dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion This paper examines whether elections affect access to credit for firms. To achieve this objective, we perform a cross-country investigation on a large dataset of firms. Our key finding is that elections exert a detrimental influence on access to credit. We observe that firms are more credit-constrained in election years but also in pre-election years. These results support the uncertainty channel according to which the occurrence of elections deteriorates access to credit by enhancing political uncertainty. We demonstrate that this effect takes place on the borrower side: electoral periods are associated with lower credit demand. By contrast, they do not affect overall credit supply. Some characteristics of firms and of countries influence the impact of elections on access to credit.
Recently, Hergenhahn and co-workers were the first to study electron transfer mediated decay in a lithium chloride solution via electrons coincidences with a MB-TOF using the temporal structure of synchrotron emission  [13]. In this work, coincidence measurements were performed by coupling a MB-TOF electron spectrometer with a liquid micro-jet under vacuum together with soft X-ray synchrotron radiation. Filtering of the data will allow identification of the different Auger structures and a better understanding of the effects of the solvent during de-excitation after innershell ionization. The first results obtained above the oxygen K-edge will be presented, allowing the liquid phase signal to be separated from the signal of the surrounding gaseous phase via energy filtering of the photoelectrons.
Acknowledgments Experiments were carried out on PLEIADES beamline with the approval of Synchrotron SOLEIL (proposals 20190766 and 20191641). We thank the SOLEIL biology and chemistry laboratories for their helpful support, as well as  E. Robert  from  PLEIADES  beamline for his technical support. This work was supported by the  ANR  project  HighEneCh  ( ANR-17-CE30-0017 ), the  NanoTheRad strategic Research Initiatives from Paris Saclay University  and the IraSolubiox project from  Labex PALM  ( ANR-10-LABX-0039-PALM ). J. P. and R. V. thank  H. Ringuenet  from LCP-MR mechanical workshop. J. P. , F. P. and C. N. acknowledge  U. Hergenhahn  for fruitful discussions and advises.
Morphological Traits Characterization In accordance with the guidelines by  UPOV (1988)  and the International Board for Plant Genetic  Resources (1985) , a panel of nine morphological descriptors were used to phenotype the durum accessions (Supplementary Table  S1 ). A durum field trial with plots consisting of 1-m rows, 60 cm apart, with 20 germinating seeds per plot was used. Data were scored on 10 plants per plot, at harvesting time, on a random sampling of 15 representative spikes (split into three replicates consisting of five spikes) and were was carried out from each landrace/cultivar. The data were used to obtain a morphological clustering of accessions using the neighbor-joining clustering method using the DARWIN software v. 6. 0. 021  (Perrier and Jacquemoud-Collet, 2006) , with bootstrapping of 1,000 replicates to determine the support for each node.
Genotyping and SNP Quality Control The durum wheat accessions were genotyped using the Illumina wheat 25K Infinium iSelect array (https://www. traitgenetics. com)  (TraitGenetics Gmb, 2022). SNP marker positions were ordered according to the physical map of durum wheat genome cv. Svevo  (Maccaferri et al. , 2019)  available at https://www. interomics. eu/durum-wheat-genome  (Interomics, 2022). The SNP data were filtered using the TASSEL software  (Bradbury et al. , 2007) , discarding SNPs with a call rate >10% and minor allele frequency less of 1%. Finally, the data set was pruned for linkage disequilibrium (LD) (r 2 = 0. 50) using the SNP and Variation Suite (SVS, 2022) software v. 8. 4. 0 (http://www. goldenhelix. com).
Genetic Diversity Analysis The SVS software was used to calculate the identity by state (IBS) distances among accessions and identify cases of synonymy (IBS > 0. 99). A phylogenetic tree was carried out using the maximum likelihood method based on the Tamura-Nei model  (Tamura and Nei, 1993)  with 1,000 replicates to generate the bootstrap values in MEGA X  (Kumar et al. , 2016). In addition, principal coordinate analysis (PCoA) was performed to estimate the genetic diversity among durum wheat accessions. The analysis of molecular variance (AMOVA) was performed to check the significance of the variance between groups obtained from phylogenetic analysis. PCoA, AMOVA, as well as the genetic indexes, such as number of effectives alleles (Ne), Shannon's information index (I), heterozygosity observed (h), percentage of polymorphic loci (PPL), and private alleles, were estimated using the Genalex v.
