dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequencing analyses were performed on the ABI Prism 3130XL Genetic Analyzer (Applied Biosystems, Thermo Fisher Scientific, France) using the DNA sequencing BigDye Terminator V3. 1 Cycle Sequencing Kit (Applied Biosystems, Foster City, CA, USA, Perkin-Elmer) according to the manufacturer's instructions. The BigDye products were purified on Sefadex G-50 Superfine gel filtration resin (Cytiva, Formerly GE Healthcare Life Science, Sweden). The sequences were compared to sequences available in the GenBank database using the BLAST algorithm (http://blast. ncbi. nlm. nih. gov/Blast. cgi). Multispacer sequence typing (MST) genotyping of Coxiella burnetii. The multispacer sequence typing (MST) method was used for Coxiella burnetii genotyping. For this purpose, five (5) different spacers, which were previously described  [80] , were selected and amplified (Cox 2, 5,  18, 22, 37).
Phylogenetic and statistical analyses Phylogenetic analysis. The obtained sequences were analyzed using ChromasPro version 1. 3 (Technelysium Pty, Ltd. , Tewantin, Queensland, Australia) for assembly and were aligned with other sequences of targeted bacteria or parasite species from GenBank using CLUS-TALW, implemented in BioEdit v7. 2  [89]. Phylogenetic trees were constructed with MEGA software v. 7  [90]. The maximum likelihood method based on the Hasegawa-Kishino-Yano model (HKY) was used to infer the phylogenetic analysis with 500 bootstrap replicates. Statistical analysis. Statistical analysis was performed with R software V3. 2. 5  [91]  using chi-square/Fisher's exact tests for data comparisons between the prevalence of infected rodents for all parasites according to habitat type. A p-value� 0.
Phylogenetic analysis for the taxonomic description of detected pathogens Bartonella. The sequencing of 733 bp of the ITS gene from the DNA extracts of 13 qPCRpositive individuals revealed five sequences of Bartonella ranging from 690 to 722 bp (Gen-Bank: MN968369 to MN968373). BLAST analysis of three sequences obtained from   Lophuromys sp. hosts showed that the most closely related species was Bartonella queenslandensis (GenBank: EU111769. 1), which presented the highest score and a percentage of identity of 84-86% (611/721, 546/634, 550/635). This percentage of identity below 95% and the fact that all these three sequences were grouped in the same cluster (with 99% of identity between each other) in the phylogenetic tree suggested that the obtained Bartonella pathogen represented a new species, an undescribed species.
We provisionally named this probable new genotype Candidatus Bartonella gabonensis. The fourth sequence obtained from a Cricetomys sp. rodent matched the B. massiliensis OS23 and OS09 strains (HM636450 and HM636449) with 96. 7% (699/723) and 96. 1% (700/728) identity, respectively. The last sequence, obtained from R. rattus, matched Bartonella sp. 'Tel Aviv' of the Bartonella elizabethae complex (GenBank: CP031843. 2) with 100% (690/690) identity. It is referred to here as B. elizabethae (Fig  2 ). Anaplasma. Among 16 qPCR-positive individuals, six sequences ranging from 623 to 683 bp (GenBank: MT269268 to MT269273) were obtained after the sequencing of the Anaplasma 23S rRNA gene  [76, 94]. BLAST analysis of these sequences showed identities with A. phagocytophilum (KM021418) ranging from 91% to 92% (578/633, 606/659, 607/659, 606/660, 607/ 659, 607/659).
The same genotype, MST20 was also found on domestic animal ticks in Ethiopia in Africa  [95]. In Franceville, C. burnetii MST group 20 was found in all the samples tested from the five rodent species mentioned Praomys sp, R. rattus, Mus Nannomys sp, Le. striatus and Lo. sikapusi. Cricetomys sp; one, from Praomys sp (1/198), showed 100% (522/522) identity to both T. brucei brucei (XR_002989635) and T. brucei gambiense (FN554966. 1); and two others from R. rattus and Mus Nannomys (AB190228) (2/198) were identified as T. otospermophili, with 97% (453/ 467) identity  (Fig 4). Theileria. The two samples that were positive according to the pan-Piroplasma 5. 8S qPCR analysis and were sequenced (GenBank: MT269266 and MT269267) were shown 98% (869/ 883, 868/881) identity to Theileria sp strain HaD-2019a (MK484070.
