing online of bibliographic data accompanied by a DOI link leading to the full-text article; the addition of a proprietary notice; prohibiting: the use of an extract of more than 200 characters from the full text; the modification, translation, or creation of derivative work based on the datasets; the reproduction, retaining or redistribution of the datasets; the extraction or use of the datasets for any commercial activity; the use of robots or other automated programmes, or algorithms for searching; the use of the output generated by the API to enhance institutional repositories in a way that would compete with the final peer-review journal article.
The Government has taken account of the opinion of the Council of State, which considers that the effects of a reference to Article 714 of the Civil Code would be uncertain, due to the lack of sufficient jurisprudence. The wording chosen, resulting from discussions with the Council of State, has the same objective, namely the free reuse of data. " • TDM. The provisions of the Digital Republic Bill in its version of July 2015 incorporated in the Intellectual Property Code an exception to copyright and the right of the database creator in favour of text and data mining. This text was first deleted and then reintroduced by amendments before the National Assembly. Proposals for amending and strengthening this text paving the way for TDM are given below. Proposals to be firmed up, avenues for the future Positive right to text and data mining Guiding principles TDM is a crucial scientific, economic and human challenge for French research.
's technical, economic and legal framework revealed several issues to which positive law offered no satisfactory answers in light of the needs of science: the economic and legal model of scientific publishing no longer corresponds to the technical model of digital platforms; access to and sharing of scientific data as working tools of scientific communities are confronted by clauses on exclusive transfer of intellectual property rights, as well as the principles of copyright and database rights; the ISTEX platform includes value-added services such as the practice of "text and data mining": this enables researchers to use tools such as smart search, data cross-referencing, exploration and transdisciplinary searches. This practice has no legal framework and certain aspects conflict with copyright and database rights.
, it stresses the importance for the scientific communities of identifying in an open, collaborative way the legal obstacles to the ethical sharing of data (intellectual property data and sui generis status of data banks), in order to promote real scientific commons, to integrate the concept of fair research use and participate in the adaptation of data rights to the legitimate interests of research. 31. "Ensuringthe integrity, accessibility and stewardship of research data in the digital age", Report of the Committee of Science, Engineering and Public Policy, Washington, the National Academies Press, 2009. 32. M. Vito, "Partageons nos données" (Let's share our data), Le Monde, 28 May 2014.
" The Government has taken account of the opinion of the Council of State, which considers that the effects of a reference to Article 714 of the Civil Code would be uncertain, due to the lack of sufficient jurisprudence. The wording chosen, resulting from discussions with the Council of State, has the same objective, namely the free reuse of data. " • TDM. The provisions of the Digital Republic Bill in its version of July 2015 incorporated in the Intellectual Property Code an exception to copyright and the right of the database creator in favour of text and data mining. This text was first deleted and then reintroduced by amendments before the National Assembly. Proposals for amending and strengthening this text paving the way for TDM are given below. Proposals to be firmed up, avenues for the future Positive right to text and data mining Guiding principles TDM is a crucial scientific, economic and human challenge for French research.
7 The concept of an "open standard" or "open format" is defined by Article 4 of Act No. 2004-575 of 21 June 2004 as "[a]ny communication, network or exchange protocol and any interoperable data format whose technical specifications are public and with no restriction of access or of implementation". The Act No. 2015-1779 relative to the freedom and conditions of the reuse of public sector information as well as the Digital Republic Bill reaffirm the need to make data available in an "open and easily reusable standard". Open source 8 9 Open licences enable data, databases, digital creations and software to be made available to third parties under licences granting varying degrees of freedom. The most widely used licences, especially as regards open data, are the following: the Etalab licence; the Open Database licence (ODbL); Creative Commons licences; the Public Domain Dedication and Licence (PDDL).
Acknowledgements. This research was funded by  NASA's Terrestrial Ecology and Carbon Monitoring System programs and Brazil's National Council on Scientific Development & Technology (CNPq) Science Without Borders Fellowship Program. Funding for lidar data collection was provided by the  US Department of State, USAID , and the  US SilvaCarbon Program. Lidar data are available from the Sustainable Landscapes Project: http://mapas. cnpm. embrapa. br/paisagenssustentaveis/. Edited by:  S. Zaehle.