Identification of Divergent Loci The F ST at single SNP loci was estimated according to  Taranto et al. (2020)  by pairwise comparison between the groups identified by the phylogenetic analysis. Venn diagram was used to visualize the divergent loci (F ST > 0. 25) using the Venny software  (Oliveros, 2007). Putative candidate genes flanking the regions of divergent SNPs were identified according to the average LD decay distance estimated in durum collection using r 2 statistic at threshold = 0. 20.
Genetic Relationships and SNP Diversity The genetic diversity of the collection was investigated by a high-throughput genotyping system based on a 25k SNP wheat array. A total of 11,746 high-quality SNPs were retained after filtering and mapped on the 14 chromosomes of the durum genome  (Maccaferri et al. , 2019). Filtered VCF file was submitted to the Mendeley Data repository (https://data. mendeley. com/datasets/9hnjw77mf2/2) under the doi: 10. 17632/9hnjw77mf2. 2. LD pruning was applied to remove markers in strong LD, thus retaining 5,555 SNPs that were used in downstream analyses. IBS values ranged from 0. 60 to 1. 00 (Figure  2 ). Eight pairs of IBS values were higher than 0. 99, suggesting the presence of synonymous accessions in the Tunisian landraces. In detail, the accessions Chili, Abdelkader, and Mahmoudi-552, as well as Mahmoudi, D-58-25-A, and Bidi-2 were synonymous.
Organism and culture conditions The strain was isolated from microbial mats anchored to submerged stones in hot water (Ain Echfa, 60 °C) located in the northern part of Tunisia (36° 49′ N, 10° 34′ E). Isolation and purification were performed by dilution and plating of water samples. The amplification and sequencing of the 16S rRNA gene fragment showed a similarity of 94. 3% with Leptolyngbya sp. ,  (Bravakos et al. 2016 ) and 93. 6% with Leptolyngbya frigida ANT. L52. 2  (Taton et al. 2006). The strain was identified as a new species of the genus Leptolyngbya and named Leptolyngbya sp. The nucleotide sequence determined has been deposited in the Gen-Bank database (accession number SUB3434644 clone-38-pGEMT-EUB28f-1492r-1454pb-MG753795GU552680). The strain was referenced IkmLPT16 and conserved in the Tunisian National Institute of Sciences and Technology collection.
Monosaccharide's composition Monosaccharide analysis was carried out by GC-FID after acid hydrolysis by comparing their retention time with that of the monosaccharide standards. A standard curve was drawn using the area normalization method (data not shown) to calculate the content of each monosaccharide component in Leptolyngbya EPSs. The result revealed a heterogeneous composition (Table  2 ) of four monosaccharides, d-mannose (35. 2%) and d-arabinose (24. 3%) as pentoses and d-glucose (15. 6%) and d-rhamnose (2. 6%) as hexoses and, one uronic acid d-galacturonic acid (8. 7%). The non-identified (13. 6%) molar proportion could be attributed to amino sugars identified in some cyanobacterial and bacterial EPSs and the analytical methods used in this work, were not suitable for detecting these components  (Nicolaus et al.
EPSs showed also high scavenging abilities on hydroxyl radical •OH of 90. 21 ± 0. 4% at 100 mg mL -1 , whereas the scavenging activity of the ascorbic acid was significantly lower with the highest value of 70. 5 ± 0. 1% (Fig. 6B ). The IC 50 of Leptolyngbya sp. EPSs on •OH were 10 mg mL -1 , twofold lower than of ascorbic acid (20 mg. mL -1 ). The reducing activity of Leptolyngbya sp. EPSs was evaluated by measuring their ability to reduce ferric ion (Fe 3+ ) to its ferrous form (Fe 2+ ). Data (Fig. 6C ) showed that EPSs exhibited significantly lower electron-donating ability (OD 700 = 0. 3 at 0. 5 nm) than EDTA (OD700 nm = 2. 9 at 3 nm). Nevertheless, the EPSs from Leptolyngbya sp. presented a higher metal chelating activity with a maximum of 91% at 100 mg. mL -1 which was higher than that obtained with EDTA (77%) (Fig.
