dataesr/scientific-paragraphs-categorization huggingface dataset:
The  DRIVE Consortium  was funded by  U19 CA148065. The  Australian Breast Cancer Family Study (ABCFS)  was supported by grant  UM1 CA164920  from the  National Cancer Institute (USA). 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 ABCFS was also supported by the  National Health and Medical Research Council of Australia , the  New South Wales Cancer Council , the  Victorian Health Promotion Foundation (Australia)  and the  Victorian Breast Cancer Research Consortium. J. L. H. is a  National Health and Medical Research Council  ( NHMRC) Senior Principal Research Fellow.
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  Australian Breast Cancer Tissue Bank (ABCTB)  was supported by the  National Health and Medical Research Council of Australia , The  Cancer Institute NSW  and the  National Breast Cancer Foundation. The ACP study is funded by the  Breast Cancer Research Trust, UK , and, KM and AL are supported by the  NIHR Manchester Biomedical Research Centre  and by the  ICEP  ("This work was also supported by  CRUK  [grant number  C18281/ A19169 ]"). 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).
The BCEES was funded by the  National Health and Medical Research Council, Australia  and the  Cancer Council Western Australia  and acknowledges funding from the  National Breast Cancer Foundation (JS). For the  BCFR-NY, BCFR-PA, BCFR-UT  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 centers in the  Breast Cancer Family Registry (BCFR) , nor does mention of trade names, commercial products, or organizations imply endorsement by the  US Government  or the BCFR. The  BREast Oncology GAlician Network (BREOGAN)  is funded by  Acción Estratégica de Salud del Instituto de Salud Carlos III   FIS PI12/02125 / Cofinanciado FEDER ;  Acción Estratégica de Salud del Instituto de Salud Carlos III FIS Intrasalud  ( PI13/01136 );  Programa Grupos Emergentes ,  Cancer Genetics Unit, Instituto de Investigacion Biomedica Galicia Sur.
Sample collection and processing was funded in part by grants from the  National Cancer Institute  ( NCI R01CA120120  and  K24CA169004 ). CBCS is funded by the  Canadian Cancer Society  (grant #  313404 ) and the  Canadian Institutes of Health Research. CCGP is supported by funding from the  University of Crete. 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. COLBCCC is supported by the  German Cancer Research Center (DKFZ) ,  Heidelberg, Germany.
Diana Torres  was in part supported by a postdoctoral fellowship from the  Alexander von Humboldt Foundation. The  American Cancer Society  funds the creation, maintenance, and updating of the CPS-II cohort. 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 and the Australian Cancer Database. The MEC was supported by  NIH  grants  CA63464 ,  CA54281 ,  CA098758 ,  CA132839  and  CA164973. The MISS study is supported by funding from  ERC -2011-294576 Advanced grant,  Swedish Cancer Society, Swedish Research Council, Local hospital funds, Berta Kamprad Foundation ,  Gunnar Nilsson. The MMHS study was supported by  NIH  grants  CA97396 ,  CA128931 ,  CA116201 ,  CA140286  and  CA177150.
MSKCC is supported by grants from the  Breast Cancer Research Foundation and Robert and Kate Niehaus Clinical Cancer Genetics Initiative. The work of MTLGEBCS was supported by the  Quebec Breast Cancer Foundation , the  Canadian Institutes of Health Research for the "CIHR Team in Familial Risks of Breast Cancer" program  -grant #  CRN-87521  and the  Ministry of Economic Development, Innovation and Export Trade  -grant #  PSR-SIIRI-701. MYBRCA is funded by research grants from the  Malaysian Ministry of Higher Education  ( UM. C/HlR/MOHE/06 ) and  Cancer Research Malaysia. The NBCS has received funding from the  K. G. Jebsen Centre for Breast Cancer Research ; the  Research Council of Norway  grant  193387/V50  (to  A-L Børresen-Dale  and  V. N. Kristensen ) and grant  193387/H10  (to  A-L Børresen-Dale  and  V.