Measurement error In our model wages are stochastic because of the type of firm that an individual may encounter, the outside offers or the layoffs that may occur and the productivity shocks. There will also be unexplained variation due to her productivity characteristic x. In the data an additional source of variation is measurement error that we need to account for, so as not to bias the other sources of variation. We use the monthly records of wages in the NLSY. Thus, while it may be reasonable to assume that measurement error is independent from one year to the next it may not be so within the year, as all records are reported at the same interview with recall. Having experimented with a number of alternatives, including a common equi-correlated component across all months, we settled on a measurement error structure that is common within year and independent across years.
The data We use the 1979 to 2002 waves of the National Longitudinal Survey of Youth 1979 (NLSY). The NLSY consists of 12,686 individuals who were 14 to 21 years of age as of January, 1979. It contains a nationally representative core random sample, as well as an over-sample of black, Hispanic, the military, and poor white individuals. For our analysis, we keep only white males from the core sample. We only include data for individuals once they have completed their education. We also drop individuals who have served in the military, and follow workers up to the point of a non-employment spell of 36 months or longer. We consider these workers to have left the labor force. We subdivide the data into two education groups: high school degree or less, and college graduate. The model is estimated separately on these subgroups.
Individuals are interviewed once a year and provide retrospective information on their labor market transitions and their earnings. From this we construct histories at a monthly frequency aggregating the data as follows: we define a worker as employed in a given week if he worked more than 35 hours in the week. We define a worker's employment status in a month as the activity he was engaged in for the majority of the month, treating unemployment spells of two weeks or less as job-to-job transitions. After sample selection, we are left with an unbalanced panel of 2125 individuals (446,747 person months). We remove aggregate growth from wages based on average wage growth in years 10 to 20 from completion of education. At this point the main source of wage growth is due to aggregate productivity.
Choice of moments and identification The model will be estimated based on worker level data recording transitions between jobs and between work and unemployment as well wages over time. We have no information on the firm side itself (such as for example productivity). As such identification is challenging. Lamadon et al. (2014)  discuss the formal non-parametric identification of a model similar to this, albeit without productivity shocks and using matched employer employee data. Other insightful work with more formal discussion of identification is  Hagedorn et al. (2012) , who analyze a version of the model without on-the-job search, and  Bagger and Lentz (2014) , where sorting results from heterogeneous search strategies. The key result is that the complementarities in the worker-job match can be identified nonparametrically in their context, using job-side information on productivity and job duration.
SaM, and  SOLE  meetings in NY. Costas Meghir thanks the  ESRC  for funding under the  Professorial Fellowship   RES-051-27-0204  and the  Cowles foundation and ISPS at Yale. Jean-Marc Robin  gratefully acknowledges financial support from the  Economic and Social Research Council  through the  ESRC Centre for Microdata Methods and Practice grant   RES-589-28-0001 , and from the  European Research Council (ERC)  grant  ERC-2010-AdG-269693-WASP. The authors are responsible for all errors or interpretations.
Materials and Methods Study population. RBCS cases (N = 1,269) came from the database of the Clinical Genetics Department at Erasmus University Medical Centre in Rotterdam, representing the Southwestern part of the Netherlands. First, we selected families that presented with at least two cases of female breast cancer or at least one case of female breast cancer and one case of ovarian cancer in first-or second-degree relatives. In addition, at least one of these two cases needed to be diagnosed before the age of 60. For each selected family, the youngest breast cancer patient who had been tested for BRCA1 and BRCA2 was then assigned to be the index case and included in RBCS. Furthermore, breast cancer cases were also included if they were diagnosed either before the age of 40 years with unilateral breast cancer or before 50 years of age with bilateral breast cancer and did not report a family history of either breast or ovarian cancer in a first or second degree relative.
All index cases and their tested relatives did not carry a BRCA1 or BRCA2 mutation. The median age of the RBCS cases was 44 years (range 18-92 years). RBCS controls (N = 1,159) came from the same database and geographic location as the RBCS cases and included women from cystic fibrosis families who were either spouses of individuals at risk of being carrier of a CFTR mutation or individuals who were tested negative for a CFTR mutation. The median age of the RBCS controls was 41 years (range 10-97 years). BCAC consists of case-control studies of unrelated women  24. For the purpose of the current analyses, only studies with participants of European and Asian ancestry were included, resulting in a total of 45 case-control studies (Supplementary Table  S3 ). Studies with participants of African ancestry (i.