Leptospira. Seven samples were positive in qPCR screening of the 16S rRNA gene of Leptospira sp. The sequencing of a portion of the LipL 32 gene from the DNA extracts of 7 qPCRpositive individuals revealed two Leptospira sequences (MT274303 and MT274304). Sequence MT274303 was 99. 1% (442/446; 444/448) similar to both Leptospira borgpetersenii serovar Hardjo (CP033440. 1) and Leptospira weilii (AY461930. 1), identified from Lophuromys sp. Sequence MT274304 was 100% (474/474) similar to both Leptospira kirshneri (JN683896. 1) and Leptospira interrogans (KC800991. 1), identified in the Crocidura goliath shrew (MT256384. 1). Trypanosoma. Five sequences of Trypanosoma ranging from 464 to 571 bp were obtained (GenBank: MT271793 to MT271797) after the sequencing of 550bp of the 28 S gene from 14 qPCR-positive individuals identified when screening for Trypanosoma spp.
Acknowledgments We thank the  Association pour la recherche en infectiologie (APRI)  for his support in finalizing this study. We thank the authorities of the city of Franceville as well as the leaders of the districts where rodent sampling took place. We also express our deep gratitude to those individuals who have kindly given us access to their homes. We also thank  Mr AWOUNDJA Lauriano  for assistance with the trapping of rodents and the interpretation of local languages and  Randy Lyn ESSONO  for his help in mapping. Finally, this acknowledgements section would be incomplete without conveying our gratitude to  Dr Celia BOUMBANDA KOYO ,  Handi DAHMANA  and  Marielle BEDOTTO-BUFFET  for their help with molecular biology analyses in the laboratory and  Dr Serge DIBAKOU  for his support in statistical analysis.
Since November 2015, during a field campaign at the French base in Dumont d'Urville, instruments have been installed, including a Micro Rain Radar (MRR) observing clouds and precipitation particles from surface  [13]. This instrument has provided a continuous vertical structure of precipitation and its climatology. Among other results, this has highlighted the sublimation of precipitation by katabatic winds, as well as providing information on the mean sedimentation rate of precipitation  [14] [15]. This vertical profile is also an excellent tool for evaluating the simulated vertical structure of precipitation. In this study, we propose to evaluate the vertical structure of precipitation at Dumont d'Urville, simulated by the general circulation model LMDz  [16, 17] , using the MRR dataset.
Acknowledgements. The authors thank  Karine Marquois ,  Philippe Weil  and the  IT department of the Laboratoire de Météorologie Dynamique / Institut Pierre Simon Laplace  for the informatics support. The authors thank  Frédéric Hourdin  and  Sébastien Froment  for their valuable advices. CG acknowledges support by the  French polar institute IPEV  under projects CALVA and APRES3 for MRR radar deployement and operation at DDU station.
| Sample selection We consider a large sample of U. S. listed firms over the period 1998-2017. 12 We begin with all firms in the Compustat database for that period and collect data on the number of employees and firm financial characteristics. We also obtain data on firm stock returns from the Center for Research in Security Prices. To retrieve information on product market 9 Previous studies argue that although firm reputation cannot be directly captured by financial statement variables, it is considered important 'soft information' that is highly valuable to capital providers  (Anginer et al. , 2016; Armitage & Marston, 2008; Cao et al. , 2015; Smith et al. , 2010). 10  Attig et al. (2013)  shed light on the importance of corporate social performance, including employee relations, for firms' credit ratings, suggesting that firms that comply with what is desired by society benefit from higher ratings and, hence, lower financing costs.