Background: Atmospheric levels of polycyclic aromatic hydrocarbons (PAHs) have been monitored in many companies since 1940. Because of the use of respiratory protective equipment (RPE) and cutaneous absorption, the measurement of urinary 1-hydroxypyrene (1-OHP), metabolite of pyrene (Pyr), and, more recently, 3-hydroxybenzo[a]pyrene (3-OHBaP), metabolite of benzo[a]pyrene (BaP), has been carried out to assess PAH exposure and estimate health risks. Objectives: This study aimed to investigate the agreement between 523 air and biological levels recorded in the Exporisq-HAP database by taking into account the effectiveness of RPE. Methods: The agreement/consistency between 523 air and biological exposure levels was assessed by estimating and comparing the probability of exceeding French limit values (LVs) for both BaP and 3-OHBaP and ACGIH LV for 1-OHP, respectively.
Data selection Since 1998, the Exporisq-HAP (E-HAP) database has prospectively recorded individual atmospheric PAH levels and urinary metabolite concentrations of workers exposed to PAHs. For each worker, data on age, smoking habits, job activities and RPE were collected. This database has been the subject of several previous publications  (Maître et al. , 2018; Petit et al. , 2017). In the present study, only individual airborne and urine samples collected on the same working day were selected. These two associated samples are referred to as "air vs. biocouple" or simply "couple" throughout this manuscript. Only couples with the analysis of urinary 1-OHP measured at the end of the work week-end of the shift (EWES) or of urinary 3-OHBaP at EWES plus 16 h (EWES+16) were considered. Occupational activities outside of France or before the year 2000 (no gaseous PAH samples), samples with a short sampling duration <2 h, samples with urinary creatinine outside the tolerable limits (<0.
3 g. L -1 or >3 g. L -1 ), or samples with coding errors were excluded. A total of 1433 air vs. biocouples collected in 12 industrial sectors were found in the E-HAP database (Fig. 1 ). Overall, 910 couples were excluded, mainly (78%) couples that were not sampled on the days and moments of interest (Fig. 1 ). A total of 523 couples sampled in 98 plants between  2006 and 2019  were included in the present study. Among them, 508 air vs. biocouples included a measure of 1-OHP (BaP vs. 1-OHP couples) and 188 of 3-OHBaP (BaP vs. 3-OHBaP couples). For 173 subjects, there were measures of BaP, 1-OHP and 3-OHBaP.
wBaP vs. 3-OHBaP couples The distributions of w BaP and 3-OHBaP within each occupational group are presented in Fig. 3  for the 181 couples. The health risk was assessed by the respective exceedance fraction of OEL CNAM and BLV INRS. No data were available for the coke production and steel foundries. The conclusions regarding health risk were identical according to.
CODE-EHR reporting framework The path from structured healthcare data to clinical research output is complex. To support further development in a transparent way, stakeholder delegates reached consensus of the need for a set of minimum standards that authors could use as a tool to enhance design, reporting, and research output. The CODE-EHR Minimum Standards Framework presented in table 2 allows authors to report on how structured healthcare data were used in their research study (either in patient identification, disease phenotyping, or outcome derivation). Preferred standards indicate high level attainment of quality and can be used as a tool to improve the future trajectory of research. The checklist was created through an iterative process based on the stakeholder proposals and covers five key areas of enhanced transparency: how and why coding was performed; the process of constructing and linking datasets; clear definitions of both diseases and outcomes; the approach to analysis, including any computational methods; and demonstrating good data governance.
Validation at local, regional and global levels. Evidence demonstrating how algorithms have been externally validated, and also what quality assessment was performed on the research findings, for example on the accuracy, completeness and timeliness of the data. 14 Data quality rules can be used to assess coded data and allow comparisons across institutions and countries. 15 porting of methods used for data pre-processing and data linkage. This includes the methods used to assess the quality of linkage and the results of any data pre-processing and linkage (with provision of false positive and false negative rates, comparisons of linked and unlinked data, and any sensitivity analyses). 16 A flow diagram showing the processes for cleaning and linking different coding sources and datasets can aid understanding of the study design.
The use of AI techniques could facilitate larger validation studies by automated extraction of supporting text from clinical notations. Such validation exercises can be pre-registered, for example in the form of a Study-Within-A-Trial. 20  Another possibility is for researchers to provide consented and anonymised gold standard cases to benchmark against, or for data from devices used to verify codes (such as lead fractures). The value of synthetic datasets for validation, which mimic real data, needs further exploration. Mixed model approaches to collect data on particular endpoints. May be valuable for situations where the EHR does not reliably collect relevant data. For example, where patients and/or clinicians are asked for information, or data are collected via wearable devices or telemonitoring.