2 ). iCOGS genotyping and imputation. Genotyping of BCAC studies was performed previously using the custom iCOGS Illumina Infinium iSelect BeadChip  24. Briefly, DNA samples from 114,255 BCAC participants were genotyped, along with HapMap2 DNAs for European, African, and Asian populations. Raw intensity data files underwent centralized genotype calling and quality control  24. The HapMap2 samples were used to identify women with predicted European and Asian ancestry by performing principal component (PC) analysis using a set of over 37,000 unlinked markers  25. Nine European PCs and two Asian PCs were found to control adequately for residual population stratification in BCAC data. Samples with a low conversion rate, extreme heterozygosity, non-female sex, or one of a first-degree relative pair were excluded.
Variants were excluded if they were monomorphic or had a call rate < 95% (i. e. when MAF > 0. 05) or < 99% (i. e. when MAF < 0. 05), deviation from HWE (i. e. P < 10-7), or > 2% duplicate discordance. Imputation of genotypes was performed using 1000 Genomes Project data (v3 April 2012 release) as the reference panel  26, 27. To improve computation efficiency we used a two-step procedure which involved pre-phasing by chromosome and by chunk using SHAPEIT software in the first step  28  and imputation of the phased data using IMPUTE version 2 software in the second  29. NBS1 rs2735383 was imputed with an imputation r 2 > 0. 999 in both Europeans and Asians.
e. ≤ 2 and > 2), breast feeding (i. e. no and yes), first-degree family history of breast cancer and receptor status (i. e. ER positive, ER negative and triple negative). Clinical and demographic characteristics of the BCAC cases are presented in Supplementary Table  S5. Association between NBS1 rs2735383 and the clinical and demographic characteristics were evaluated using a χ 2 test. All P-values were two-sided and P < 0. 05 was considered to be statistically significant after correction for multiple testing by the Bonferroni procedure. Logistic regression analyses were performed using SPSS statistics version 23 (IBM Corporation, Armonk, NY) and fixed-effects meta-analyses using Stata version 13 (StataCorp, College Station, TX). Instituto de Salud Carlos III (CEGEN) and grants from the Fondo de Investigación Sanitario (PI11/00923 and PI12/00070) with FEDER funds and H2020 (BRIDGES project).
The CTS was initially supported by the California Breast Cancer Act of 1993 and the California Breast Cancer Research Fund (contract 97-10500) and is currently funded through the National Institutes of Health (R01 CA77398, UM1 CA164917, and U01 CA199277). Collection of cancer incidence data was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885. HAC receives support from the Lon V Smith Foundation (LVS39420). The ESTHER study was supported by a grant from the Baden Württemberg Ministry of Science, Research and Arts. Additional cases were recruited in the.
Acknowledgements We thank all the individuals who took part in these studies and all the researchers, clinicians, technicians and administrative staff who have enabled this work to be carried out. This study would not have been possible without the contributions of the following:  Paul Pharoah  (BCAC),  Andrew Berchuck  (OCAC),  Rosalind A. Eeles ,  Ali Amin Al Olama ,  Zsofia Kote-Jarai  and  Sara Benlloch  (PRACTICAL),  Antonis Antoniou ,  Lesley McGuffog  and  Ken Offit  (CIMBA),  Andrew Lee ,  Ed Dicks ,  Craig Luccarini  and the staff of the  Centre for Genetic Epidemiology Laboratory , the staff of the CNIO genotyping unit,  Francois Bacot ,  Daniel Vincent ,  Sylvie La Boissière  and  Frederic Robidoux  and the staff of the  McGill University and Génome Québec Innovation Centre ,  Sune F.
The content of this manuscript does not necessarily reflect the views or policies of the  National Cancer Institute  or any of the collaborating centers in the  Breast Cancer Family Registry (BCFR) , nor does mention of trade names, commercial products, or organizations imply endorsement by the  USA Government  or the BCFR. The PBCS was funded by  Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. The RBCS was funded by the  Dutch Cancer Society  ( DDHK 2004-3124 ,  DDHK 2009-4318 ). The SASBAC study was supported by funding from the  Agency for Science, Technology and Research of Singapore (A*STAR) , the  US National Institute of Health (NIH)  and the  Susan G. Komen Breast Cancer Foundation. The SBCGS was supported primarily by  NIH  grants  R01CA64277 ,  R01CA148667 ,  UMCA182910 , and  R37CA70867.