The Carolina Breast Cancer Study was funded by  Komen Foundation , the  National Cancer Institute  ( P50 CA058223 ,  U54 CA156733 ,  U01 CA179715 ), and the  North Carolina University Cancer Research Fund. The NGOBCS was supported by the  National Cancer Center Research and Development Fund (Japan). The NHS was supported by  NIH  grants  P01 CA87969 ,  UM1 CA186107 , and  U19 CA148065. The NHS2 was supported by  NIH  grants  UM1 CA176726  and  U19 CA148065. The ORIGO study was supported by the  Dutch Cancer Society  ( RUL 1997-1505 ) and the  Biobanking and Biomolecular Resources Research Infrastructure  ( BBMRI-NL CP16 ). The PBCS was funded by  Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. Genotyping for PLCO was supported by the  Intramural Research Program  of the  National Institutes of Health, NCI, Division of Cancer Epidemiology and Genetics.
SEARCH is funded by  Cancer Research UK  [ C490/A10124 ,  C490/A16561 ] and supported by the  UK National Institute for Health Research Biomedical Research Centre  at the  University of Cambridge. The  University of Cambridge  has received salary support for PDPP from the  NHS in the East of England through the Clinical Academic Reserve. 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  National Research Foundation Singapore ,  NUS  start-up Grant,  National University Cancer Institute Singapore (NCIS)   Centre Grant ,  Breast Cancer Prevention Programme, Asian Breast Cancer Research Fund  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. The Sister Study (SISTER) is supported by the  Intramural Research Program  of the  NIH, National Institute of Environmental Health Sciences  ( Z01-ES044005  and  Z01-ES049033 ). The  Two Sister Study  ( 2SISTER ) was supported by the  Intramural Research Program  of the  NIH, National Institute of Environmental Health Sciences  ( Z01-ES044005  and  Z01-ES102245 ), and, also by a grant from  Susan G. Komen for the Cure , grant  FAS0703856. SKKDKFZS is supported by the  DKFZ. The SMC is funded by the  Swedish Cancer Foundation  and the  Swedish Research Council  ( VR 2017-00644 ) grant for the  Swedish Infrastructure for Medical Population-based Life-course Environmental Research (SIMPLER).
The SZBCS was supported by Grant  PBZ_KBN_122/P05/2004  and the program of the  Minister of Science and Higher Education  under the name " Regional Initiative of Excellence " in 2019-2022 project number  002/RID/2018/19  amount of financing  12 000 000  PLN. The TWBCS is supported by the  Taiwan Biobank  project of the  Institute of Biomedical Sciences, Academia Sinica ,  Taiwan. Ascertainment  and data collection for the UBCS is supported by funding from  National Cancer Institute  grants  R01 CA163353  (to N. J. Camp) and the  Women's Cancer Center at the Huntsman Cancer Institute (HCI)  which is funded in part by the  Huntsman Cancer Foundation. Data collection is also made possible by the  Utah Population Database (UPDB)  and the  Utah Cancer Registry (UCR). Support for the UPDB is provided by the  University of Utah ,  HCI , and the  Comprehensive Cancer Center Support  grant  NCI P30 CA42014.
The UCR is funded by the  NCI's SEER Program , Contract No. HHSN261201800016I , with additional support from the  US Center for Disease Control and Prevention's National Program of Cancer Registries , Cooperative Agreement No. NU58DP0063200 , the  University of Utah  and  Huntsman Cancer Foundation. The UCIBCS component of this research was supported by the  NIH  [ CA58860 ,  CA92044 ] and the  Lon V Smith Foundation  [LVS39420]. The UKBGS is funded by  Breast Cancer Now  and the  Institute of Cancer Research (ICR), London. ICR  acknowledges  NHS  funding to the  NIHR Biomedical Research Centre. The UKOPS study was funded by  The Eve Appeal (The Oak Foundation ) and supported by the  National Institute for Health Research University College London Hospitals Biomedical.
