dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015; Buga et al. , 2019). Regional experts are involved in the validation of the aggregated data collection; as a result, quality flags are assigned to all data according to a standard scale (SeaDataNet measurand qualifier flags, L20 vocabulary 3  ). To keep all relevant information describing the dataset (e. g. , data originator, station characteristics, sampling instruments. ) data files and metadata files are merged in ODV to create a metadata enriched SeaDataNet ODV data collection. Due to the large variety in chemical data, dedicated and customized approaches and the development of new tools for.
Eutrophication and Acidity In order to produce thematic data sets, aggregated at Regional Sea scale, a dedicated vocabulary was implemented (P35 vocabulary: EMODnet Chemistry aggregated parameter names) to combine various P01 terms associated to a same substance (e. g. , nitrate in seawater) but measured with different protocols or expressed with different measurement units, into a unified aggregated term with a uniquely identified standard unit. The ODV software  (Schlitzer, 2020)  has a built-in aggregation procedure, making use of the P35 vocabulary and also applying a number of business rules, such as for averaging and possible unit conversions. The information related to the specific parameters (as described by the P01 vocabulary) is available as metadata and, if provided by data originators, details about the acquisition instrument are also made accessible.
The final resulting ODV data collections, aggregated and validated, are used as input for data interpolation on a regular grid (Data-Interpolating Variational Analysis,  Brankart and Brasseur, 1996)  and for the preparation of data products available through EMODnet Chemistry portal. A synthetic description of the data formats and the validation process is given below. Two types of data are defined for each collection and treated separately: • vertical profiles (VP) for data that have been collected roughly at the same time and location for several consecutive vertical depths, • time series (TS) for data collected at the same location and depth but repeated in time. Most data related to eutrophication are provided as VP collections whereas some TS are available for Mediterranean Sea, Greater North Sea and North East Atlantic Ocean.
Contaminants In the case of contaminants, heterogeneity of data is particularly high, as contaminants are measured in three matrices (water, sediment, biota), with different characteristics (dissolved/ particulate phase in water, different sediment size classes, different marine species and target tissues/organs), with different sampling, analytical and normalization protocols. This complexity results in a high number of different P01 terms for each single substance, depending on the matrix, sampling, analytical procedures, and makes data comparability among different areas really challenging. As an example, only for the Black Sea 347 unique P01 terms were listed. In order to focus EMODnet Chemistry efforts at regional sea scale, an in-depth analysis of the relevant EU legislation concerning contaminants (EU Directive 2013/39/EU; EU Water Framework Directive 2000/60/EC; Marine Strategy Framework Directive MSFD -2008/56/EC) and of the procedures defined by the Regional Sea Conventions for the assessment of chemical pollution of the marine environment was carried out.
The most relevant substances used for the assessment of ecosystem status were identified. Among the large list of substances target of EMODnet Chemistry data collection, priority was given to the following parameters: • Pesticides: p,p'-DDTs (including in this group p,p'-DDE, p,p'-DDT and p,p'-DDD) and HCB; • Antifoulants: TBT and TPT; • Heavy metals: mercury, cadmium, lead, plus copper and zinc; • Hydrocarbons: anthracene, benzo(a)pyrene, fluoranthene, naphthalene, phenanthrene, hexachlorobenzene; • Radionuclides: 137Cs; • Polychlorinated biphenyl (PCBs): PCB118, PCB138, PCB153 and PCB180, plus PCB28, PCB52, PCB101 and PCB105. To address the wide heterogeneity of measurement units used by the different laboratories and to facilitate data comparability across regions, preferred units have been proposed, in line with those indicated for Good Environmental Status assessment in the recent EU directives (2013/39/UE; Comm.
Dec. EU 2017/848). Following recommendations were given to express concentrations in the different matrices: • Water: µg/l • Sediment: µg/kg of dry weight sediment • Biota: µg/kg of fresh weight for biota following guidelines (except mussel as dry weight). In the case of contaminants, to improve data processing and make use of the important information on species, chemical substance, matrix, basis of determination etc. connected to each single variable (P01+P06 code), the P01 code is decomposed into its separate parts of relevant information (i. e. , split in its subcomponents namely the substance name, the matrix characteristics, the taxonomic level, the measured statistics,. ). In this way, the user can independently aggregate the data and reduce the large heterogeneity. This approach has been agreed within the EMODnet community in consultation with the MSFD group of experts and new functionalities were added to ODV tool.