Biological sample preparation was conducted the  Survey and Biospecimen Shared Resource , which is supported by  P30 CA68485. The scientific development and funding of this project were, in part, supported by the  Genetic Associations and Mechanisms in Oncology (GAME-ON)  Network  U19 CA148065. The SBCS was supported by  Sheffield Experimental Cancer Medicine Centre and Breast Cancer Now. SEARCH is funded by a programme grant from  Cancer Research UK  [ C490/A10124 ] and supported by the  UK National Institute for Health Research Biomedical Research Centre  at the  University of Cambridge. SEBCS was supported by the  BRL (Basic Research Laboratory ) program through the  National Research Foundation of Korea  funded by the  Ministry of Education, Science and Technology  ( 2012-0000347 ). SGBCC is funded by the  NUS start-up Grant, National University Cancer Institute Singapore (NCIS)   Centre Grant  and the  NMRC Clinician Scientist Award.
Additional controls were recruited by the  Singapore Consortium of Cohort Studies-Multi-ethnic cohort (SCCS-MEC ), which was funded by the  Biomedical Research Council , grant number:  05/1/21/19/425. SKKDKFZS is supported by the  DKFZ. The SZBCS was supported by Grant  PBZ_KBN_122/P05/2004. The TBCS was funded by The  National Cancer Institute Thailand. The TNBCC was supported by: a  Specialized Program of Research Excellence (SPORE) in Breast Cancer  ( CA116201 ), a grant from the  Breast Cancer Research Foundation , a generous gift from the  David F. and Margaret T. Grohne Family Foundation ; the  Hellenic Cooperative Oncology Group research grant  ( HR R_BG/04 ) and the  Greek General Secretary for Research and Technology (GSRT) Program ,  Research Excellence II , the  European Union (European Social Fund -ESF) , and Greek national funds through the  Operational Program "Education and Lifelong Learning " of the National Strategic Reference Framework (NSRF) -ARISTEIA.
Research Centre. This study was funded by the  Cancer Genomics Netherlands  (CGC. nl) and a grant for the  Netherlands Organization of Scientific Research (NWO). J Liu  received a scholarship from the  China Scholarship Council (Beijing, China). BCAC is funded by  Cancer Research UK  [ C1287/A10118 ,  C1287/A12014 ] and by the  European Community 's  Seventh Framework Programme  under grant agreement number  223175  (grant number  HEALTH-F2-2009-223175 ) (COGS). Funding for the iCOGS infrastructure came from: the  European Community 's  Seventh Framework Programme  under grant agreement n°  223175  ( HEALTH-F2-2009-223175 ) (COGS),  Cancer Research UK  ( C1287/A10118 ,  C1287/A 10710 ,  C12292/A11174 ,  C1281/A12014 ,  C5047/A8384 ,  C5047/A15007 ,  C5047/A10692 ,  C8197/A16565 ), the  National Institutes of Health  ( CA128978 ) and  Post-Cancer GWAS initiative  ( 1U19 CA148537 ,  1U19 CA148065  and  1U19 CA148112  -the  GAME-ON initiative ), the  Department of Defence  ( W81XWH-10-1-0341 ), the  Canadian Institutes of Health Research (CIHR)  for the  CIHR Team in Familial Risks of Breast Cancer, Komen Foundation  for the Cure, the  Breast Cancer Research Foundation , and the  Ovarian Cancer Research Fund.
M. C. S. is a  NHMRC Senior Research Fellow. The ABCS study was supported by the  Dutch Cancer Society  [grants NKI 2007-3839; 2009 4363]. The ACP study is funded by the  Breast Cancer Research Trust, UK. The work of the BBCC was partly funded by  ELAN-Fond of the University Hospital of Erlangen. The BBCS is funded by  Cancer Research UK  and  Breast Cancer Now and acknowledges NHS  funding to the  NIHR Biomedical Research Centre , and the  National Cancer Research Network (NCRN). ES is supported by  NIHR Comprehensive Biomedical Research Centre, Guy's & St. Thomas' NHS Foundation Trust  in partnership with King's  College London, United Kingdom. IT is supported by the  Oxford Biomedical Research Centre. The BSUCH study was supported by the  Dietmar-Hopp Foundation , the  Helmholtz Society  and the  German Cancer Research Center (DKFZ).