Data availability OncoArray summary statistics from the BCAC are available at http://bcac. ccge. medschl. cam. ac. uk/bcacdata/ oncoarray/gwas-icogs-and-oncoarray-summary-results/. Per-sample genotype data, core demographic data and data on diagnosis and pathology can be requested via the BCAC Data Access Co-ordinating Committee (DACC) at http://bcac. ccge. medschl. cam. ac. uk/bcacdata/. OncoArray summary statistics from the PRACTICAL consortium are available at http://practical. icr. ac. uk/blog/?page_id=8088.
The taxonomic relationship of OTU representative sequences was identified by BLASTn searches against the SILVA database (http://www. arb-silva. de, last access: June 2019) version 132. To identify contaminants, 16S rRNA genes from extraction blanks and dust samples from the lab were also sequenced. These 16S rRNA gene sequences from contaminants were used to identify any contaminating bacteria in our samples. All OTUs containing sequences from these "contaminant" samples (< 5 % of total) were removed prior to downstream analysis. The qPCR and sequencing data were then used to quantify the abundance of individual 16S rRNA genes per OTU across the sampled water column, in the different biogeochemical zones. The fractional abundance (percent total sequences per sample) of each 16S OTU was multiplied by the total number of 16S rRNA genes per sample.
Microbial community distribution in the water column of Fohnsee To identify the microbial taxa potentially responsible for mediating the N and C cycling processes identified in the chemical and stable-isotope profiles, we performed highthroughput Illumina sequencing of the V4 hypervariable region of the 16S rRNA genes together with qPCR at selected depths throughout the water column corresponding to the distinct geochemical zones identified in the vertical chemical profiles. Analysis of similarity (ANOSIM) performed on the data revealed that significantly (R: 0. 57, P = 0. 002) different microbial communities inhabited four geochemical zones in the water column: the oxic lake water (6 m); the upper NMTZ (12-14 m); the lower NMTZ (16-18 m); and a methane-rich zone near the lake bottom, where nitrate and nitrite concentrations decreased towards the detection limit (20-22 m) (Figs.
1  and 2b ). Stable-isotope data were used to test the hypothesis that denitrification occurred in zones where methane concentrations decreased. Methane is formed by methanogenesis in the sediments  (Conrad et al. , 2007; Norði et al. , 2013)  and diffuses upwards toward the oxycline. The δ 13 C value of -71. 6 ‰ for dissolved methane at the bottom of Fohnsee (Fig. 2b ) indicates a biogenic source  (Norði et al. , 2013; Rudd and Hamilton, 1978). In the absence of dissolved oxygen (< 0. 625 µmol L -1 ), methane concentrations decreased and δ 13 C values of methane increased to values of -38. 6 ‰ toward the oxycline (Fig. 2b ), providing some evidence for AOM. At this depth interval, nitrate concentrations also decreased and δ 15 N and δ 18 O values of nitrate increased from around 7 to 45 ‰ and from around https://doi.
, 2014). We did not detect any representatives of the ANME-2d in our 16S dataset -despite relatively deep sequencing depth (> 150 000 reads per sample), indicating that, if they were in the lake water, they were at abun-dances below our detection limit. ANME-2d may, therefore, be major contributors to AOM in bioreactor studies  (Haroon et al. , 2013; Shen and Hu, 2012)  and sediments, but not in the water column of this lake. The presence of two separate populations of NC10 bacteria at a water depth between 12 and 22 m, in the region where anaerobic oxidation of methane linked with denitrification also exists, may suggest that this organism was also partially contributing to the anaerobic oxidation of methane with nitrite (n-damo). However, it remains unclear whether Crenothrix, which also peaked in this region, completely reduced dissolved nitrate to N 2 or whether both NC10 bacteria (NO - 2 reduction) and Crenothrix are involved in the N loss processes in a portion of the water column.
Author contributions. FE designed the study. AW performed the field work and the measurements of stable water isotopes. Instrumentation and methodology were provided by  MS  for N isotopes and BM for CH 4. FE performed the modeling study. ÖKC and WDO performed the qPCR. WDO interpreted the data. FE, AW, BM, MS, and WDO discussed the results. FE wrote the original manuscript, supported by  BM  and  WDO. AW visualized the isotope and water chemistry data. Acknowledgements. We thank  Susanne Thiemann  for technical assistance and the staff of the  Research Station Iffeldorf  for the support during fieldwork. Thanks to two anonymous reviewers for their excellent comments and suggestions on an earlier version of the manuscript. Financial support. This research has been supported by the  Deutsche Forschungsgemeinschaft  (grant no.