DATA QUALITY CONTROL LOOP Data originators are responsible for Quality Assurance (QA) and Quality Control (QC), involving the assessment of the whole sampling/laboratory analysis process and resulting on data flagging according to a standardized approach. All data originators provided information on QA/QC procedures through questionnaires based on ISO/IEC 17025:2017 standards, which indicate that most laboratories follow standard procedures involving inter-calibrations and the use of certified reference material 4. EMODnet Chemistry involves a network of National Oceanic Data Centres (NODCs) that supervise the national availability of research and environmental monitoring data, which are provided, respectively, by research institutes and environmental agencies. NODCs are also responsible for archiving qualitycontrolled data, flagged with quality information (Vinci et al.
,   4 https://www. emodnet-chemistry. eu/data/questionnaires 2017). Within EMODnet Chemistry  (2009-2012, 2013-2016, 2017-2019) , strong effort was dedicated to evaluate the quality of data and to define a validation protocol. The comparisons of datasets from different sea regions showed many inconsistent data quality flags, which is an evidence of the need to coordinate and harmonize practices. Commonly agreed and standardized data aggregation and validation protocols have been defined and shared to guarantee consistency among comprehensive databases, which include data from different origin and spatial or temporal scopes. An internal process that harvests information to check its consistency and quality, revealed a number of unexpected issues. These were reported back to data originators through the related data center, in charge of correcting them and updating the official copy of the data made available for the next harvest.
ANALYSIS OF THE EMODnet CHEMISTRY AGGREGATED AND VALIDATED DATASETS With six different sea regions, three main data categories (covering nutrients, oxygen, chlorophyll, hydrocarbons, pesticides, heavy metals, antifoulants, and marine litter) and more than one century of data for some of them, the qualitycontrolled data collections produced by EMODnet are really huge and thus it is difficult to describe them in detail with a limited number of figures. Some general information on the geographic area covered by the regional data collections, the temporal extent, the number of profiles and DOI reference for their open access is provided in Table  1. Here we focused on the process implemented for managing eutrophication and contaminants. Data quality control is crucial in oceanographic data management, especially for the creation of multidisciplinary and comprehensive databases which include heterogeneous data from different and/or unknown origin covering long time periods  (Buck et al.
, 2019). The outcomes of the validation process are indicated by quality flags assigned to data or to datasets. Merging data from different laboratories, sea regions and quality pose a great challenge to allow data inter-comparability at regional scale. Therefore, the adoption of appropriate and shared methodology, as well as the feedback from scientific community and from data originators are necessary ingredients that contribute to the validation of large data collections. While eutrophication data are mainly available in the water column, on repeated stations or along scientific cruises in the open sea, contaminants are mainly collected in sediments and in different biota species, following national implementations of EU directives requirements. Thus, customized protocols and tools are adopted, while comparable efforts are dedicated to marine litter and described in specific papers.
ACKNOWLEDGMENTS The  European Commission  under the service contract no. EASME/EMFF/2016/1. 3. 1. 2-Lot4/SI2. 749773and EASME/EMFF/2018/1. 3. 1. 8/Lot4/SI2. 810314supported this work. The authors want to acknowledge the contribution of all  EMODnet Chemistry network. Tools  and technical developments received funding in part by the  EU H2020   SeaDataCloud  project (Grant Agreement  730960 ).
3b ). In the analysis with the intact genes, the monophyly of the avian γ-c clade remains well supported as is the clade including all shearwater genes (bootstrap value of 92%). positive selection in full length γ-c shearwater oR genes. To determine the selection regimen governing the evolution of the intact γ-c shearwater genes, we considered two data partitions as GARD found evidence of one breakpoint at alignment position 246, which falls in the putative TM6 domain (Fig. 2 ). Based on the two-partition dataset of all full-length OR genes, the different methods found that the overall d N /d S was never significantly different from 1, varying between ω = 0. 552 (SLAC) and ω = 1. 044 (REL). However, positive selection was identified on individual site codons of the full-length shearwater γ-c genes, consistent with a key role of selection in the functional diversification of these genes in the shearwater (Fig.
