dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion and Conclusions We considered here two databases of fully developed turbulence: flume tank and wind tunnel databases. In each case, there are scaling properties of different turbulent quantities, such as the power spectrum with slope -5/3 and structure functions of Order 2 with slope 2/3 over a given range of scales corresponding to the inertial range (Figures  1  and 2 ). Using these databases, several indicators based on triple correlation functions have been estimated: Figures  4  and 6  show two first indicators proposed, respectively, by Pomeau  [17]  and by Ramsey and Rothman in the finance framework  [19]  (let us note here that the RR indicator from Equation (3) has already been estimated in turbulence by Josserand et al. [15]  in a work devoted to the energy cascade in turbulence).
The fact that the forward and backward terms are not equal is a clear indication of time-symmetry breaking. Figures  4  and 6  show that these indicators have some scaling properties, with different slopes for the flume tank and wind tunnel databases. These slopes are found to be close to one in each case. These indicators of time-reversal asymmetry are scaling in the inertial range: they increase with the scale r and they decrease at a large scale which is close to the outer scale of the inertial range. The slight difference in scaling slopes found here could be explained by differences in Reynolds numbers. The slope close to one of these third-order moments could also be related to the energy balance equation in the analogy with the exponent one found for the third-order structure function in the inertial range.
Data collection and selection criteria An overview of the data collection process is shown in Fig. 1 , where the selection and exclusion criteria are stated. In this study, data from ovarian cancer cases were collected (ovarian epithelium, primary peritoneum or fallopian tubes as primary sites) from reported germline BRCA1 and BRCA2 pathogenic or likely pathogenic variant carriers and individuals who tested negative for germline BRCA1 and BRCA2 (likely) pathogenic variants (noncarriers), with known histology information. Variant class (pathogenic or likely pathogenic) was based on the classification assigned by contributing sources at the time of collection. The main tumour information analysed was ovarian tumour histology, where the histological subtypes ('histotypes') considered were in accordance with the most recent ovarian tumour classification system defined by the World Health Organisation (WHO)  [3].
Only data falling into these histological categories were considered. These included: high-grade serous carcinomas (HGSC), lowgrade serous carcinomas (LGSC), mucinous carcinomas, endometrioid carcinomas, clear cell carcinomas and the 'other' category. The 'other' category comprised rare forms of ovarian cancer not belonging to the above-mentioned subtypes and included tumours defined as: 'other' by data sources not specifying tumour histology; mixed-epithelial carcinomas; carcinosarcomas; transitional cell carcinomas (Brenner tumours); undifferentiated or poorly differentiated carcinomas; squamous cell carcinomas. Data sources included clinically-or research-tested data, as well as data from published studies. Specifically, we initially collected data for 9396 individuals from clinical or research sources, subjected to germline BRCA1 and BRCA2 genetic testing, from the CIMBA (Consortium of Investigators of Modifiers of BRCA1/2)  [36]  and OTTA (Ovarian Tumour Tissue Analysis)  [37]  consortia, the AOCS (Australian Ovarian Cancer Study) study  [38]  and collaborators of the ENIGMA consortium  [34].
Data were collected using a predefined variable template, requesting information on the gene affected, classification of the detected variant, tumour invasion, histology, stage (FIGO), grade, variant nomenclature, ethnicity, age at ovarian cancer diagnosis and age at breast cancer diagnosis (if any). To collect relevant data from published studies, a literature search was conducted within the PubMed database searching for keywords such as 'ovarian cancer' and/or 'ovarian cancer histology' in combination with 'BRCA1 and/or BRCA2 frequency' or 'predisposition' (Supplementary Table  S1 ). A total of 20 published studies meeting the study's selection criteria, comprising 7733 ovarian cancer cases subjected to germline BRCA1 and BRCA2 genetic testing, were used.
Exclusion criteria Of the data collected, sites with a proportion of HGSCs over 90% and/or studies where the selection was applied for BRCA1 and BRCA2 genetic testing based on HGSC or non-mucinous histology were excluded from the dataset to account for potential selection bias  [11, 17, [38] [39] [40] [41]. Sources with a high proportion of unknown histology (≥50%) were also excluded. Overlaps between consortia or study groups (CIMBA, ENIGMA) and published studies were removed. Finally, tumour data of unknown/unclear, inconsistent, adenocarcinoma histology (representing carcinomas that cannot be allocated with certainty within major categories) and serous of undefined tumour grade information, were removed. Data reported as 'other' were comprehensively reviewed when such information was available and reclassified into appropriate categories or excluded.
