dataesr/scientific-paragraphs-categorization huggingface dataset:
This search paradigm is a low-cost solution for scientific domain repositories to expose their data holdings in a highly cross-disciplinary manner. In effect, the LTG participants advocated that we change the paradigm from "build data repository, data will come" to "publish data online, cybertools will find. " Participants argued for less money for building data repositories and more for improving the capabilities of tag and search. With this new paradigm, every data provider would put their data into an online data repository with appropriate metadata that are tagged during upload or after, enabling future data discovery. Some researchers might go into datasets posted by others and tag them, just as internet users tag online photographs for Google Search, and funding agencies could reward this activity if specific data types were deemed especially important.
While this shift would mean that reusability and interoperability of data would not be possible until tagging and search tools became available, the data publication process would be less onerous for the data providers, and would likely result over the long run in more data uploads with appropriate metadata. Examples of the type of search tools that are needed are beginning to appear. For example, the Data Observation Network for the Earth (DataONE) currently enables cross-search amongst registered member nodes using indexed metadata. DataONE, a community project, links data repositories as well as providing data search functionality (https://www. dataone. org/). Another example is Google Dataset Search, which is built around a metadata vocabulary and codes created and maintained by Schema.
org. Schema. org, only recently adapted to Earth science data through the NSF-funded EarthCube 418 (https://www. earthcube. org/p418) and 419 projects (https://www. earthcube. org/p419), provides structured vocabulary that can be used to encode metadata, keywords, and web urls into a machine readable format. Google Dataset Search crawls these encoded datasets, extracts metadata attributes, and catalogs them for search. The result is a catalog of datasets from many different sources, including data repositories, that can easily be searched via datasetsearch. google. com or from a more community-specific portal such as GeoCodes (e. g,https://geocodes. earthcube. org/geocodes/textSearch. html). End users in different disciplines can query and discover data across scientific domains and disciplines from a single access point.
Several data repositories already expose datasets in a manner that allows them to be found by Google Dataset Search. Such capabilities for dataset search again point to the need for controlled vocabularies that can be indexed to promote discovery of precise data types and terms. Data harmonization. The slow growth of geochemical databases (LEHNERT AND ALBAREDE, 2019) despite the emerging importance of meta-analysis in many disciplines emphasizes that the geochemistry community neither prioritizes nor rewards systematic data publication in repositories nor data standards. Journal editors and funders can mandate movement through the bottleneck in Figure  2 , and data search cybertools can help. But none of the data repositories --specialized, targeted, and structured or generalized and unstructured --have required data harmonization: providers simply do not use the same method of sample preparation, analysis, nor reporting.
For example, in the USGS National Water Information System, 32 different name-unit conventions are used for dissolved nitrate alone  (SHAUGHNESSY et al. , 2019). In some cases, monitoring networks and government agencies have imposed common standards across specific projects, but among the networks, agencies, and projects, no norms have been established. This data harmonization problem will only be resolved when LTG practitioners themselves develop and accept standardized formats and controlled vocabularies across their discipline. This in turn will likely only happen if the community begins to prioritize and reward integrated databases and meta-analyses. It is possible, however, that data harmonization never emerges organically from the community because of all the issues discussed earlier in this paper (e.
g. , differences in analytical needs for different types or provenances of samples, and the high premium placed by LTG on development of new analyses). If the datasets are crucial enough, agencies could require or impose harmonization. Alternately, an agency could fund groups to help communities develop reporting formats, along the lines of the community-driven strategy used to develop ESS-DIVE (Table  1 ). This latter strategy tries to involve a lot of contributors brokering agreements (see, for example, http://essdive. lbl. gov/community-projects/). It is also possible that cybertools could help in the problem of harmonization. Specifically, some funders have promoted the development and expansion of "translators" or thesauruses for controlled vocabularies used for data. For example, Skomos/OZCAR provides lists of closely related controlled vocabulary terms and their sources.
