dataesr/scientific-paragraphs-categorization huggingface dataset:
METHODS, PROTOCOLS AND PROCEDURES AND UNCERTAINTY BUDGETS C5 International worldwide cooperation on all levels (e. g. , agencies, research institutes, experts, etc. ) is imperative in order to ensure high quality data for global climate and coastal and inland water environmental monitoring. Different protocols existing for OCR data validation all over the world shall be harmonised, understood and applied in a consistent manner to ensure global uniformity of measurements. C6 Data (including appropriate metadata) and expertise collected over years by the international community shall be acknowledged, preserved and passed on to the next generations. C7 Principles of good practice in performing measurements shall be documented and their application encouraged. C8 Practical consolidated examples on compiling uncertainty budgets shall be provided.
PROPERTIES OF OCR C11 The performance of OC radiometers must reflect the needed accuracy for satellite OCR data validation and correspond to requirements as identified and established by the international community in the field. Community consensus on practically feasible requirements is needed. However, the principles of metrology-SI traceability and uncertainty -must be followed. C12 A document, setting minimum requirements for the most important properties of radiometric instruments used for satellite OCR validation, is needed. Preparation of such a document should be encouraged and funded by ESA and other space agencies or entities. C13 Vital components and specifications for new generation instruments shall be identified and characterisation capabilities of required metrology infrastructure shall be developed accordingly.
COMPARISON EXPERIMENTS AND DATABASE OF OCR FIELD RADIOMETER PERFORMANCE C17 Periodic comparison experiments are needed for validation of established methods and uncertainty budgets at all levels of the traceability chain. C18 Comparison experiments also serve the purpose of training, sharing experience, and facilitating common understanding and interpretation of the measurement protocols. C19 Application of unified data handling or a community processor will reduce overall uncertainty and improve agreement between individual datasets, although care not to limit innovation must be ensured. C20 Worldwide international participation of agencies and research organisations in comparison exercises shall be aimed for.
OPTIONS FOR LONG-TERM FUTURE EUROPEAN SATELLITE OCR VICARIOUS ADJUSTMENT C22 Operational FRM infrastructures to underpin SVC with SI traceability, full uncertainty characterisation and the best possible accuracy and precision are mandatory. Such FRM infrastructure of the quality needed for SVC shall be redundant in order to ensure steady and sufficient data provision. C23 BOUSSOLE as the existing unique SVC site in Europe must be maintained in the long term and upgraded to full operational status. C24 Development and long-term operation of a second new European infrastructure for OC-SVC in a suitable location to gain ideal SVC conditions and ensure operational redundancy is needed. A12 Upgrade BOUSSOLE to fully operational status. A13 Develop a new infrastructure based on MOBY-Net and/or new European technology in a suitable location, e.
Bioinformatics analysis of small RNA libraries The quality of the fastq files was assessed with FastQC software (www. bioinformatics. babraham. ac. uk/projects/fastqc/). Low-quality bases and adaptors were trimmed from each read using the Cutadapt program and only reads showing an acceptable quality (Phred score, 20) were retained. A second set of graphics was generated by the FastQC software using the trimmed fastq files. Reads were mapped to the genome sequences of SINV (GenBank: NC_001547. 1) using Bowtie (https://sourceforge. net/projects/ bowtie-bio/files/bowtie/1. 0. 0/). The default parameters for small RNAs were used, with the exception that one mismatch was allowed between each read and the target (the Àv 1 parameter). Bowtie generates results in the Sequence Alignment/Map (SAM) format.
All SAM files were analyzed using the SAMtools package to produce bam indexed files. Homemade R scripts with Rsamtools and Shortreads in the Bioconductor software were used for the analysis of the bam files to create graphs. The number of small RNA reads mapping to the virus was normalized by the total number of reads in each small RNA library. RNA transcriptome sequencing and analysis F1 females (from SINV or mock primed F0 female flies, see Transgenerational immune priming protocol) were crossed with their sibling males to obtain the F2 generation. The F2 flies were infected with 100 PFU of SINV/fly or mock infected. Two days later, RNA was extracted from 3 pools of 4 flies per condition for each sample. Libraries were prepared with the Illumina TruSeq RNA Library Prep kit. Libraries were diluted to 4 nM and sequenced using a NextSeq 500 High-Output Kit v.