, 2017b ). To adequately identify potential risks and to establish future research and management priorities the scientific community, governments and other interested parties need well-structured, easily accessible and usable empirical data, often at large temporal and spatial scales. Multiple types, levels and sources of data are currently available, which can be harmonised to make compatible and comparable databases  (GTOS, 1998) , and prepare them to be suitable for model-based analyses. The aim of this paper is to support studies predicting forest responses and vulnerability to climate change by assessing the availability and accessibility of harmonised databases of forest functioning and underlying environmental drivers at the European scale. Firstly, based on a literature review, we identified the main types of forest response to climate change and the underlying interacting drivers.
Forest demography and structure Forest demography can be assessed using vegetation inventories, tree ring data or remote sensing data. Regional, national and continental inventories (see Appendix B) are useful tools to estimate forest demographic processes such as tree growth, mortality and recruitment at the individual tree  (Kunstler et al. , 2016 , Neumann et al. , 2017)  or plot level  (Carnicer et al. , 2014 , Ruiz-Benito et al. , 2017a)  at regular intervals (often each c. 10 years). Recruitment data in systematic inventories have been successfully harmonised for saplings (height between 30 and 130 cm) across single censuses in Europe  (Ruiz-Benito et al. , 2017a; van der Plas et al. , 2018) , but recruitment data contain differential information about tree seedlings. In addition, recruitment data rarely contain time series records, dispersion information or individual tree information required to understand forest responses to climate change.
, 2013). LiDAR can provide sub-metre accuracy of surface heights  (Lefsky et al. , 2002 , Lee et al. , 2010) , although accuracy can vary with canopy height and distribution  (Hopkinson & Chasmer, 2009) , ground slope  (Breidenbach et al. , 2008)  and sampling intensity  (Hyyppä et al. , 2000). Low point density data can be used to calculate stem density, vertical foliage profile  (Coops et al. , 2007)  and basal area  (Lee & Lucas, 2007) , and is a promising method for above ground biomass measurement  (Lefsky et al. , 2002 , Mascaro et al. , 2011 , Simonson et al. , 2016). There is enormous potential to develop large spatial and temporal scale datasets when combining these different data types, e. g. the spatially continuous height, age, biomass and carbon information derived from NFI and MODIS data  (Mäkisara et al.
Species or functional occurrence, abundance and diversity Species or functional type occurrence and abundance data can be calculated from data generally available in vegetation inventories, palaeoecological or remote sensing data. Data on actual species distribution in Europe tends to come from individual field-based observations (e. g. the worldwide database GBIF) and current knowledge (e. g. EUFORGEN or European maps from JRC, see a complete list in Appendix B). The systematic information from NFIs, gathered at regional or national level, and International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP forests gathered at European level) provides large-scale and long-term information about the state of forests (Appendix B). Systematic vegetation inventories provide detailed information on tree species occurrence and abundance (generally through basal area or density measurements) with a good spatial coverage within Europe across biomes but over a relatively short time span (see Appendix B and  (Mauri et al.
, 2017a)  or specific field-based trait measurements  (Vilà-Cabrera et al. , 2015). Plant trait information and plant phylogeny is available for a large number of plants (see e. g. the TRY database, try-db. org, Kattge et al. , 2011 or Zanne et al. , 2014, Appendix B)  and it is being used to further estimate functional or phylogenetic diversity  (Paquette & Messier, 2011). Tree species diversity is not directly available from medium-resolution open-access Earth Observation data such as Landsat or MODIS. However, several studies have demonstrated the potential for predicting species richness and diversity from satellite-derived land cover and landscape complexity (e. g. Honnay et al. , 2003 , Hernandez-Stefanoni & Ponce-Hernandez, 2004 , Ma et al. , 2019) , leaf traits  (Moreno-Martínez et al.
