dataesr/scientific-paragraphs-categorization huggingface dataset:
In the case of GRT-1, significant changes occur between the 2012 dataset (prior to reservoir stimulation operations) and the 2013-2015 datasets (after stimulation). Panel (b) of Fig. 18  shows that the changes in the S H stresses between 2012 and 2015 in GRT-1 are larger than our measurement uncertainty for 15 % of the measurements and show principal stress increases. This change can be fully explained by the thermal equilibration of the well. The uncertainty on our data does not allow us to relate stress changes to the reservoir stimulation operations. Cornet (2016)  showed that largescale fluid injections conducted at the Soultz-sous-Forêts site generated large-scale failure zones whose orientation varies with depth. Based on the analyses of borehole failures, considerable stress orientation variations were also highlighted with depth at Rittershoffen  (Hehn et al.
This contradicts the common assumption that breakouts do not widen but only deepen until the borehole reaches a new stable state  (Zoback et al. , 2003). 3. The changes in breakout width occur between datasets collected prior to and after the reservoir stimulation that took place in 2013. However, the most likely effect on breakout width is the thermal equilibration of the wellbore, and our data do not provide evidence of stress changes resulting from reservoir stimulation. In addition to this analysis, a study of the geometry of borehole failures in both wells leads us to propose a characterization of the in situ stress tensor at depth, including the orientation and magnitude of the three principal stresses. This detailed stress state analysis includes the estimation of thermal stresses. A Mohr-Coulomb criterion is retained here to estimate the principal stress magnitude as it is in our parameterization, which is the most consistent with a frictional strength limit in the crust.
However, such a step is determined by the choice of the failure criterion and its parameterization, which is uncertain at Rittershoffen. 3. S H is generally slightly larger than the vertical principal stresses Sv consistently with a strike-slip to normal faulting transitional regime. This is consistent with stress characterization at the neighbor site of Soultzsous-Forêts  (Cornet et al. , 2007; Klee and Rummel, 1993; Valley and Evans, 2007b)  The Rittershoffen borehole imaging dataset is unique in that repeated logging allowed for the study of the temporal evolution of borehole breakouts and the possible stress changes induced by reservoir stimulation. Our results change the common view that breakouts mostly deepen but do not widen. Further work is, however, required to reduce the uncertainties related to stress magnitude estimates from borehole breakouts and to quantify stress changes induced by reservoir stimulation.
Acknowledgements. We thank  ÉS-Géothermie , a subsidiary company of  Électricité de Strasbourg (ÉS) , for support and allowing us to use the borehole data on wells GRT-1 and  GRT-2 of the Rittershoffen ECOGI  project. A part of this work was conducted in the framework of the  EGS Alsace  project, which was cofounded by  ADEME. We would like to thank the  Swiss Competence Center for Energy Research-Supply of Electricity  ( SCCER-SoE ) for support of the study. The present work has been done under the framework of LABEX  ANR-11-LABX-0050-G-EAU-THERMIE-PROFONDE  and benefits from state funding managed by the  French National Research Agency (ANR)  as part of the  "Investments for the Future" program. Financial support. This research has been supported by the  EU H2020   DESTRESS  Project (grant no.
Our findings raise implications about romantic passion in the form of a kiss, within human pair-bonds. Although kissing, in contrast to romantic passion, is not a human universal  11  (see also  33  ) data from remote societies suggests that social complexity, indexed via stratification, predicts the likelihood of observing romantic kissing in that culture  11  (same dataset as used in  27  ). Social complexity and increased group competition for scarce resources was thought to be a key driver in the evolution of human social intelligence, selecting for those who could monitor relations between group members as groups increased in size  34. Evidence that relative poverty predicts kissing frequency raises the possibility that this gesture evolved in humans to maintain pair bonds in light of the constraints of that environment.
Methods participants. Three-thousand one hundred nine participants (M age = 31. 90 years, SD = 11. 60 years) were recruited to an online study. Data collection ended after collating data from 13 countries (6 continents), with countries included in cross-national comparisons if we obtained data from at least 50 respondents (who reside in the same country as their birth) and responses to our kissing questionnaire from at least 30 women. Participants were recruited from campuses and the wider community, research participant pools, word of mouth, twitter, academic groups on social media and a press release from the lead author's communications department. This press release informed readers that we were conducting a global study into how people across cultures express themselves in romantic relationships, but did not mention national health or national wealth.