Statistical analyses Statistical analyses performed in this study are summarised in Fig. 1. As part of the main analysis, ovarian tumour histology was assessed as a predictor of germline BRCA1 or BRCA2 pathogenic variant status by defining likelihood ratio (LR) estimates. Data were grouped for BRCA1 carriers, BRCA2 carriers and non-carriers (BRCA 0 ), and histology prevalence was determined for each group. LRs were calculated for each histological subtype by comparing their frequency between BRCA1 or BRCA2 pathogenic variant carriers and non-carriers: LR ¼ p i p 0 where p i ¼ BRCAi P BRCAi and p 0 ¼ BRCAo P BRCA0 BRCAi = 1, 2 denotes the number of BRCA1 or BRCA2 pathogenic variant carriers, respectively, for a given histological subtype, and BRCA 0 denotes the number of non-carriers for the same histological subtype.
Other races and ancestries, including Hispanic (N = 302) and African (N = 14), did not provide informative predictions due to the low number of tumour data points available. Furthermore, histological associations were refined by other tumour and/or patient characteristics. First, tumour grade characteristics were refined as appropriate for each histotype. Mucinous, endometrioid, and 'other' histological subtypes were categorised as grade 1 (well-differentiated), grade 2 (moderately differentiated) and grade 3 (undifferentiated or poorly differentiated). Serous tumours, i. e. , HGSC and LGSC were already separated according to a two-tier system. Clear cell was not refined by grade, since they are, by definition, high-grade  [3, 43]. Histological subtype data were also combined with known information on tumour invasion (invasive or borderline).
Clinicopathological characteristics The assembled dataset consisted of 10,373 ovarian cancer cases, including 2044 germline BRCA1 carriers, 761 germline BRCA2 carriers and 7568 non-carriers (based on germline testing) (Supplementary Table  S2 ). Patient clinicopathological characteristics are shown in Table  1. The most frequent histotype was HGSC (70. 9%), followed by endometrioid (9. 7%), clear cell (6. 3%), LGSC.
Histotypes failing to provide informative ACMG/AMP evidence, provided statistically significant LR estimates, suggestive of suitability to be included in Multifactorial Likelihood modelling (where there are no limitations set for individual LRs included). Specifically, LR estimates in favour of pathogenicity were identified for the HGSC and 'other' histotypes for BRCA1 and BRCA2 and the endometrioid histotype for BRCA1. Evidence against pathogenicity was also identified for LGSC for BRCA1 and BRCA2. Histological subtype associations also were compared between clinically derived and literature-derived data, to determine any major differences (Supplementary Table  S4 ). Not all subtypes provided sufficient occurrences in this stratified dataset for informative comparisons. Overall, differences in ACMG/AMP code strength were observed for LGSC for BRCA1 and BRCA2 and mucinous for BRCA2, but these estimates, for literature-derived data in particular, were based on a small number of cases in each category, and confidence intervals for LR estimates overlapped.
Evaluation of the associations also were assessed by ancestry, by comparing the results of Asian-and European-ancestry data separately (Supplementary Table  S5 ). Considering that the Asianancestry dataset was much smaller, no meaningful differences were observed in the direction of effect for LR estimates between the two sets, with the exception of the mucinous histotype which was not reported in Asian-origin BRCA1 carriers. Results of the European-origin data alone, agreed with the LR estimates derived in the main analysis, with the addition of LGSC providing evidence against BRCA1 variant pathogenicity (LR: 0. 45 (95% CI: 0. 33-0. 60), supporting evidence).
This weak evidence reflects the high percentage of HGSC in ovarian cancer, irrespective of the presence of germline BRCA1 and BRCA2 pathogenic variants  [1]. Evidence against pathogenicity at supporting strength was derived for BRCA1 and BRCA2 for the mucinous and clear cell histologies. Furthermore, evidence against pathogenicity for some categories, even though they may not reach ACMG/AMP strengths, could be used for inclusion in Multifactorial Likelihood modelling. Sensitivity analyses exploring the heterogeneity within the dataset indicated that the inclusion of clinically-or researchcollected data and data from published sources was unlikely to have caused any major confounding within the dataset. No significant differences were observed when comparing associations between data of different ancestries.