In some cases, parallel monitoring of patients alongside the EHR study may provide additional confidence (for example to identify serious unexpected adverse events). Technological advances in EHR systems will help, such as the ability to retrieve EHR data on a daily basis to support clinical trials. 21 king advantage of the many real world data initiatives to support new research. Government agencies, regulators, charities and professional bodies have initiated programmes for better use of real-world data that can support further activity and dissemination. governmental, and healthcare agencies, as well as clinicians and patients in every medical specialty. The CODE-EHR checklist asks for clarity on reporting and defines a set of minimum and preferred standards on the processes that underpin coding, dataset construction and linkage, disease and outcome definitions, analysis, and research governance.
Acknowledgments We are grateful for the work of the CODE-EHR international consensus. Delegates and attendees of the meetings were: Regulatory agencies:  European Medicines Agency ,  Netherlands  ( Xavier Kurz );  US Food and Drug Administration, USA  ( John Concato ,  Robert Kazmierski ,  Jose Pablo Morales , and  Ileana Piña ). Reimbursement authorities:  National Health Care Institute, the Netherlands (Wim Goettsch );  National Institute for Health and Care Excellence, UK (Adrian Jonas) ;  Dental and Pharmaceutical Benefits Agency (TLV), Sweden (Niklas Hedberg ). Medical Journal Editors: European Heart Journal, Switzerland ( Filippo Crea  and  Thomas F Lüscher ); The  BMJ, UK  ( Wim Weber  and  Tiago Villanueva ); The Lancet, UK (Stuart Spencer); The  Lancet Digital Health, UK (Rupa Sarkar ).
Industry representatives:  AstraZeneca  ( Martin Fredriksson  and  Mats Sundgren );  Bayer  ( Andrea Derix ,  Gunnar Brobert , and  Lothar Roessig) ;  Servier  ( Benoit Tyl ); The  Hyve (Kees van Bochove) ;  Vifor Pharma  ( Maureen Cronin ). Funders:  EU/EFPIA Innovative Medicines Initiative ,  Belgium  ( Colm Carroll );  European Commission, Belgium (Ceri Thompson). Patient Advocacy: European Heart Network, Belgium  ( Birgit Beger ,  Susanne Løgstrup , and  Marilena Vrana );  European Society of Cardiology Patient Forum, France (Paul McGreavy ). Clinical Academic and Professional Societies:  European Society of Cardiology , France from  EU/EFPIA Innovative Medicines Initiative  ( BigData@Heart 116074 ), during the study; grants from  National Institute for Health Research  ( NIHR CDF-2015-08-074   RATE-AF ;  NIHR130280 DaRe2THINK ;  NIHR132974 D2T-NeuroVascular );  British Heart Foundation  ( PG/17/55/33087 ,  AA/18/2/34218  and  FS/CDRF/21/21032 ); and  European Society of Cardiology  supported by educational grants from  Boehringer Ingelheim ,  BMS-Pfizer Alliance, Bayer, Daiichi Sankyo, Boston Scientific , the  NIHR/ University of Oxford Biomedical Research Centre , and  British Heart Foundation/University of Birmingham   Accelerator Award  ( STEEER-AF NCT04396418 );  Amomed Pharma  and  IRCCS San Raffaele/Menarini  (Beta-blockers in Heart Failure Collaborative Group  NCT0083244 ), outside of this work; and advisory board personal fees from  Bayer, Amomed, Protherics Medicines Development and Myokardia , outside of this work.
Funding The  BigData@Heart  project has received funding from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement No  116074. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme  and  European Federation of Pharmaceutical Industries and Associations. The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.
for each variable. standard met Preferred: Provided within a (c) Provide data on completeness pre-published protocol or open of follow-up. access document. (d) For linked datasets, specify how linkage was performed and the quality of linkage methods. 2. Data fit for To ensure transparency with the Minimum: Clear unambiguous (a) Confirm origin, clinical Choose one from: purpose approach taken, with respect statements on the process of processes, and the purpose of (1) Minimum standard to coding of the structured coding in the methods section of data. not met healthcare data. the research report. (b) Specify coding systems, clinical (2) Minimum standard Preferred: Provided within a terminologies, or classification met pre-published protocol or open used and their versions, and OR (3) Preferred access document.