This model is specified as follows: a a a a a a a a a a a a a a a a a ε NET_HIRE = + SALES_GROWTH + SALES_GROWTH + ΔROA + ΔROA + ROA + RETURN + SIZE_R + QUICK + ΔQUICK + ΔQUICK + LEV + LOSSBIN1 + LOSSBIN2 + LOSSBIN3 + LOSSBIN4 + LOSSBIN5 + Industry dummies + , it it i t it i t it it i t i t i t it i t i t i t i t i t i t it 0 1 2 ,-1 3 4 , -1 5 6 7 , -1 8 , -1 9 , -1 10 11 , -1 12 , -1 13 , -1 14 , -1 15 , -1 16 , -1 (1) where NET_HIRE is percentage change in number of employees; SALES_GROWTH is percentage change in firm total sales; ROA is firm profitability, defined as the ratio of net income 13 The Hoberg and Phillips Data Library is available at http://hobergphillips. tuck. dartmouth. edu. to total assets at the beginning of the year; RETURN is total stock return over the past 12 months; SIZE_R is percentile rank of firm size; QUICK is the ratio of cash and short-term investments plus receivables to current liabilities; LEV is the ratio of debt in current liabilities plus total long-term debt to total assets; LOSSBIN1 through LOSSBIN5 are five dummy variables that indicate losses ranging between zero and -0.
18  We use a difference-in-differences (DID) estimation approach to examine how labour investment decisions react to large tariff reductions. We first obtain tariff data using U. S. trade data available at the Harmonized System product level from Schott's International Economics Resource Page. Schott (  2010 ) also provides Trade Data and Concordance Tables, which map each Harmonized System product code into SIC manufacturing industry codes. Using this mapping, we calculate the ad valorem import tariff rates for each three-digit SIC industry as the ratio of duties collected from that industry to the dutiable value of all imports. 19 Following  Huang et al. (2017) , we define large tariff reduction events as industry-year observations in which the decrease in tariff rates exceeds twice the industry mean tariff reduction.
19 We first download tariff data from Schott's International Economics Resource Page (http://faculty. som. yale. edu/ peterschott/sub_international. htm) up to 2005. We then update the data up to 2017 using import and export data, which are also available from Schott's website. Valta (2016) , we match each treated observation to its nearest-neighbour observation that is closest in terms of its probability of being affected by a competitive shock event. This probability is calculated based on a probit model that estimates the likelihood of a large tariff reduction event on the basis of firm size (SIZE), investment opportunities (MTB) and firm leverage (LEV)  (Mattei & Platikanova, 2017). The matching procedure yields a sample of 15,232 firm-year observations equally distributed among firms operating in industries that experienced a tariff reduction event and those that did not.
(2011)  argue that labour unions could reduce firm operating flexibility, as they increase the costs of labour adjustments. For example, union bargaining power makes wages sticky, thereby increasing the costs associated with retaining existing workers and hiring new ones. This situation can distort firm hiring decisions and lead to lower investment efficiency. Following previous work (e. g. ,  Ben-Nasr & Alshwer, 2016; Jung et al. , 2014) , we proxy for industry-level labour union coverage using data from the Union Membership and Coverage Database developed by  Hirsch and Macpherson (2003). 22 On the basis of information collected from the monthly Current Population Survey conducted by the Bureau of Labour Statistics, this database provides data on labour union membership and coverage for both the public and private sectors at the Census Industry Classification (CIC) industry level.
We also retrieve data on number of employees by occupation from the Occupational Employment Statistics of the Bureau of Labour Statistics. We then calculate the industry-specific index of reliance on labour skills (LABOR_SKILL) as the weighted average number of employees working in different occupations per industry, where the weight is the U. S. Department of Labor's O*NET program classification of occupations based on worker skill levels. 24 More specifically, the degree of reliance on skilled labour for a particular industry i is calculated as follows: E E Z LABOR_SKILL = × , i j O ji i j =1              where E ji is number of employees working in industry i for particular occupation j, E i is total employees in industry i, O is number of occupations in industry i and Z j is skill level of occupation j determined by the U.
We retrieve data on historical business headquarters from the Software Repository for Accounting and Finance (SRAF) website of Bill McDonald. 29,30 We next identify the year in which each state enacted an RTW law based on  Chava et al. (2020). We define RTW as an indicator variable that equals 1 for all years after each firm's historical home state has passed an RTW law and zero otherwise. We then collect information on UI benefits from the U. S. Department of Labor's 'Significant Provisions of State UI Laws'. We follow Agrawal and T A B L E 10 The role of labour laws This table reports regression results on the impact of labour laws on the relation between product market competition and labour investment efficiency. RTW is an indicator variable that equals one for all years after each firm's historical home state has passed a Right-To-Work (RTW) law and zero otherwise.