ACKNOWLEDGEMENTS The authors gratefully thank all patients and their families for submitting their data and all the collaborators, researchers, clinicians, technicians and coordinating teams who have enabled this work to be carried out. We acknowledge the  Cyprus Institute of Neurology and Genetics (CING) , the CING institution and the  Telethon organisation Cyprus  for supporting this work. We acknowledge the contribution of the CIMBA (https://cimba. ccge. medschl. cam. ac. uk/) and ENIGMA (https://enigmaconsortium. org/) consortium, members and collaborators. We also acknowledge the contributions of the OTTA consortium (https://ottaconsortium. org/) and the  AOCS Group  (http:// www. aocstudy. org). AOCS gratefully acknowledges additional support from  Ovarian Cancer Australia  and the  Peter MacCallum Cancer Foundation.
ABS and SJR are supported by the  NHMRC Investigator Fellowships  ( APP177524  and  APP2009840 ). iCOGS: the  European Community 's  Seventh Framework Programme  under grant agreement no. 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  ( CRN-87521 ), and the  Ministry of Economic Development, Innovation and Export Trade  ( PSR-SIIRI-701 ),  Komen Foundation  for the Cure, the  Breast Cancer Research Foundation , and the  Ovarian Cancer Research Fund.
OncoArray: the  PERSPECTIVE and PERSPECTIVE I&I  projects funded by the  Government of Canada through Genome Canada  and the  Canadian Institutes of Health Research, the 'Ministère de l'Économie, de la Science et de l'Innovation du Québec' through Genome Québec , and the  Quebec Breast Cancer Foundation ; the  NCI Genetic Associations and Mechanisms in Oncology (GAME-ON) initiative and Discovery, Biology and Risk of Inherited Variants in Breast Cancer (DRIVE)  project ( NIH  Grants  U19 CA148065  and  X01HG007492 ); and  Cancer Research UK  ( C1287/A10118  and  C1287/A16563 ). AOCS was supported by the  U. S. Army Medical Research and Materiel Command  ( DAMD17-01-1-0729 ), The  Cancer Council Victoria, Queensland Cancer Fund , The  Cancer Council New South Wales , The  Cancer Council South Australia , The  Cancer Council Tasmania , The  Cancer Foundation of Western Australia , and the  National Health and Medical Research Council of Australia (NHMRC ;  ID199600 ,  ID400413 ,  ID400281 ).
For the BCFR-NY, BCFR-PA this work was supported by grant  UM1 CA164920  from the  National Cancer Institute. The content of this manuscript does not necessarily reflect the views or policies of the  National Cancer Institute  or any of the collaborating centres in the  Breast Cancer Family Registry (BCFR) , nor does mention of trade names, commercial products, or organisations imply endorsement by the  US Government  or the BCFR. BFBOCC: Lithuania (BFBOCC-LT):  Research Council of Lithuania  grant  P-MIP-22-187. BRICOH: SLN was partially supported by the  Morris and Horowitz Families Endowed Professorship. CNIO: CNIO study is partially funded by  Instituto de Salud Carlos III , project reference  PI19/00640 , cofunded by the  European Regional Development Fund (ERDF) , "A way to make Europe" and the  Spanish Network on Rare Diseases (CIBERER).
CCGCRN: Research reported in this publication was supported by the  Breast Cancer Research Foundation  (project  20-172 ),  National Cancer Institute of the National Institutes of Health  under grant number  R25CA112486 , and  RC4CA153828  (PI:  J. Weitzel ) from the  National Cancer Institute  and the Office of the Director,  National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. CONSIT TEAM:  Associazione Italiana Ricerca sul Cancro  (AIRC; IG2015 no. 16732 ) to P. Peterlongo. CZECANCA: projects of the  Ministry of Health of the Czech Republic  ( NU20-03-00016 ;  RVO-VFN64165 ),  Ministry of Education, Youth, and Sports of the Czech Republic  ( LX22NPO5102 ) and  Charles University  project  COOPERATIO.