Given difficulty in recruiting an African sample, a sample of Nigerians were recruited via the buy responses function on surveymonkey. com. No participants were reimbursed for their time. Duplicate responses from the same device were not permitted by the survey platform. All procedures for testing and recruitment were approved via the lead author's Ethics Committee (School of Social and Health Sciences, Abertay University) and run in accordance with the ethical principles and guidelines of the British Psychological Society. Hypotheses, methods and analyses were pre-registered (https://osf. io/ pbqwm/). All aspects of the pre-registration report are identical to the current manuscript, except for aspects of the analytical strategy following discussion between the first, second and third authors in response to valuable comments from the Reviewers about the suitability of our data for analyses via LMEM.
Acknowledgements We are grateful to many colleagues for supporting the research, including  Clare Cunningham  (internal funding for Nigerian sample),  Gislene Rocha ,  Carlota Batres ,  Elena Rusconi ,  Corinne Jola ,  Alexandre Schwob  (help with translations),  Graeme Bletcher ,  Jitka Fialova ,  Colin McLean  and  Tomasso Querci. We are also grateful to the anonymous Reviewers for helpful comments on the manuscript.
The genome structures for the modified CBSV_Tanza ICs are provided in Fig. 1  and sequences are available on the NCBI database under the accession numbers MK409379 (CBSV_GFP1), MK409380 (CBSV_GFP2) and MK409381 (CBSV_UCP). For CBSV_GFP1/2 ICs proteolytic cleavage sites were included flanking GFP to enable cleavage of from the CBSV polyprotein. In CBSV_GFP1, the Ham1-CP cleavage site: I-D-V-Q-/A was added either side of GFP, whereas in CBSV_GFP2, the NIa-NIb cleavage site: I-S-V-Q-/A was added to the 5′ of the GFP sequence and no cleavage site was necessary at the 3′ end, as GFP is the last peptide in the polyprotein (Fig. S1 ). Proteolytic cleavage site sequences were designed so that third base of each codon was modified to a different nucleotide to encode the same amino acid but reduce spurious homology with the corresponding cleavage sequence elsewhere in the genome.
Background: Antimicrobial resistance (AMR) of bacterial pathogens is an emerging public health threat. This threat extends to pets as it also compromises our ability to treat their infections. Surveillance programs in the United States have traditionally focused on collecting data from food animals, foods, and people. The Veterinary Laboratory Investigation and Response Network (Vet-LIRN), a national network of 45 veterinary diagnostic laboratories, tested the antimicrobial susceptibility of clinically relevant bacterial isolates from animals, with companion animal species represented for the first time in a monitoring program. During 2017, we systematically collected and tested 1968 isolates. To identify genetic determinants associated with AMR and the potential genetic relatedness of animal and human strains, whole genome sequencing (WGS) was performed on 192 isolates: 69 Salmonella enterica (all animal sources), 63 Escherichia coli (dogs), and 60 Staphylococcus pseudintermedius (dogs).
The data from such studies can also be used to develop antimicrobial use (AMU) guidelines to educate veterinarians on the principles of good antimicrobial stewardship in their daily practice. Since the health of humans and animals are intricately linked, this data source is one of the critical components of One Health surveillance  [6]. In March of 2015, the United States National Action Plan for Combating Antibiotic-Resistant Bacteria (CARB) was released to guide government, public heath, healthcare, and veterinary partners in addressing the AMR threat  [7]. The National Action Plan specifically charged the FDA Veterinary Laboratory Investigation and Response Network (Vet-LIRN) with developing, expanding, and maintaining capacity in veterinary and food safety laboratories to conduct standardized antimicrobial susceptibility testing (AST) and characterize priority animal pathogens through whole genome sequencing (WGS).
enterica and Enterobacter cancerogenus, respectively). Two other isolates, one Salmonella (SAL-17-VL-SD-NC-0013) and one S. pseudintermedius (SPSE-17-VL-LA-KY-0018) were excluded after the majority of the assembly length was classified as a different species (Citrobacter braakii and S. schleiferi, respectively). The final dataset consisted of 63 E. coli, 69 S. enterica, and 60 S. pseudintermedius sequences.