ACKNOWLEDGMENTS We thank members of the  Saleh lab  for fruitful discussions,  Lluis Quintana-Murci  and  Jared Nigg  for critical reading of the manuscript, and  Adriana Va  ´zquez for the execution of the graphical abstract. This work was supported by the  European Research Council  ( FP7/2013-2019 ERC CoG 615220 ) and the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases  (grant  ANR-10-LABX-62-IBEID ) to M. -C. S. J. A. M. was supported by  AXA Research Fund.
Results Transcriptomics-based Disease Molecular Similarity Network. To establish patient similarity networks, we collected gene expression data from microarray assays for 6284 patients (and 3887 controls) suffering from 132 diseases and 3 lifestyle conditions (smoking, aging, and exercise) with relative controls. It is worth mentioning that each of the used freely available dataset was conceived for the analysis of a single disease, and so, we cannot know if the patients are suffering from additional diseases. We identified differentially expressed genes (DEGs) by comparing each case sample (from now on patient) to all the control samples from the same study (Fig. 2 ). We then looked for molecular similarities among patients, based on the significant overlap between the top 500 up-and downregulated genes in each of them  23  (the procedure was repeated selecting different numbers of DEGs showing this to be the optimal choice; see "Methods").
1 ). On the other hand, it showed a lower overlap with comorbidity interactions involving the diseases of the blood and blood-forming organs. This may be due to a higher variability in cell composition in blood samples, which presents, among other, seasonal and even diurnal variations  27. Since the relative molecular similarity calculation takes into account the size of the universe of analyzed diseases, larger datasets would be needed to better test the matching of our molecular similarity interactions with epidemiological comorbidity datasets. We then investigated whether our positive interactions (pRMS) overlapped interactions from a directed disease network, namely the disease-pairs underlying temporal disease trajectories, obtained by Jensen et al. 22  mining clinical data on 6. 2 million patients over 14.
Congenital anomalies Diseases of the nervous system and sense organs Fig. 4  Epidemiologically described disease comorbidities present in the Disease Molecular Similarity Network. Disease-pairs extracted from the Temporal disease trajectories (gray edges) showing which pairs are also detected in the transcriptomic-based comorbidity relations identified by our approach (blue edges). Since the ICD10 codes might involve several diseases, we indicate the specific names of the diseases we are analyzing using transcriptomic data involved in pRMS interactions. Inset: The overlap is statistically higher than what would be expected from randomized datasets (see "Methods"). derived from expression data could recapitulate previous epidemiological results, at least in part, potentially being useful in the discovery of the molecular bases of direct and inverse comorbidity relations between diseases.
47. To this end, we compared patients' differential expression profiles with those reported in the LINCS L1000 library (see "Methods"). This database records the gene expression profile changes induced by thousands of compounds on a large panel of cell lines, allowing the user to estimate which compound could generate a given gene expression alteration profile. We added LINCS drugs as nodes in our SCN and investigated whether specific patient-subgroups have any common drug associations. If the changes generated by the drug were similar to the ones observed in specific patient-subgroups, the drug could be responsible for such patterns. On the other hand, if the changes were opposite to the ones observed in patient-subgroups, the drug could serve to treat those patients specifically, opening the way to drug repurposing  47, 48.
Methods Gene expression analysis. Gene expression raw data (CEL files) were downloaded from the Gene Expression Omnibus (GEO, GSE* files http://www. ncbi. nlm. nih. gov/geo) and ArrayExpress (EMTAB* files https://www. ebi. ac. uk/arrayexpress/) for 132 diseases, 2 lifestyle conditions (smoking and physical activity) and ageing, including 186 datasets (Supplementary Data 4). Studies conducted on HG U133Plus2 Affymetrix microarray platform were selected to allow using the frozen Robust Multiarray Analysis normalization method  54  and reduce the bias due to inter-platform differences. The linear regression model provided by the LIMMA package was used to identify differential gene expression  55  , comparing each sample case (from now on denoted as patient) with all the control samples from the same study.