The CECILE study was supported by  Fondation de France ,  Institut National du Cancer (INCa) ,  Ligue Nationale contre le Cancer, Agence Nationale de Sécurité Sanitaire, de l′ Alimentation, de l′ Environnement et du Travail (ANSES) ,  Agence Nationale de la Recherche (ANR). The CGPS was supported by the  Chief Physician Johan Boserup and Lise Boserup Fund , the  Danish Medical Research Council , and  Herlev and Gentofte Hospital. The  CNIO-BCS  was supported by the context of the VERDI study, which was supported by a grant from the  German Cancer Aid (Deutsche Krebshilfe). The GENICA was funded by the  Federal Ministry of Education and Research (BMBF)   Germany grants   01KW9975/5 ,  01KW9976/8 ,  01KW9977/0  and  01KW0114 , the  Robert Bosch Foundation, Stuttgart, Deutsches Krebsforschungszentrum (DKFZ) ,  Heidelberg , the  Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA) ,  Bochum , as well as the  Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany.
The HEBCS was financilly supported by the  Helsinki University Central Hospital Research Fund ,  Academy of Finland  ( 266528 ), the  Finnish Cancer Society, The Nordic Cancer Union  and the  Sigrid Juselius Foundation. The HERPACC was supported by  MEXT Kakenhi  (No. 170150181  and  26253041 ) from the  Ministry of Education, Science, Sports, Culture and Technology of Japan , by a  Grant-in-Aid for the Third Term Comprehensive 10-Year Strategy for Cancer Control  from  Ministry Health, Labour and Welfare of Japan , by  Health and Labour Sciences Research Grants for Research on Applying Health Technology from Ministry Health, Labour and Welfare of Japan , by  National Cancer Center Research and Development Fund, and "Practical Research for Innovative Cancer Control  ( 15ck0106177h0001 )" from  Japan Agency for Medical Research and development, AMED , and  Cancer Bio Bank Aichi.
The HMBCS was supported by a grant from the  Friends of Hannover Medical School  and by the  Rudolf Bartling Foundation. The pKARMA study was supported by  Märit and Hans Rausings Initiative Against Breast Cancer. The KBCP was financially supported by the special Government Funding (EVO) of  Kuopio University Hospital  grants,  Cancer Fund of North Savo , the  Finnish Cancer Organizations , and by the strategic funding of the  University of Eastern Finland. kConFab is supported by a grant from the  National Breast Cancer Foundation , and previously by the  National Health and Medical Research Council (NHMRC) , the  Queensland Cancer Fund , the  Cancer Councils of New South Wales, Victoria , Tasmania and  South Australia , and the  Cancer Foundation of Western Australia. Financial support for the AOCS was provided by the  United States Army Medical Research and Materiel Command  [ DAMD17-01-1-0729 ],  Cancer Council Victoria, Queensland Cancer Fund, Cancer Council New South Wales, Cancer Council South Australia , The  Cancer Foundation of Western Australia, Cancer Council Tasmania  and the  National Health and Medical Research Council of Australia  (NHMRC; 400413, 400281, 199600).
MBCSG is supported by grants from the  Italian Association for Cancer Research (AIRC)  and by funds from the  Italian  citizens who allocated the 5/1000 share of their tax payment in support of the  Fondazione IRCCS Istituto Nazionale Tumori , according to Italian laws ( INT-Institutional strategic  projects "5 × 1000"). The MCBCS was supported by the  NIH  grants  CA192393 ,  CA116167 , CA176785 an  NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer  [ CA116201 ], and the  Breast Cancer Research Foundation  and a generous gift from the  David F. and Margaret T. Grohne Family Foundation. MCCS cohort recruitment was funded by  VicHealth  and  Cancer Council Victoria. The MCCS was further supported by Australian  NHMRC  grants  209057 ,  251553  and  504711  and by infrastructure provided by  Cancer Council Victoria.