Isolates from Canada were serotyped by Public Health Agency of Canada National Microbiology Laboratory. Laboratories were instructed to select only one isolate per client submission. Isolate species were determined by either analytical profile index (API), matrix assisted laser desorption/ionizationtime of flight (MALDI-TOF) mass spectrometry, polymerase chain reaction (PCR), Sensititre, Vitek, or biochemical identification. A frozen aliquot of each isolate was sent to the corresponding WGS laboratory. Each quarter, Vet-LIRN randomly selected one isolate of each pathogen species from each source laboratory to be sequenced. Source laboratories submitted metadata for each isolate, while anonymizing certain features by omitting specific geographic location and client information. In the U. S.
, veterinarians are required by the Principles of Veterinary Medical Ethics  [28] , and by law  [29] , to keep the medical records of their patients confidential. Metadata was collected using the metadata sheet developed by the GenomeTrakr program  [30] , with additional information required by the Vet-LIRN Program Office. Those fields included the information on which source lab collected the isolate, Vet-LIRN specific isolate ID, isolate taxonomic name, date of collection (day, month, or year), U. S. state, specific animal host, case type (primary, secondary, tertiary), as well as the anatomical site from which the pathogen was isolated. A complete metadata sheet template is provided as Additional file 1. Four sequencing laboratories ("WGS labs") each had five collaborating source labs (Fig.
Whole genome sequencing (WGS) After harmonizing the test method across four different laboratories and passing an FDA GenomeTrakr program proficiency test, Vet-LIRN WGS laboratories sequenced the isolates. DNA was extracted from either a single colony, or a pellet of a liquid culture from a single colony, using the DNeasy Blood and Tissue Kit (QIAGEN Sciences, Germantown, MD). DNA quality control was performed using Qubit instrumentation and reagents (Thermo Fisher Scientific, Waltham, MA). Genomic libraries were prepared following the Nextera XT Library Preparation Kit protocol (Illumina, Inc. ) according to the manufacturer's instructions. Laboratories had the option of normalizing libraries either using the Illumina bead-based normalization procedure or by concentration of the purified libraries using Qubit.
Sequence analysis All sequencing reads were uploaded to National Center for Biotechnology Information (NCBI) SRA under BioProjects PRJNA316449, PRJNA314607, and PRJNA316451. Isolate-level accession numbers are listed in Additional file 2. Any samples with an average coverage of less than 30X were repeated until they met this threshold. Low quality segments were removed using the Trimmomatic version 0. 36 sliding window program with a window size of 4 and minimum quality score of 20  [31]. Trimmed reads were then assembled using SPAdes version 3. 10. 1  [32]. Assembly quality was assessed using Quast version 4. 0  [33]  and contigs were classified using Kraken2  [34]. Samples were excluded from further analysis if they showed evidence of substantial contamination. Parsnp was used to generate a core genome alignment phylogenetic tree for each of the three species  [35].
Assemblies were screened for AMR genes in the NCBI and ARG-ANNOT  [36]  databases and plasmids in the Plas-midFinder  [37]  database using ABRicate version 0. 8 (https://github. com/tseemann/abricate). Endogenous and ubiquitously detected resistance genes (ampC, ampH, and penicillin-binding protein in E. coli) and regulatory genes (tetR in S. enterica and E. coli and mecI and mecR1 in S. pseudintermedius) were excluded from antimicrobial resistance gene (ARG) counts but are listed in Additional file 2. Trees and ARG predictions were visualized using iTOL  [38]. Assemblies were annotated using Prokka  [39] , and the gyrA gene was searched for amino acid changes associated with fluoroquinolone resistance: amino acids 83 and 87 in E. coli and Salmonella and 84 in S. pseudintermedius  [25, 40, 41].
Acknowledgements The authors would like to acknowledge: The staff of participating veterinary diagnostic laboratories, for technical assistance:  Cornell University, Animal Health Diagnostic Center, New York :  Brittany D. Chilson. University of Georgia, Athens Veterinary Diagnostic Laboratory, Bacteriology Section. University of Georgia, Tifton Veterinary Diagnostic and Investigational Laboratory, Bacteriology Section. University of Guelph, Canada. Animal Health Laboratory, Bacteriology section. Iowa State University, Department of Veterinary Diagnostic and Production Animal Medicine: Chloe Stinman. Kansas State University, Veterinary Diagnostic Laboratory: Tanya Purvis. Michigan State University, Veterinary Diagnostic Laboratory :  Niesa Kettler  and  Hannah Rawza. Mississippi State University, Veterinary Research and Diagnostic Lab System :  Jana Morgan ,  Heather Perry ,  Jay Kay Thornton.