Different drug concentrations and exposure time to the drug were not taken into account. In the LINCS L1000 data some drugs (pert_iname) are represented by different molecules, (pert_id) usually from different vendors. In these cases, we obtained the pert_id associated signatures, that is, associated to the molecule, and a consensus signature in which all the pert_id corresponding to the same drug were considered. In this last case, the pert_id was also taken as a confounding variable. The t-moderated statistic was used as a measure of the expression of the gene. It was preferred over the logFC because the t statistic takes into account the sampling variance. However, both statistics were highly correlated in all the signatures tested. In order to measure the similarity of each patient signature to a given drug signature, the enrichment of the top 250 upregulated and downregulated genes by the drug was determined in the patient signature using a pre-ranked GSEA, as previously done by Iorio et al.
48. The fgsea R package was used  60. A consensus Enrichment Score (ES) was obtained subtracting the ES values of the DN signature from those ES of the UP signature. Personalized comorbidity profiles. For each patient we calculated the pRMS and nRMS with each of the analyzed diseases based on transcriptomic similarities with other patients, as done before with diseases and patient-subgroups, producing a ranked disease list from the most similar to the least. Then, for each disease we added LINCS drugs and ranked them from the one similar to most patients to the one similar to the least, highlighting the first-line treatments (https://www. vademecum. es). As a final step, we look for examples where a patients' first-line treatment might be causative of increasing the risk of developing the most probable secondary diseases (Fig.
6 ), i. e. drugs that are positively connected to most patients of the secondary diseases. Disease PERCEPTION portal. The portal is composed of a database loader, an SQL database, a REST API and a web frontend. The tabular data and the source code of the database loader, REST API and web frontend are available at the GitHub project https://github. com/inab/disease_perception. The database loader is written in Python 3. 5, and it uses pandas  61  and SQLite to prepare an SQLite database instance. The SQL database is composed of 16 tables, with the disease groups, diseases, patient-subgroups, patients, studies, genes, drugs and their relationships. The data loaded comes from all the results consolidated from the analyses previously described. The REST API is written in Python 3. 5, and it uses Flask, Flask-RESTPlus and Flup.
It is available at http://disease-perception. bsc. es/api/, and it is documented using OpenAPI. The Disease PERCEPTION web frontend is written in Javascript ES7/ES2016, and it uses Cytoscape. js  62 , the external layout plugins COLA, COSE-Bilkent, Dagre and Klay, JQuery, Bootstrap, Tippy and Popper. It is built using yarn, babel and webpack, as described in its documentation on the GitHub repository. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements We thank  Miguel Ponce de León  ( Barcelona Supercomputing Center ) for the helpful discussions and for building up the Disease PERCEPTION database. We also thank  Alba Lepore  ( Barcelona Supercomputing Center ) for critical reading of the manuscript and helpful discussions, and  Eduard Porta  ( Barcelona Supercomputing Center ) and  Davide Cirillo  ( Barcelona Supercomputing Center ) for critical reading of the manuscript. This work was supported by a  Ph. D. Fellowship  ( BES-2016-077403 ) and funded by the  Spanish Ministry of Economics and Competitiveness  ( BFU2015-71241-R ,  RTI2018-096653-B-I00 ). The Coordination Node led by A. V. at the  Barcelona Supercomputing Center (BSC)  is a member of the  Spanish National Bioinformatics Institute (INB) ,  ISCIII-Bioinformatics  platform and is supported by grant  PT17/0009/0001  of the Acción Estratégica en Salud 2013-2016 of the  Programa Estatal de Investigación Orientada a los Retos de la Sociedad , funded by the  Instituto de Salud Carlos III (ISCIII)  and  European Regional Development Fund (ERDF).