Depth of coverage was obtained from an equi-molar pool of non-tagged cosmids, using Illumina HiSeq 2000. 34 million Illumina 100 bp-long reads were used to build multiple assemblies at different depth of coverage using Velvet  51   pooled into ten subsets that minimized within pool clone similarity. For each pool, DNA was sheared to 3Kb and a MID 454 GS FLX Titanium Paired End library was built and sequenced. A total of 541,133 reads were obtained for the 10 pools, with an average read length of 311 bp, resulting in an additional 20X coverage of each of the 96 cosmids. Hybrid assemblies were built with the Celera Assembler v. 7 52 using all 454 data and varying numbers of Illumina reads. Further analyses proceeded on the most comprehensive hybrid assembly. All scaffold and degenerate contigs (the latter contain repetitive sequences that could not be unambiguously assembled with the main scaffolds) were processed through the Institute for Genome Sciences' eukaryotic structural annotation pipeline, which was configured to use a combination of tools, including ab initio gene finders, protein aligners and model combiners.
The HMM database used for HMMer3 is custom-made with TIGRFAMs, PFAM, among other custom records. Proteins which match the HMMs above trusted cut off levels inherit these annotations, or else BLAST results were used. phylogeny of shearwater oR genes. Phylogenetic trees were estimated for two different amino acid (AA) alignments, one including all OR shearwater sequences of length > 75AA (n = 220) (Supplementary Dataset 1) and the other only the full-length ORs (n = 20) (Supplementary Dataset 2). The alignments included complete OR sequences from chicken, zebra finch and green anole (Anolis carolinensis) from 9 and all non-redundant Northern fulmar (Fulmarus glacialis) proteins from Protein database of GenBank. We use a non-OR rhodopsin-like family GPCR gene (Adenosine receptor A2b from the chicken, zebra finch and emperor penguin (Aptenodytes forsteri), respective GenBank accession numbers NP_990418, XP_002198489, XP_009276449) as outgroup  21.
estimates of positive selection on clade γ-c. The ratio of non-synonymous to synonymous substitutions (ω = d N /d S ) is used as an indicator of the strength of selection on protein-coding genes. The presence of recombination or gene conversion, frequent in multi-gene families, precludes the use of methods that estimate selection based on a single phylogenetic tree  69, 70. We estimated d N /d S per codon with four codon-based maximum likelihood methods available in HYPHY  71  , as implemented in the Datamonkey webserver  72. We accounted for the possibility of recombination using the GARD module (also implemented in Datamonkey  72, 73  to infer breakpoints in the alignment and generate tree topologies for each of the non-recombinant data partitions. We then used SLAC, FEL, REL  70  , MEME  74  methods, which differ in assumptions for the distribution of rates across sites or lineages, and an integrative approach, which considers all codon sites detected by each method, to infer selection within each partition.
Acknowledgements We are indebted to  P Catry  and  JP Granadeiro  for collecting the Cory's shearwater sample from the Selvagem Grande I. We thank  João Moreno  for his help with the protein handling software. We thank researchers from  America Pharma Source, LLC  for their advice during the building and screening of the cosmid library. This work was financed by  Fundação para a Ciência e a Tecnologia (FCT) ,  Portugal , through research project  PTDC/ BIA-BDE/100118/2008. MCS was supported by a post-doctoral research fellowship  SFRH/BPD/85700/2012  from  FCT , Portugal. JCS and JBM were supported in part by  NSF 's  Dimensions of Biodiversity program  grant  OCE-1046371.
The socio-economic typology The first typology is built from socio-economic variables, according to a funnel-like logic, i. e. we first seek to qualify the site's local socio-economic context on the scale of its EPCI, then the site profile itself (communes of the site) and finally to qualify the degree of heterogeneity within the site (differentiation between communes of the same site). The local socio-economic context is characterised based on the results of a Principal Component Analysis, followed by an Ascending Hierarchical Classification, both built using a database specifying for each EPCI selected, and then at the site level: -the weight of the various economic bases or drivers (see box); -the propensity to transform these bases into income for the territorial residents; -the tourism intensity and seasonality; -the territorial dynamics of human settlement, employment and income; -the profile of the resident population.