Louisiana State University, School of Veterinary Medicine, Baton Rouge, LA U18FD005010 -South Dakota State University, Veterinary and Biomedical Sciences, Brookings, SD U18FD005144 -Cornell University, Population Medicine & Diagnostic Sciences, Ithaca, NY U18FD006160 -Florida Department of Agriculture and Consumer Services; Bronson Animal Disease Diagnostic Laboratory, FL U18FD004623 and U18FD006157 -University of Georgia, Athens Veterinary Diagnostic Laboratory, GA U18FD004636 and U18FD006169 -University of Georgia, Tifton Veterinary Diagnostic and Investigational Laboratory, GA U18FD004637 and U18FD006164 -Iowa State University, Department of Veterinary Diagnostic and Production Animal Medicine, IA U18FD004629 and U18FD006156 -Purdue University, Animal Disease Diagnostic Laboratory, IN U18FD004849 -Kansas State University, Veterinary Diagnostic Laboratory, KS U18FD004619 and U18FD006155 -Murray State University, Breathitt Veterinary Center, KY U18FD006173 -Louisiana State University, School of Veterinary Medicine, Baton Rouge, LA 1U18FD004632 and U18FD006175 -Michigan State University, Veterinary Diagnostic Laboratory, MI 1U18FD006151 -University of Minnesota, Veterinary Diagnostic Laboratory, MN U18FD004624 and U18FD006165 -Mississippi State University, Veterinary Research and Diagnostic Lab System, MS U18FD004848 -North Carolina State College of Veterinary Medicine, NC U18FD004847 -North Dakota State University, Veterinary Diagnostic Laboratory, ND U18FD004626 and U18FD006245 -New Jersey Department of Agriculture, Division of Animal Health, NJ U18FD005162 -Oklahoma State University, Oklahoma Animal Disease Diagnostic Laboratory, OK U18FD006172 -Pennsylvania Department of Agriculture, Pennsylvania Veterinary Laboratory, PA U18FD004625 and U81FD006158 -University of Pennsylvania, Pennsylvania Animal Diagnostic Laboratory, New Bolton Center, PA U18FD005164 -University of Pennsylvania, School of Veterinary Medicine, The Ryan Veterinary Hospital Clinical Microbiology Laboratory, PA U18FD004638 and U18FD006171 -Texas A&M University, College of Veterinary Medicine, Clinical Microbiology Laboratory, College Station, TX U18FD004621 and U18FD006180 -Washington State University, College of Veterinary Medicine, Washington Animal Disease Diagnostic Laboratory, WA -LIRN WGS laboratories to conduct WGS Chih-Hao Hsu, from FDA's Center for Veterinary Medicine, Laurel, Maryland, -for development of bioinformatics tools for data analysis Michael Grabenstein, from FDA's Center for Veterinary Medicine, Laurel, Maryland, -for help with database development Maria Sanchez, Ruth Timme and the FDA GenomeTrakr program: -for integrating our data with NCBI and providing data management support William Klimke, Michael Feldgarden and the NCBI Pathogen Detection team.
This led to some oil and gas operators (e. g. BP DELOS) installing deep-ocean observatories near their operations and making data publicly available. Twenty-five years later, the oil and gas sector is again being asked to be more transparent in their operations to be better corporate citizens with regards to climate change. Observation of both physical and biological changes by this sector in the deep oceans and their linkage to increases in greenhouse gases are increasingly seen as necessary to increase long-term corporate performance by reducing company risks (asset, environmental, etc. ) and financial unknowns  (production, operability, etc. ). Partnerships between scientists and the offshore energy industry are particularly beneficial for ocean observing owing to the complementary skills and resources of both groups.