V. P. is supported by  INSERM , the  Fondation Toulouse Cancer Santé and Pierre Fabre Research Institute as part of the Chair of Bioinformatics in Oncology of the CRCT. A. B. has received funding from the  Excellence Initiative of Aix-Marseille University-A*Midex , a  French "Investissements d'Avenir" program. D. J. was supported by  Juan de la Cierva fellowship  ( FJCI-2016-29558 ) from the  Ministerio de Ciencia, Innovación y Universidades. H. T. and F. A. -S. were supported by the  Instituto de Salud Carlos III (ISCIII) ,  Spanish National Bioinformatics Institute (INB)  Grant ( PT17/0009/0011-ISCIII-SGEFI/ERDF ) and  Marie-Curie Career Integration Grant  ( CIG334361 ). J. M. F. and S. C. -G. were supported by the  Instituto de Salud Carlos III (ISCIII) ,  Spanish National Bioinformatics Institute (INB)  Grant ( PT17/0009/0001-ISCIII-SGEFI/ERDF ).
Data availability All data needed to understand and assess the conclusions of this research are available in the main text, supplementary materials and Disease PERCEPTION portal (http://diseaseperception. bsc. es). The raw datasets (whose identifiers are provided in Supplementary Data 4) are publicly available and can be downloaded from ArrayExpress (https://www. ebi. ac. uk/arrayexpress/) and GEO (http://www. ncbi. nlm. nih. gov/geo).
RT-qPCR For human tumors analyses, mRNA expression levels were assessed by quantitative RT-PCR using Fluidigm 96. 96 Dynamic Arrays and specific TaqMan predesigned assays (SIGMAR1 = Hs00195337_m1) (Life Technologies, Carlsbad, CA, USA). Data were calibrated with the RNA ribosomal 18S and changes in mRNA expression levels were determined using a comparative CT method using 5 normal tissue.
Accounting for proxy uncertainties in a comprehensive and transparent manner requires not only expert knowledge but also the availability of extensive metadata in addition to the proxy data. However, due to a lack of standardisation and inconsistent archiving of metadata, synthesising palaeoclimate data in a way that allows for robust uncertainty assessment remains challenging and time consuming. Efforts are underway to alleviate these challenges. The largest palaeoclimate data repositories (World Data Service for Paleoclimatology, operated by the national centres for environmental information (NCEIs) at NOAA, and PAN-GAEA) are both striving for more standardisation and to store data in (more) machine-readable formats. In addition, standardisation is progressing through the use of existing data formats from other communities (netCDF;  Langner and Mulitza, 2019)  as well as the implementation of new data formats specifically targeted to palaeodata (Linked Paleo Data (LiPD);  McKay and Emile-Geay, 2016).
At the same time there is ongoing discussion on data and metadata requirements and standards  (Khider et al. , 2019). Traceability of datasets is also improved through data citations, not only ensuring that data producers receive proper credit for their work but also allowing for better linking of different datasets. Nevertheless, these initiatives have only recently been emerging and the majority of the palaeoclimate data remains inconsistently formatted, non-standardised and scattered over various data repositories. The need for synthesis products and documentation of potential synthesis approaches is therefore as large as ever. Here we present the first version of a new multi-proxy marine palaeoclimate data synthesis that covers the past 130 000 years developed within the German climate modelling initiative PalMod  (Latif et al.
In Sect. 3 we provide general information on palaeoclimate proxies from marine-sediment archives that is used to guide the metadata selection. Section 4 details the structure of the database, and the contents of version 1. 0. 0 are outlined in Sect. 5. The formats of the data product, future plans and versioning are described in Sects. 6 and 7. Section 8 describes where the data can be accessed. In the last section, Sect. 9, we reflect on the data synthesis effort and provide recommendations for data archiving and data rescue.