At the moment, it unites 32 expert centers and 10 affiliated partners in 21 European countries, as well as patient representatives, but will soon cover nearly all countries of the European Union as a result of the ongoing expansion process. Disease expert groups developed and consented on diagnostic flowcharts and disease scales to assess the different aspects of RNDs. ERN-RND has started to discuss diagnostically unclear patients in the CPMS, is one of four ERNs that serve as foundation of Solve-RD, and has established an RND training and education program. The network will facilitate trial readiness through the establishment of an ERN-RND registry with a minimal data of all patients seen at the ERN-RND centers, thus providing a unique overview of existing genotype-based cohorts. The overall aim of the ERNs is to improve access for patients with RDs to quality diagnosis, care, and treatment.
Travel of Knowledge Put Into Practice-The Clinical Patient Management System To improve the diagnosis and treatment of RDs in practical terms, the European Commission has set up the Clinical Patient Management System (CPMS 3 ), a web-based clinical software application, enabling secure remote multi-national and multidisciplinary case discussions. The CPMS can be used by healthcare professionals of all ERNs, who can upload patient data in a structured manner following an informed consent procedure. Clinicians from outside the ERNs can request CPMS-based advice from ERNs on specific patients through referring them to the nearest national ERN healthcare provider. So far, over 40 CPMS case discussions have been performed by ERN-RND members.
To define the minimum quality and interoperability criteria for RND registries allowing the exchange of data between existing registries and the ERN-RND registry. Building on existing mature RND European disease networks, such as the European Huntington's Disease Network 4  , the Ataxia Study Group 5  , and DystoniaNet 6  , which have already strong clinical collaborations, ERN-RND focuses on the following disease groups at the moment:.
REGISTRIES AND RESEARCH With the foundation of ERN-RND, a large network of potential trial sites for RND has been formed. As the ERN-RND expertise centers are very likely those centers that are performing and will perform RND treatment trials in Europe, the network will facilitate trial readiness through three activities. Firstly, it is about to establish an ERN-RND registry that is going to comprise minimal data of all patients seen at the ERN-RND centers. This registry will thus provide a unique overview of existing genotype-based cohorts. The minimal data set being used is based on the "Set of common data elements for Rare Diseases Registration" as recommended by the European Platform on Rare Disease Registration 14. Secondly, ERN-RND has organized and will be organizing multi-stakeholder workshops focusing on the different aspects of trial readiness.
Tower-Based GHG Measurements Data were also collected on a network of instrumented communications towers within the ACT-America sampling domain. 11 towers were deployed to spatially complement the routine long-term tower measurements of the NOAA Global Greenhouse Gas Reference Network (GGGRN;  Andrews et al. , 2014). Figure  1f  shows the location of these 11 towers along with current active GGGRN sites (including in situ towers and airborne flasks) within the central and eastern US (see Table  S1  for site coordinates and measurement height). Tower-based instruments measured GHGs mole fractions continuously at about 100m AGL using the Picarro G2301 CRDS. Tower-based measurements began in early 2015 and continued through 2019. Data from 11 towers are provided, although not all towers have data from all years  (Miles et al.
Data Calibration and QA/QC During the field campaigns, the individual instrument teams collected data and performed quick QA/QC checks and processing using initial calibrations to produce preliminary data, which were used for flight plan-  ning, assessing instrument operations, and determining progress in achieving the overall project sampling strategy. After the completion of each campaign, the instrument teams performed their full data processing (e. g. , applying final calibrations and measurement synchronization) and more rigorous QA/QC process to generate publication-quality data. A summary of the calibration and QA/QC procedures used by the major instruments and measurements of ACT-America is provided in Text S1. Data may be revised even after this point; for example, as instrument issues are revealed through additional analysis, as new trace gas calibration scales are adopted, or because the processing depends on final data from another instrument team.