Searching at https://in-situ. theialand. fr/skosmos/theia_ozcar_thesaurus/en/ for "ammonium" yields a list of 17 cognates, organized as "Exactly Matching Concepts", "Closely Matching Concepts" and "Related Matching Concepts" with links to the source of each one. This thesaurus can be used in combination with web search tools to identify common data across multiple databases that use different vocabularies, controlled or otherwise. Such thesauruses will only be successful, however, if LTG scientists work closely with data scientists to bring cyber-and subject-matter expertise together. As pointed out for a related problem by Schroeder (2018), computers can help impose some harmonization but if algorithms to relate datasets are not agreed upon, then cybertools cannot solve the problem. This points again to the need for education of LTG students to promote better systematic data management in the future.
This means that the geochemical datascape will grow to include both highly structured super-databases (e. g. , PetDB and CUAHSI HIS) as well as less well-structured repositories (e. g. EarthChem, HydroShare, ESS-DIVE). As this data-scape emerges along with powerful cybertools for search, increasingly powerful answers to LTG questions will arise. All of these data transformations within LTG require a new emphasis on data science for LTG professionals and especially, for training future generation LTG scientists. Repositories in the first two categories and sometimes the third typically issue DOIs. Importantly, a data repository may or may not require specific preparation, analytical methods, and/or data reporting styles. Data set or database A group of data values for a given project, with some metadata.
Sample A physical entity that could be archived. Template Form with pre-set structure for data input. 1. Improving the accessibility of geochemical data promotes better science and better societal decisionmaking. 2. Although the data enterprise from measurement to meta-analysis is complex and fraught with opportunities for error, systematic management of data leads to improvements in data quality and promotes identification of large-scale trends. 3. Highly structured relational databases may be useful for LTG data where geochemical measurement protocols are reproduced identically in many locations on many samples, but less structured data repositories are needed for measurements that are implemented differently from sample to sample or place to place, or where protocols are developing or non-standardized.
4. The many goals and workflows of LTG scientists have resulted in a multitude of different data structures and metadata requirements, and a proliferation in the number of data repositories and portals housing or pointing to LTG data. 5. LTG scientists often resist sharing data in online data management systems. 6. The data-scape for LTG must encompass i) flexible management systems for datasets where measurement methods are less routine or still under development, ii) highly structured and managed data systems for datasets with established standards for measurement, and iii) a process whereby data systems can sometimes evolve from mode (i) to mode (ii). 7. Standards for data and meta-data are only developed when the community or the science demands it. 8. LTG scientists need both archived (unchanging) and versioned (modified and updatable) datasets.
Acknowledgements The workshop, held Feb. 18 to 20, 2020 in Atlanta GA at the  Georgia Tech Conference Center , was funded by  NSF   EAR 19-39257  to SLB and organized by  S. L. Brantley ,  T. Wen ,  D. Agarwal , and  J. Catalano. All participants engaged in the ideas in the paper but some, not listed as coauthors, did not work on this document. Those latter participants included  Enriqueta Barrera ,  Phil Bennett ,  Omar Harvey ,  Robert Hazen ,  Nadine Kabengi ,  Miguel Leon ,  Shaunna Morrison ,  Eric Pierce ,  Christopher Reinhard ,  Jinyun Tang , and  Ken Williams. Four webinars were run prior to the workshop:  Deb Agarwal  ( 11/25/2019 ); X. Ma ( 12/16/ 2019);  K. Lehnert  ( 1/17/2020 );  J. Bales  (1/31/2020). Twenty-four institutions were represented. Support for PAS is acknowledged from  EAR-GEO-1331846.