Data synthesis strategy Our data product focuses on time series from marinesediment archives. A single marine-sediment archive (sediment core) can be used for measurements of different parameters, each providing information on different aspects of the environmental conditions at the time of deposition. However, for the purpose of analysis, the various proxy time series must refer to a single age-depth model for the sediment core they are derived from. For this reason, the basis of our synthesis is formed by a collection of sediment cores, each associated with its own age-depth model. Marine sediments are dated using absolute age controls, where specific layers are dated using, for instance, radiocarbon, tephra or palaeomagnetic properties, and/or relative age controls, where time series are aligned based on the hypothesised synchronicity of the changes recorded by some properties of the sediment.
A well-established hypothesis-based age modelling approach with a solid theoretical basis is the alignment of benthic foraminifera stable-oxygen-isotope ratio (δ 18 O) time series  (Lisiecki and Raymo, 2005). We thus base our chronological framework on a combination of radiocarbon dates and benthic foraminifera δ 18 O and have selected time series where both parameters are available as the foundation of this data product. This approach of blending absolute and relative age controls is required to provide agedepth models for sediment cores that extend beyond the radiocarbon dating range (∼ 40 000 years). If available, further proxy time series were then added, thus ensuring a common chronology among all proxy time series measured on the same sediment core. We selected palaeoclimate parameters to synthesise the following discussion with climate modellers within the PalMod project.
Data discovery In principle, data synthesis can proceed by expansion or reduction (Fig. 1 ). The first, more traditional, approach relies on expert knowledge of what data are available and/or on asystematic literature search. In this approach the synthesis grows by including more data until sufficient data that meet inclusion criteria are compiled. In this way, a lot of time is www. earth-syst-sci-data. net/12/1053/2020/ Earth Syst. Sci. Data, 12, 1053-1081, 2020 Figure  1. Data synthesis approaches. In the expansion approach the database size increases slowly as records are added. The database size follows an opposite pathway using the reduction approach and reaches a stable size more quickly, with less effort. Since the expansion approach is not restricted to data that are available in the public domain, this approach may lead to a database that includes data that are not publicly available (dark data).
The reduction approach on the other hand is arguably more objective, can be automated and is therefore more efficient. This approach also encourages good data stewardship. spent on discovering and retrieving datasets, and it is possible that valuable, but less exposed, data are missed. On the other hand, this approach has a chance of uncovering dark data that are not publicly available (Fig. 1 ). The second approach starts from a large and crude synthesis of data from public sources and proceeds by weeding out data that do not meet criteria for inclusion in the data product. This approach faces different challenges: making sure that the initial bulk database is comprehensive (efficient data mining) and assuring that the data filtering is efficient (fast and accurate). In contrast to the expansion approach, this reduction approach cannot discover dark data.
Initial synthesis We followed the reduction approach and used a semiautomated pipeline to compile data from public sources. Keywords (Supplement) were used to make lists of URLs of potentially relevant data on https://pangaea. de (last access: September 2016), and the linked files were then downloaded in bulk (n = 108 239). A slightly different approach was followed for the NCEI archive. Here, all files that were machine-readable at the time of download  (September 2016 (September , n = 1925) )  were obtained from the FTP server (ftp://ftp. ncdc. noaa. gov/pub/data/paleo/ paleocean//sediment_files/complete, last access: September 2016). Custom scripts in R were used to put all data in a common format and merge time series that could be unambiguously assigned to the same core (based on name and x, y, z position).
Metadata and chronology Subsequently, as far as possible, metadata values were added manually, often by scraping the information from the original publication. Next, all time series from a single site (core) were put on a common depth scale to allow for age modelling. Data that could not be put on a depth scale were excluded from the synthesis. This was the case where parameter values were archived only against age and where no other data file was available that allowed unambiguous reassignment to depth. Ambiguity also resulted from the use of multiple (composite) depth scales for the same archive. Finally, chronological data (all absolute markers, including radiocarbon dates and associated metadata) were manually added, where necessary also by consulting the original publications. Throughout the process, publication information (digital object identifier (DOI) or, if not available, full bibliographic details) and the data source (URL and/or DOI) were preserved in order to trace the source of the data.