It concerns natural risks (floods, earthquakes, avalanches, and so on) and anthropic (technological, mining) risks, which can be differentiated according to their frequency of occurrence (frequent/rare). The number of natural disaster orders (since 1983) is also taken into account. -The landscape data We used the results of an existing typology 18 identifying 10 landscape classes according to the more or less marked presence of buildings or soil artificialisation, combined with the importance of reliefs, the nature of activities (crops, grasslands, and so on), the presence or absence of coasts, and so on. -The climate data This concerns the usual descriptive elements of the climates such as hours of sunshine, wind speed, temperatures and precipitation. The data is formalised at the communal level.
Some variables are available directly on this scale (such as the risk or landscape variables); for others, it is necessary to perform geoprocessing using a GIS to identify the communes concerned by a zoning (typically for the regulatory variables). Finally, some variables must be extrapolated to the communal scale, the initial data being departmental (climate data). The data available on the scale of the commune is aggregated on the scale of the site. As for the other typologies, the local environmental context is determined from the results of a Principal Component Analysis, performed using the database containing the values of the variables mentioned for each site, followed by an Ascending Hierarchical.
The tax wealth of local authorities and then the tax revenues they collect come from the amount of tax bases relating to properties for residential use on the one hand, and to the presence of economic activities and their dynamism on the other hand. What is then the importance of these two components for intercommunal groupings (hosting outstanding sites), contributing to determining their own situation? The first observation is that there is no 'site effect' which is really noticeable in terms of the fiscal valuation of properties for residential use. The exploration of the Filocom database leads to this observation 26. Indeed, the database includes information concerning the tax bases of the housing tax HT paid by the occupants of residential premises, and this tax is an essential fiscal resource for local authorities.
Proteasome activity INS-1E, A-20 and Jurkat cells, human and mouse islets were plated in duplicates or triplicates in 96-well plates and treated with 50 nM ONX-0914, a selective inhibitor of theβ5i subunit activity (Selleck Chemicals, Rungsted, Denmark, IC50: ~10 nM for β5i,  [35] ) or 2 μM MG132, a broad proteasome inhibitor (Sigma-Aldrich, Søborg, Denmark) or control medium for 2 hours prior to experiments. Chymotrypsin-, trypsin-and caspase-like activity was measured through luminescent assay using commercially available Proteasome-Glo TM Assay (Promega, Nacka, Sweden) according to the manufacturer's protocol. Depicted data are averages of either technical duplicates or triplicates as indicated. The added trypsin-like, chymotrypsin-like and caspase-like activity is referred to as total proteasome activity.
Bulk mouse islet RNA Sequencing Five hundred mouse islets were plated and exposed to IL-1β (50 pg/mL) for 10 days or left non-exposed for 10 days. Total mRNA was extracted from the islets by employing RNeasy 1 Micro Kit (Qiagen, Vedbaek, Denmark). Single-stranded, single-end sequencing libraries were generated using 35 ng of extracted RNA by means of TruSeq1 Stranded mRNA Library Prep (Illumina1, Copenhagen, Denmark), and library sequencing was done with the HiSeq 4000 System (Illumina1, Copenhagen, Denmark). Sequence files were drawn to the UCSC mouse genome NCB137/mm9. Further technical and analysis details in  [36]  and RNA-seq raw data are accessible here: https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE110691. In brief, expression levels of all genes were estimated by Cufflink (cufflinks v2.
Single-Cell RNA Sequencing of Pancreatic Islets Each single-cell transcriptome was sequenced to *750,000 reads, sufficient for cell-type classification. Islet cell subpopulations were analyzed for PSMB8, PSMB9 and PSMB10 genes expression using published human islet single-cell sequencing data  [37]. FastQ files were downloaded from ArrayExpress (accession: E-MTAB-5061). Data was analyzed with bcbionextgen (https://github. com/chapmanb/bcbio-nextgen), using the hisat2 algorithm  [38]  to align sequence reads to human genome version hg38 and uniquely aligned reads within RefSeq gene annotations were used to quantify gene expression with the Salmon algorithm  [39]. Data is then expressed as log2 of counts per million (CPM). Only cells that passed the quality control in the original study  [37]  were maintained for further analysis, and the cell type classification from the original study was also maintained.