Data repositories can be of many types, which include general purpose repositories that accept any types of data (e. g. Figshare, Dryad), funder or institutional or national cross-domain repositories (e. g. ESS-DIVE, CUAHSI HIS), and domain-specific repositories that are theme-based (e. g. NCBI, PetDB). Science Environmental Systems Science program. A portal to data describing porous micro-structures, DRP (Digital Rocks Portal) especially for the fields of hydrocarbon resources, https://www. digitalrocksportal. org/ environmental engineering, and geology. Repository for geochemical EarthChem Library datasets (analytical data, experimental data, http://earthchem. org/library synthesis databases). ECOSTRESS Spectral Library The ECOSTRESS spectral and human-made materials. library is a compilation of over 3400 spectra of natural https://speclib.
jpl. nasa. gov/ EDI NSF funded data portal for (Environment-al Data data from the Long-Term Ecological Research https://portal. edirepository. org/nis/home. jsp Initiative) network. U. S. Environmental Protection Agency's water EPA WQX quality monitoring data from lakes, streams, rivers, https://www. epa. gov/waterdata/water-quality-data-wqx and other types of water bodies. GDR (Geothermal Repository) Data Data collected from researchers funded by US Office. Geothermal Technologies Dept. of Energy https://gdr. openei. org/ GeoReM Max Planck Institute (Geological database for reference and Environmental materials (rocks, glasses, minerals, isotopes, http://georem. mpch-mainz. gwdg. de/ Reference biological, river water, Materials) seawater). GEOROC (Geochemistry of Rocks of Continents) and the Oceans Max Planck Institute database with published mantle xenoliths).
There are several reviews that fall within the scope of smoking and COVID-19  [11, [13] [14] [15] [16] [17] [18]. We aimed to produce a rapid synthesis of available evidence pertaining to the rates of infection, hospitalization, disease severity and mortality from SARS-CoV-2/COVID-19 stratified by smoking status. Given the increasing availability of data on this topic, this is a living review with regular updates. As evidence accumulates, the review will be expanded to include studies reporting COVID-19 outcomes by alternative nicotine use (e. g. nicotine replacement therapy or e-cigarettes).
Study design This is a living evidence review, which is updated as new evidence becomes available  [19]. We adopted recommended best practice for rapid evidence reviews, which involved limiting the search to main databases and having one reviewer extract the data and another verify  [20]. This study was not pre-registered, but evolved from a report written for a UK medical society  [21]. The most recent (and all future) version(s) of this living review is https:// www. qeios. com/read/latest-UJR2AW. A completed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist is included in Supporting information, Fig. S1.
Quality appraisal The quality of included studies was assessed to determine suitability for inclusion in meta-analyses. Studies were judged as 'good' quality if they: (i) had < 20% missing data on smoking status and used a reliable self-report measure that distinguished between current, former and never smoking status; AND (ii) used biochemical verification of smoking status and reported results from adjusted analyses; OR reported data from a representative/random sample. Studies were rated as 'fair' if they fulfilled only criterion (i) and were otherwise rated as 'poor'. The quality appraisal was conducted by one reviewer and verified by a second.
Evidence synthesis A narrative synthesis was conducted. Data from 'good' and 'fair' quality studies were pooled in R version 3. 6. 3 [22]. In a living review where new data are regularly added to the analyses, it may be more appropriate to use a Bayesian (as opposed to frequentist) approach where prior knowledge is used in combination with new data to estimate a posterior risk distribution. A Bayesian approach mitigates against the issue of performing multiple statistical tests, which can inflate family-wise error. A series of random-effects hierarchical Bayesian meta-analyses were performed with the brms  [23]  package to estimate the relative risk for each comparison with accompanying 95% credible intervals (CrIs). We first defined prior distributions for the true pooled effect size (μ) and the between-study heterogeneity (τ), with μ specified as a normal distribution with a mean equal to the derived point estimate from each comparison of interest in the immediately preceding version of this living review  [24] , and τ specified as a half-Cauchy distribution with a mean of 0 and standard deviation of 1.
Smoking status Categorization of smoking status was heterogeneous (see Table  1 ). One hundred and forty-five studies collected data on smoking status through routine electronic health records (EHRs), 59 studies used a bespoke case report form for COVID-19 and 29 studies did not state the source for information on smoking status. None of the studies verified smoking status biochemically. Notably, only 57 (24. 4%) studies reported current, former and never smoking status (see Supporting information, Table  S2a ), with a further 17 studies reporting ever and never smoking status (see Supporting information, Table  S2b ). The remaining 159 studies reported current, current/former or current and former smoking status but did not explicitly state whether remaining participants were never smokers or if data were missing on smoking status (see Supporting information, Table  S2c ).