Data Management at the LaRC Field Data Repository The goal of the field data repository is to facilitate data exchange within the science team so that they can efficiently generate publication-quality data products. The ACT-America field data repository was operated by the Suborbital Science Data for Atmospheric Composition (SSD-AC) group at NASA LaRC. During the campaign periods, the field data repository served several important functions, including hosting the preliminary/quick-look data, providing secure data access for science team members and collaborators, and ensuring that data adhered to standard file naming and format conventions to improve data usability. In situ ACT-America data products are reported in the International Consortium for Atmospheric Research on Transport and Transformation (ICARTT) v1.
1 format  (Aknan et al. , 2013). Raw lidar remote sensing data products are provided in the HDF5 (Hierarchical Data Format 5) format and the derived products (e. g. , ABL depths) are provided in the ICARTT v1. 1 format. The ICARTT file format contains critical metadata including the investigator, variable names and descriptions, estimated measurement uncertainty, missing data and limit of detection flags, a brief instrument description, flight date, and version number, most recent data revision date, and revision history. Files in the ICARTT format were additionally checked against the format standards, particularly when the sampling time stamp is monotonically increasing, no overlaps exist between sampling time intervals, and the data codes for missing data and limits of detection are properly used.
During the course of the project, there were 5,552 preliminary and 6,579 publication-quality data files submitted to the field data repository, including data revisions (e. g. ,  Davis et al. , 2018; Pal, 2019; Pal et al. , 2020b). Another major function of the SSD-AC group was to generate merged products, which combined all in situ airborne measurements onto common time bases (see Section 3). Preliminary data typically went through multiple revisions. The publication-quality data is nominally submitted within six months of the end of each field campaign, though some data were subsequently revised (e. g. , due to improved calibration and/or processing methods). The publication-quality data were released to the public and transferred to the ORNL DAAC for long-term preservation and public distribution.
Data Management and Publication at the ORNL DAAC The ORNL DAAC is one of 12 NASA Earth Observing System Data and Information System data centers, which provides open access to data from NASA's Earth Science Missions. The ORNL DAAC is the archive of record for the final ACT-America data products. Upon receipt of the ACT-America data, the ORNL DAAC evaluated the data for completeness and structure, created Climate and Forecasting (CF, https:// cfconventions. org) compliant netCDF (Network Common Data Form) files from the ICARTT source data, and archived and distributed files in both formats. In addition to being more broadly standards-compliant, self-describing, and consistent with interoperability best practices (see https://daac. ornl. gov/datamanagement), providing these netCDF files allow data users to take advantage of the numerous tools and opensource libraries that have been developed for netCDF data.
These tools include the Thematic Real-time Environmental Distributed Data Services (THREDDS; https://daac. ornl. gov/cgi-bin/service_dataset_lister. pl?svc_id=4), which provides access and subsetting capabilities for netCDF data via programmatic and graphical interfaces. ACT-America used an ICARTT-style file naming convention for all observational data products following the pattern: ACTAMERICA-instrument_platformID_YYYYMMDD_R#_L#. ext, where instrument is one of the instrument names listed in Table  1 , platformID = "B200' or "C130', "merge", or "Ground-"+ ground site name. YYYYMMDD = flight date in UTC time, R# = revision number (higher number indicates a more recent revision), L# = optional launch number (some B-200 flights had more than one sortie or launch on a single day), ext = file extension with either ".
nc"/". nc4' for NetCDF or ". ict' for ICARTT. For example, ACTAMERICA-Ozone_B200_20160726_R1_L2. nc is the netCDF file that contains the revision 1 of measurements made by the ozone instrument onboard the B-200 aircraft during its second launch on July 26, 2016. Data files were grouped into a number of data products for publication at the ORNL DAAC, with each data product typically containing measurements from one instrument or a set of related instruments at a specific processing level (e. g. , original or processed data set). The ORNL DAAC also prepared comprehensive NASA-compliant metadata and a detailed user guide to accompany each ACT-America data product. Each user guide includes the following information: Data citation, overview and description, spatial and temporal coverage and resolution, number of data files, file formats and standards, file naming conventions, data dictionary including all measured parameters, units, and description, data application and derivation, quality assessment and uncertainty information, detailed data acquisition and methods section, data access instructions, and references.