Forest responses Harmonised data on forest conditions is available in multiple global and European scale databases (see Appendix B and a summary in Table  3 ) and range from open-to restricted-access (Table  2 ). For openaccess databases citation and acknowledgment is usually mandatory. For more restricted datasets, the data managers or contributors can request authorship as a prerequisite for access (e. g. some harmonised NFI databases, common garden experiments, Table  2 ). Harmonised data at the European extent is generally of high quality, i. e. well-structured and documented. In some cases, data use does not require a high degree of expertise (e. g. processed or combined remote sensing products), but it requires managing large volumes of data. In others the use of data requires a medium-high degree of expertise as e.
g. when managing unprocessed inventory data, tree ring or palaeoecological data (Table  2 ). The data products of individual observational or experimental studies are increasingly being published online thanks to research networks, public repositories and more recently data-papers gaining increasing attraction. However, whether scientific data should be freely-accessible is under an intense debate  (Gewin, 2016)  and often there is a low replicability, even in journals with an established data policy  (Stodden et al. , 2018). Data available and accessible at European level in data repositories or specific harmonisation initiatives cover many different data types such as trait information (e. g. TRY database,  Kattge et al. , 2011) , plant growth-related experimental responses to environment (i.
, 2015). However, the improvements in the next-generation of sequencing technologies is increasing the availability of open-access databases (  (Neale & Kremer, 2011) , Table  3 , Appendix B). Despite evidence that genotypes respond differently to climate change across the range of the species (e. g. Matías et al. , 2017)  it can be difficult to measure genetic diversity and to incorporate it in predictive models of climate change effects  (Kramer et al. , 2010). For example, neutral diversity does not show direct effects of genetic variation on fitness and, therefore, it is not informative about the adaptative or evolutionary potential of the species  (Holderegger et al. , 2006). However, common gardens and provenance trials are an important source of knowledge on the effects of intra-specific genetic and phenotypic variation on species response to different climates  (Savolainen et al.
, 2012). Further methodological standardisation is emerging in new American (NEON) and European (ICOS) research infrastructures  (Franz et al. , 2018). The availability and accessibility of vegetation inventories depend on the database owner, varying from systematic vegetation inventories (e. g. NFI or ICP forests) to specific databases from research network or data-papers (see Appendix B). Several initiatives to harmonise NFIs are being undertaken, including COST Actions  (Tomppo et al. , 2010) , European projects such as e. g. BACCARA (http://www. baccara-project. eu/), FunDivEUROPE (http://www. fundiveurope. eu/, Baeten et al. , 2013)  or DIABOLO (http://diabolo-project. eu/), and European Networks such as ENFIN (http://www. enfin. info/) or global Initiatives (GFBI, https://www. gfbinitiative.
org). NFI data can be open-or restricted-access at country level but the data require error identification and harmonisation considerations (e. g. minimum tree size or basal area, management,  (Ratcliffe et al. , 2016) ) and harmonisation of heterogeneous databases as country-level NFIs should include standardisation steps to the final outputs. Harmonisation initiatives are resulting in the availability of NFI data at the European level, such as species occurrence  (Mauri et al. , 2017)  or forest structure  (Moreno et al. , 2017). ICP plots include information about biodiversity and the health and vitality of forests, for example canopy affectation by defoliation or/ and climate change interactions with other air pollutants  (de Vries et al. , 2014 , UNECE & ICP Forests Programme Co-ordinating Centre, 2016).
The main data limitations are based on the temporal coverage of the data (available since the 1980s) and the importance of understanding the knowledge any sampling effects that might include the underrepresentation of large trees, differential plot sizes and time intervals. Tree ring data are harmonised at global scale by NOAA's "International Tree Ring Data Bank" (ITRDB, Table  2  and Appendix B). The ITRDB provides long-term growth information (usually tree-ring widths but also tree-ring density data) at tree, stand and species levels that can be freely downloaded. However, most of the ITRDB data refer to classical dendrochronological data, i. e. cross-dated tree-ring series obtained from 10-20 dominant and climatically sensitive trees of the same species living in the same site, stand or tree population; often at climate-sensitive sites.