Limitations This living rapid evidence review was limited by having a single reviewer extracting data with a second independently verifying the data extracted to minimize errors, restricting the search to one electronic database and one pre-print server and by not including at least three large population surveys due to their reliance upon self-reported suspected or confirmed SARS-CoV-2 infection (which means they do not meet our eligibility criteria)  [35, 45, 46]. We also did not include a large, UK-based, representative seroprevalence study  [47]  in our metaanalyses, as the odds of testing positive in former smokers was not reported. However, the odds of infection for current smokers (odds ratio = 0. 64, 95% CI = 0. 58-0. 71) was in concordance with the pooled estimate in our meta-analysis.
Population surveys-particularly with linked data on confirmed infection or antibodies-will be included in future review versions to help mitigate some of the limitations of healthcare based observational studies. The comparisons of current and former smoking prevalence in the included studies with national prevalence estimates did not adjust observed prevalence for the demographic profile of those tested/admitted to hospital. Other reviews focused on this comparison have applied adjustments for sex and age, and continue to find lower than expected prevalence-notwithstanding the issues complicating interpretation described above  [17].
Acknowledgements An original short review for the  Royal College of Physicians  was converted to an extended living review after a request by  Martin Dockrell ,  Tobacco Control Lead ,  Public Health England. All scientific decisions were made by the authors independently of funders and external organizations. The authors would like to thank  Rosemary Koper  for her assistance in running the electronic searches and data extraction. D. S. is supported by a PhD studentship from the  UK Biotechnology and Biological Sciences Research Council  ( BB/M009513/1 ). O. P. receives salary support from  Cancer Research UK  ( C1417/A22962 ). J. B. , L. S. and O. P. are members of SPECTRUM, a  UK Prevention Research Partnership Consortium  ( MR/S037519/1 ). UKPRP is an initiative funded by the  UK Research and Innovation Councils , the  Department of Health and Social Care (England)  and the UK devolved administrations, and leading health research charities.
(F) NF-kB and IRF3 activities in ZIKV-infected cells (MOI 1. 0) was measured at the indicated time intervals using the same readouts as in (D) and (E). (G) NF-kB and IRF3 activities in EMCV-infected cells infected at MOI 1. 0 for the indicated time intervals and measured as in (D) and (E). (H) A549 dual cells were stimulated with 10 mg/mL of 2 0 3 0 -cGAMP. NF-kB induction was determined by QUANTI-blue by measuring optical density (OD) at 620 nm; IRF induction was determined by QUANTI-Luc in a luminometer. (I) Cells in (H) were stimulated with 10 mg/mL of linearized 2 0 3 0 -GAMP as a negative control. All curve fits were generated with data from at least 3 independent experiments. The images in (B) and (C) are representative of at least 3 independent biological replicates. To detect RIG-I$K63 association under the conditions described above, we immunoprecipitated myc-RIG-I from the reaction mix and immunoblotted with linkage-specific anti-ubiquitin antibodies.
To determine which E2s might function in this cascade, we generated cells depleted of the known E2 interactors of OTUB1, UBCH5(a-c), and UBC13. Depletion of UBC13, which functions in TRIM25-dependent K63 ubiquitylation of RIG-I, resulted in abrogation of IRF3 activity, whereas that of UBCH5c in particular allowed rapid IRF3 dimerization, reminiscent of OTUB1 rescue assays. Our data suggest that OTUB1 (F and G) OTUB1 À/À A549 dual cells were reconstituted with either WT, C91S, or D88A constructs of OTUB1 and stimulated with (F) poly(I:C) or (G) H1N1 (MOI 1. 0) for the indicated time intervals. IRF induction was determined by QUANTI-Luc in a luminometer. (H) IFN production was measured from IAV-infected (H1N1(pdm), MOI 1. 0) WT and OTUB1 À/À A549 cells or those expressing either C91S or D88A mutants in the OTUB1 À/À background.