INS-1E cells contain two major types of proteasomes We next investigated the composition of proteasomes in INS-1E cells through immunoprecipitation (IP) of the 20S α2 proteasome subunit that is an obligatory member of all types of assembled proteasomes  [46]. In vivo gently cross-linked INS-1E cells proteasomes were immunoprecipitated and samples analyzed by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). About 86% of the total proteasomes were found to be s-proteasomes with enzymatically active rings composed of β1-β2-β5 subunits (Fig  2 ). However, almost 14% of the precipitated proteasomes contained β5i replacing the standard β5 subunit, forming an intermediate proteasome β1-β2-β5i. We found neither β1i nor β2i in protein complexes precipitated with α2 subunit. Finally, our MS data indicated the presence of proteasomal regulatory particles 19S and 11S (PA28αγ) within α2 proteasome complexes at the rate of 55.
Acknowledgments The authors are thankful to JDRF award  31-2008-416  ( ECIT Islet for Basic Research program ) for providing human islets for this study. The authors also acknowledge  C. Wollheim  and  P. Maechler  ( University Medical Centre, Geneva, Switzerland ) for providing INS-1E cell line. Soren Buus , ( Department of International Health, Immunology and Microbiology, Copenhagen, Denmark ) generously provided the A20 and Jurkat cells. We would like to thank  Nils Billestrup  for sharing and discussing his islet sequencing data that is presented in this manuscript.
As a comparison, 23 female geoscientists were active in the  USA in 1921 USA in (and 60 in 1938;; Rossiter, 1982)  compared to 630 men  (1425 in 1938; Rossiter, 1982) , women thus representing 3. 7 % of  USA geoscientists in 1921 USA geoscientists in (4. 2 % in 1938;; Rossiter, 1982). I am not aware of data giving the evolution of the number of geologists worldwide from 1800 to the early 20th century, but today women represent about a third of geoscientists (e. g. Holmes et al. , 2011) , so progress has been made. The list I established is certainly not exhaustive. In the same way as many past female artists are discovered anew, women scientists are progressively saved from the dustbin of history. Thus we realise that even if undertaking studies and a scientific career meant then even more than now facing obstacles (see Sect.
Methodology This paper is mainly a review of the existing literature on the role of women in the history of earth sciences. The biographical dictionary of women in science established under the direction of Marilyn B. Ogilvie and Joy D. Harvey in 2000 has been a particularly rich resource, using all the available sources (in particular Mary R. S. Creese and Thomas M. Creese publications in the 1990s;  Sarjeant, 1978 Sarjeant, -1987;; Aldrich, 1982)  and making the data collected by Nalivkin in Russian accessible in English  (Nalivkin, 1979). Of the 210 women geologists listed in Table  1  and the Supplement, 153 have an entry in  Ogilvie and Harvey (2000). The book by Margaret Rossiter on American women scientists was of course another important source  (Rossiter, 1982)  as was the special publication of the London Geological Society in 2007 coordinated by Cynthia V.
Burek and Bettie Higgs  (Burek and Higgs, 2007). Several blogs of science popularisation have helped me discover new figures and sources about them, especially TrowelBlazers (https://trowelblazers. com, last access: 2019), Letters from Gondwana (https://paleonerdish. wordpress. com, last access: 2019), and Women in American Paleontology (http://www. daringtodig. com, last access: 2019). Many of the women came to my attention as co-authors, collaborators or students of other women as female scientists often developed strong sorority networks (see Sect. 7 "Overcoming discrimination 3: sorority"). Original new data on one of the female pioneers of hydrogeology, Norah Dowell Stearns (see Sect. 8 "Focus on a new discovery: Norah  Dowell Stearns") , are presented in this paper. I found her by going through the authors of early 20th-century hydrogeological publications, looking for female first names.