Ros Eeles  and  Elizabeth Bancroft  are supported by  Cancer Research UK  Grant  C5047/A8385. Ros Eeles  is also supported by  NIHR  support to the  Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. The FCCC and KUMC cohorts:  The University of Kansas Cancer Center  ( P30 CA168524 ), the  Kansas Institute for Precision Medicine  ( P20GM130423 ) and the  Kansas Bioscience Authority   Eminent Scholar Program. AKG was funded by  R01CA140323 ,  R01CA214545 ,  R01CA260132 ,  5U10CA180888 , and by the  Chancellors Distinguished Chair in Biomedical Sciences Professorship. FPGMX:  A. Vega  is supported by the  Spanish Health Research Foundation ,  Instituto de Salud Carlos III (ISCIII) , partially supported by  FEDER  funds through  Research Activity Intensification Program  (contract grant numbers:  INT15/ 00070 ,  INT16/00154 ,  INT17/00133 ,  INT20/00071 ), and through  Centro de Investigación Biomédica en Red de Enferemdades Raras CIBERER  ( ACCI 2016: ER17P1AC7112/ 2018 );  Autonomous Government of Galicia  (Consolidation and structuring program:  IN607B ), and by the  Fundación Mutua Madrileña  and  Asociación Española Contra el Cáncer  (AECC).
Alignment of LR estimates to ACMG/AMP evidence strengths Data collection from ovarian cancer cases Selection criteria -Carriers of germline pathogenic/likely pathogenic BRCA1/2 variants -Non-carriers, negative for germline BRCA1/2 variants -Tumour origin: epithelium, primary peritoneum, fallopian tubes -Minimum information: histology -iHistological subtypes: High-grade serous, Low-grade serous, Clinical or research-data Published data Mucinous, Endometrioid, Clear cell, 'Other' N = 9396 N = 7733 Exclusion criteria -Data subjected to histology selection for BRCA1/2 testing -Studies with >90% high-grade serous tumours -Studies with >50% unknown histology -Data overlap between sources -Unknown or unclear histology Remaining dataset -Adenocarcinoma -Serous carcinomas of undefined grade N = 10,373 -Non-carriers at germline but somatic BRCA1/2 carriers 2044 BRCA1 carriers, 761 BRCA2 carriers, 7568 non-carriers Likelihood ratio (LR) association analyses Main analysis Assessment analyses on Integration of histology with • Association of histology with BRCA1 and BRCA2 variant pathogenicity • Heterogeneity in data sources • Ethnicity heterogeneity • Tumour grade • Tumour invasiveness • Age at ovarian cancer diagnosis Fig.
Occurrence of coronary artery disease (CAD) Patients who did not achieve non-HDL-C level (but achieved LDL-C level) showed higher CVD risk than those who achieved the non-HDL-C goal or achieved both. Patients who did not achieve both goals showed higher CVD risks than those who achieved either of the goals. The same trends were also seen in patients treated with EPA. 3a  and 3b  for details of the analysis)  [62]. [50]. A similar trend was seen in a Chinese study that demonstrated a positive correlation of non-HDL-C with MI (Odds Ratio 1. 371, 95% CI: 1. 103-1. 704, p = 0. 004), but not LDL-C  [51]. A sub-analysis of a consortium database with 524,444 individuals from 44 cohorts found that 30-year CVD event rates were three to four times higher in patients in the highest non-HDL-C category (57 mmol/L) than among those in the lowest category (26 mmol/L)  [7].
Introduction Digital language equality (DLE) as well as the European data economy rely on the availability, the interoperability and the form of (unstructured, semi-structured, structured) data as a basis for further innovation and improved technological development, especially for trustworthy AI "made in Europe" and powerful language technology (LT) that respects and reflects European values. Data spaces, 2 data sharing and exchange platforms 3 and marketplaces are enablers, key to unleashing the potential of such data. However, data sharing and interoperability are still in their infancy. The diffusion of platforms for data sharing and availability of interoperable datasets is one of the key success factors which may help to drive the European data economy and industrial transformation. The European Digital Single Market strategy that was adopted on 6 May 2015 4 has been built on three pillars: access, environment, and economy & society.