MS/MS spectra were analyzed using Sequest algorithm. The target sequences comprised the human protein repository of the Uniprot database and protein sequences corresponding to the Influenza A PR8, WSN and H1N1 pandemic strains. Decoy sequences were obtained upon reversing the orientation of target sequences. Allowed criteria for searches required trypsin cleavage (two missed cleavages allowed), peptide mass tolerance of 20 p. p. m. , variable oxidation of methionine residues, and static carbamylation modification of cysteine residues. Peptide-spectrum matches were determined with estimated false discovery rate of 1%. Spectral counts for each condition were combined at a protein level and normalized by protein length to infer protein abundances and intensities in each case. Identified hits were further categorized into different biological pathways using Ingenuity Pathway Analysis software.
D, unless stated otherwise. Comparisons between two populations of data (e. g control and sample) were made using the Student's unpaired t test with a confidence limit for significance set at % 0. 05. For multi sample comparisons, two-way ANOVA was performed. All data generated were assumed to follow normal distribution and homogeneity of variance based on Q-Q plots and Levene's test. Statistical analyses were performed in GraphPad PRISM 8. 0. and Bioconductor package on RStudio. Pathway enrichment of hits from BioID assay was performed using Ingenuity Pathway Analysis software.
ACKNOWLEDGMENTS This work was funded by  Research Grants Council (GRF)  grants  17113915  and  17112617  and partially supported by  Health and Medical Research Funds  ( 16150592 ), a theme-based research grant from the  Research Grants Council  (project  T11-705/14N ), and  PTR  ( 546 ) from  Institut Pasteur. S. S. is supported by the  Croucher Foundation. The authors thank  Michael Weekes  and  Benedikt Kessler  for suggestions regarding mass spectrometry analyses.
SModelS We start by checking the constraints from searches for promptly decaying new particles with SModelS  [102] [103] [104] [105]. The working principle of SModelS is to decompose all signatures occurring in a given model or scenario into simplified model topologies, also referred to as simplified model spectra (SMS). Each SMS is defined by the masses of the BSM states, the vertex structure, and the SM and BSM final states. After this decomposition, the signal weights, determined in terms of cross-sections times branching ratios, σ × BR, are matched against a database of LHC results. SModelS reports its results in the form of r-values, defined as the ratio of the theory prediction over the observed upper limit, for each experimental constraint that is matched in the database. All points for which at least one r-value equals or exceeds unity (r max ≥ 1) are considered as excluded.
Concretely we are using SModelS v1. 2. 3  [105]. For our purpose, the most relevant "prompt" search results from Run 2 included in the v1. The SLHA files produced with SPheno in our MCMC scan contain the mass spectrum and decay tables. For evaluating the simplified model constraints with SModelS, also the LHC cross sections at s = 8 and 13 TeV are needed. They are conveniently added to the SLHA files by means of the SModelS-micrOMEGAs interface  [90] , which moreover automatically produces the correct particles. py file to declare the even and odd particle content for SModelS. Once the cross sections are computed, the evaluation of LHC constraints in SModelS takes a few seconds per point, which makes it possible to check the full dataset of 52. 5k scan points. The results are shown in Figures  10  and 11.
Constraints from searches for long-lived particles As mentioned in section 4. 1, a relevant fraction (about 20%) of the points in our dataset contain LLPs. Long-lived charginos, which occur in about 14% of all points, can be constrained by Heavy Stable Charged Particles (HSCP) and Disappearing Tracks (DT) searches. Displaced vertex (DV) searches could potentially be sensitive to long-lived neutralinos; in our case however, the decay products of long-lived neutralinos are typically soft photons, and there is no ATLAS or CMS analysis which would be sensitive to these. We therefore concentrate on constraints from HSCP and DT searches. They can conveniently be treated in the context of simplified models. For HSCP constraints we again use SModelS, which has upper limit and efficiency maps from the full 8 TeV  [123]  and early 13 TeV (13 fb -1 )  [124]  CMS analyses implemented.