any manipulation of the coded standard met data. (c) Provide detail on quality assessment for data capture. (d) Outline potential sources of bias. 3. Disease and To fully detail how conditions Minimum: State what codes were (a) Detailed lists of codes used for Choose one from: outcome AND outcome events were used to define diseases, each aspect of the study. (1) Minimum standard definitions defined, allowing other treatments, conditions, and (b) Date of publication and access not met researchers to identify errors outcomes prior to statistical details for the coding manual (2) Minimum standard and repeat the process in analysis, including those relating (please add to box below). met other datasets. to patient identification, therapy, (c) Provide definitions, OR (3) Preferred procedures, comorbidities, and implementation logic and standard met components of any composite validation of any phenotyping endpoints.
Biodiversity databases are changing the longevity of data in the era of open science. They also represent a collaboration opportunity in analyzing large-scale (paleo)biological patterns beyond a local project or a time scale. Ostracods, microscopic crustaceans, are a component in many biodiversity databases. They live in most kinds of aquatic environments today and their fossil record spans nearly the whole of the Earth's metazoan biosphere history from Ordovician to Holocene. Thus, ostracods provide an ideal model system for understanding large-scale biodiversity patterns and dynamics in both space and time. Thanks to many contributors, current and future ostracodologists have access to databases that have gone through numerous improvements and have been populated by many datasets. However, rapid growth of databases has caused confusion among users regarding available data, technical terms and database aims.
We review key databases that include ostracods, summarizing their history of development, current spatial and temporal coverage, various types of data models and the intertwined relationships between databases. We also present a quantitative summary of ostracod diversity history based on the Paleobiology Database. Our investigations show that the database field is transitioning from the traditional single focus to multipurpose, from static to dynamic data display/download and from independent systems to collaborative networks. We compare the ways several databases approach persistent challenges such as taxonomic harmonization, validation of the original sampling metadata and paleolocality uncertainties. With increasing capability of data integration, databases continue to require enormous efforts regarding highquality data entry and careful coordination among scientists and technical teams.
Introduction To assess the biological impacts of global changes there is an increasing demand to understand the evolving dynamics among ecological components over various spatial and temporal scales (e. g. ,  Jablonski and Sepkoski Jr, 1996; Scholes et al. , 2008; Rick and Lockwood, 2013). Playing an increasingly important role in this research trend, many databases have been developed to preserve, curate, and mobilize hard-earned biodiversity data  (Goddard et al. , 2011) , motivated by a need to increase the discoverability or accessibility of such data. Investigations into macroecology, ecosystem dynamics, and macroevolution increasingly rely on data drawn from biodiversity databases (e. g. ,  Liow and Stenseth, 2007; Marx and Uhen, 2010; Tittensor et al. , 2010; Villéger et al. , 2011; Lazarus et al.
, 2014; Kocsis et al. , 2018b; Reddin et al. , 2020; Chaudhary et al. , 2021). The importance of biodiversity databases is evident, and their continuous improvement requires the support and collaboration of the scientific community. Biodiversity databases are rapidly evolving. Traditionally, data have been scattered across platforms in various formats with lower interoperability. One important characteristic of improved biodiversity databases is data mobilization with higher interoperability. The term mobilization refers to making data readily operable for analysis and downloadable through a freely accessible website  (Faith et al. , 2013; Nelson and Ellis, 2019). It can be applied to all kinds of biodiversity data, including digitized museum collections data, field-based research datasets and laboratory-generated DNA barcode data.
The implementation of data mobilization is often consolidated by research infrastructures and digital tools that are designed to (ideally) enhance the reproducibility of scientific studies, to facilitate exchange of datasets in consistently structured ways, to promote open data/science  (Burgelman et al. , 2019; Powers and Hampton, 2019)  and to facilitate integrated data analyses (e. g. ,  Costello and Wieczorek, 2014; Peters and McClennen, 2016; Williams et al. , 2018). For example, metadata documentation and many data standards have been developed to allow machine readable exchange of data between databases. Depending on the database, the rules for metadata documentation vary from a free "readme" style to formal standards that define/dictate vocabularies for describing data. Although more work is required, especially for 3D digital morphological data  (Davies et al.