Scope of this Deep Dive This deep dive covers a relatively wide range of technologies in the area of LT, including machine translation (MT, Chapter 40), speech technologies (Chapter 41), text analytics and NLU (Chapter 42) as well as content management and knowledge management systems, text generation, and language learning systems, as data and LRs are the backbone for all these technologies as well as many more. In addition, the area of KGs plays an important role in this deep dive as KGs provide powerful mechanisms and principles to interlink and enrich data in a high-quality manner. KGs can build a powerful and relatively easy to maintain network of interlinked data -including and combining structured, semi-structured and unstructured data -that can be seen as a crucial element of the data infrastructure required to develop future LT solutions, which require not only a single underlying dataset but in addition a wide range of meaningful and contextualised data.
13  Since the development of the Semantic Web, KGs have often been associated with Linked Open Data (LOD) projects, focusing on the connections between concepts and entities (Soylu et al. 2020; Auer  et al. 2018). They are prominently associated with and used by search engines such as Google or Bing; knowledge-engines and personal assistants such as Wolfram Alpha, Apple's Siri, and Amazon Alexa; and social networks such as LinkedIn and Facebook. LT solutions require not only targeted datasets but also high-quality, interlinked, meaningful and contextualised data that can easily be used, quickly expanded and efficiently maintained with reasonable effort. KGs provide these characteristics and contribute to the data and knowledge backbone for LT. Semantic AI -Modern approaches tend to combine statistical AI (ML) and symbolic AI (models like ontologies, knowledge bases for common sense knowledge, and cultural resources, among others).
State-of-the-Art From the start of the open data movement in 2007 with its eight principles of open government data, the requirements of industry data as well as organisation-based data-sharing and collaboration have found their feet and culminated in the next era of data sharing: data catalogues and data portals, as well as, more recently, data spaces and data markets. In the area of LT, data availability, accessibility, aggregation, shar-ing and reuse have received attention since the early 1990s, with associations and organisations providing LR catalogues, like the European Language Resources Association 15  or the Linguistic Data Consortium. 16  Since the early 2010s, several research and innovation projects have contributed to the field including FLaReNet and META-NET with META-SHARE 17   (Piperidis 2012).
In Europe, data spaces are a (relatively) new concept and solution to stimulate the data economy by providing secure and trustworthy mechanisms and platforms for data sharing and data trading. The European Commission lists a number of data spaces in its Data Strategy as of February 2020 41 that is strongly interconnected with the EU Data Governance Act. 42 EU Member States have supported research on data spaces in recent years, as for example Gaia-X 43 and the International Data Spaces initiative (Germany) that channeled into the establishment of the International Data Spaces Association (IDSA) and the publication of several standards and recommendations in the field (IDS Information Model or the 38 https://sagroups. ieee. org/7000/ 39 https://ec. europa. eu/education/education-in-the-eu/digital-education-action-plan_en 40 https://www.
worldbank. org/en/programs/data-use-and-literacy-program 41 https://ec. europa. eu/info/strategy/priorities-2019-2024/europe-fit-digital-age/european-data-s trategy 42 https://digital-strategy. ec. europa. eu/en/policies/data-governance-act 43 https://www. data-infrastructure. eu/GAIAX/Navigation/EN/Home/home. html Reference Architecture Model),  44  or the Data Market Austria (DMA)  45  prototype for a public marketplace for data trading. In January 2023, the European Commission launched the Common European Language Data Space which aims to focus on language data and models discoverability, sharing and trading covering all EU languages and aiming to support a wide range of LT applications in different modalities, domains and contexts.
Main Gaps The following observations have been formulated, collected and further analysed together with researchers and practitioners in the field and reflect our joint understanding of the current gaps in the components of this deep dive. There is untapped potential when it comes to data available in archives as well as old data files. There is a real need for open AI models in LT that are provided to interested parties with open licences. Not only ready-to-use models are required, but also the raw data needs to be made available in order for developers to create their own models. Annotated corpora are often available mainly in English, and it is often the case that they are not available in other languages, let alone all those required for different technologies and applications. The ELG dashboard 46 offers a visual overview of the current standing of Europe's languages (and beyond) with respect to available language data, tools and services.