Baseline data on the biological, physical and chemical environment are required for management and to secure the permits for mining activities  (Durden et al. , 2018). As the impacts of mining activities in the deep ocean are expected to be extensive in space and time  (Jones et al. , 2017) , evaluations of impact and recovery from experiments and early mining activities are also critical to inform management and set the policy framework for future exploitation activities. This is particularly critical in the case of mining activities beyond national jurisdiction, where the International Seabed Authority has a remit under the UN Convention on the Law of the Sea to prevent serious harm to the marine environment  (UNCLOS, 1982). As a potentially global industry in its infancy, with internationally coherent regulations, environmental measurements associated with environmental management of deep-sea mining offers great potential to increase the frequency and extent of deep-ocean observations to the benefit of both the mining industry and deep-ocean science.
The ISA is already taking action to harmonize the data provided by its contractors as part of exploration activities to enhance the amount of regional deep-ocean data available. As mining companies move to exploitation, have greater presence in the deep ocean, and begin monitoring activities, the data collected will increase in volume. At present, most data are obtained from individual shipboard expeditions or regular cruise programs. Some physical oceanographic data have been obtained by moorings, put in place for up to 3 years  (Aleynik et al. , 2017). As yet, the only truly multidisciplinary, deep-water seafloor observatories in areas of potential mining interest are on the mid-Atlantic ridge (e. g. , the MoMAR observatory at Lucky Strike;  Ballu et al. , 2008). Furthermore, long-term seafloor or water column observatories and integrated multi-stakeholder cruise programs would result in improvements in the temporal and spatial extent of ocean observations.
Exploration and Mapping Communities Knowing the depth, shape and character of the seafloor is fundamental for ocean science and has been identified by the Decade of Ocean Science for Sustainable Development as a major research and development priority for 2021-2030 (IOC, 2018). Seafloor bathymetry is a foundational dataset for understanding ocean circulation, tides, tsunami forecasting, fishing resources, environmental change, underwater geo-hazards, cable and pipeline routing, mineral extraction, oil and gas exploration and development, and infrastructure construction and maintenance. The ocean exploration community collects bathymetry and other initial characterization datasets using a routine, systematic or "mapping" methodology with the objective of making results publicly available and accessible soon after collection.
The project was launched at the UN Ocean Conference in June 2017 and is aligned with the UN's Sustainable Development Goal #14 to conserve and sustainably use the oceans, seas and marine resources. The Seabed 2030 Project will apply GOOS concepts and establish distributed regional data assembly and coordination centers that will identify existing data from their assigned regions that are not currently in publicly available databases and seek to make these data available. They will also develop protocols for data collection and common tools to assemble and attribute metadata by regional grids using standardized techniques. A Global Data Assembly and Coordination Center (GDACC) will integrate the regional grids into a global grid and distribute to users world-wide. The GEBCO Seabed 2030 Project will encourage and help coordinate and track new survey efforts and facilitate the development of new and innovative technologies to increase the efficiency of seafloor mapping and help to achieve the ambitious goals of the project.
Reusing and Repurposing Data Investigators around the world routinely collect, analyze, and publish data for specific studies or to further scientific knowledge in areas of interest. Collectively, these data represent a vast amount of measurements that, more often than not, could be repurposed. Here we describe two examples of the integration between different communities (scientific disciplines, and observation networks, hazard monitoring, offshore industry) that allow for the innovative use of existing datasets for applications other than what was originally intended. Deep-ocean Assessment and Recording of Tsunami (DART) measurements are routinely made to detect tsunamis in the deep ocean and serve as the basis for warning coastal populations at the time of tsunami propagation. These data represent years, sometimes decades, of consecutive time series of highly resolved pressure and temperature in areas of the deep ocean for which measurements are scarce.
In addition to the use of bottom pressure for investigations into ocean processes, recent investigations into the sonification of bottom pressure time series have shown promise in identifying an earthquake signature in advance of rupture  (McKinney personal communication). This work follows that of  Ballora and Evans (2017)  who sonified hurricane data, noting that "our ears are better at sensing properties that change and fluctuate"  (Ballora and Evans, 2017). Temperature data recorded by DART systems are being used by investigators for validation of global models and to identify climate variability signatures in the deep ocean. The spatial and temporal scales of use of these data, however, are limited due to a calibration process that prohibits inter-record comparisons. Another example involves assimilation and analysis of the Global Navigation Satellite System (GLONASS) data which are being explored to quantify seafloor displacement as a new approach to rapid characterization of a tsunami source.