dataesr/scientific-paragraphs-categorization huggingface dataset:
3. 1 We ran the relativistic Zel'dovich approximation evolution package -0. 1. 10-1da3bed from -body initial conditions, with a resolution of = 128 3 particles, fundamental domain comoving (reference model) size 40 Mpc/ℎ, and the resolution parameter levelmax_EdS = 10, with and without a perturbation as described in Sect. 2. 4. 1. We compared the numerically measured scale factor evolution with the expected FLRW model by defining a residual and a fractional deviation , defined := e / re FLRW / r := e / re FLRW / r e FLRW / r. The fractional deviation describes the numerical deviation as a fraction of the excess expansion that should be generated by the perturbation. Figure  3  (for an initial density perturbation I i = 0. 001) and Fig. 4  (for I i = -0. 001) show that the code behaves as expected to fair numerical accuracy, correctly taking into account the effect of spatial curvature and matching the emergent Friedmannian model.
This would quite likely involve the inclusion of more second-order terms in the algebraic justification and in the coding itself, together with allowing Φ ≠ Ψ for the homogeneous mode. This future extension would not be completely trivial, since the code does not currently include unit tests or regression tests, and the built-in dependence on an implicitly fixed FLRW reference model of ( ) could yield non-linearities that are numerically hard to control. Nevertheless, this would be a promising followup of the current analysis. Another option, but possibly unstable numerically, would be to use the formulae in Sect. 2. 3 to calculate a new effective model at each time step and use this instead of the reference model in all appropriate formulae. The source package for this paper includes an optional patch (not applied by default) that illustrates how this approach might be attempted.
In the case of , we found that out of the two solutions of a quadratic solution, the choice of solution is hardwired. The choice made by is the decaying mode that is implied by linearisation in the first conformal-time derivative of the potential, Φ ,. Thus, inhomogeneous initial conditions in a cosmological simulation that lead to an emergent homogeneous mode that is growing rather than decaying are likely to be excluded by construction in. Work extending to allow the growing mode might provide a useful extension of the code. The checks presented here have been done aiming at long-term reproducibility; the git commit hash of the source package of this paper, for those wishing to confirm that attempted reproductions are using the same version of the source package, is 54398b7. We gratefully acknowledge the use of the following free-software packages and libraries.
2. 0, GNU Multiple Precision Complex library, GNU Multiple Precision Floating-Point Reliably 4. 0. 2, GNU Nano 5. 2, GNU NCURSES 6. 2, GNU Patch 2. 7. 6, GNU Readline 8. 0, GNU Scientific Library 2. 6, GNU Sed 4. 8, GNU Tar 1. 32, GNU Texinfo 6. 7, GNU Wget 1. 20. 3, GNU Which 2. 21, GPL Ghostscript 9. 52, HDF5 library 1. 10. 5, icu release-67-1, inhomog 0. 1. 10-1da3bed, Less 563, Libbsd 0. 10. 0, Libffi 3. 2. 1, libICE 1. 0. 10, Libidn 1. 36, Libjpeg v9b, Libpaper 1. 1. 28, Libpng 1. 6. 37, libpthread-stubs (Xorg) 0. 4, libSM 1. 2. 3, Libtiff 4. 0. 10, libXau (Xorg) 1. 0. 9, libxcb (Xorg) 1. 14, libXdmcp (Xorg) 1. 1. 3, libXext 1. 3. 4, Libxml2 2. 9. 9, libXt 1. 2. 0, Lzip 1. 22-rc2, Metastore (forked) 1. 1. 2-23-fa9170b, mpgrafic 0. 3. 19-4b78328, Open MPI 4. 0. 4, OpenSSL 1. 1. 1a, PatchELF 0.
10, Perl 5. 32. 0, Perl Compatible Regular Expressions 8. 44, Pixman 0. 38. 0, pkg-config 0. 29. 2, Python 3. 8. 5, ramses-scalav 0. 0-482f90f, r-cran 4. 0. 2, Unzip 6. 0, util-Linux 2. 35, util-macros (Xorg) 1. 19. 2, Valgrind 3. 17. 0, X11 library 1. 6. 9, XCB-proto (Xorg) 1. 14, xorgproto 2020. 1, xtrans (Xorg) 1. 4. 0, XZ Utils 5. 2. 5, Zip 3. 0 and Zlib 1. 2. 11. L A T E Xpackages used for producing the pdf file include the following. alegreya 54512 (revision), biber 2. 16, biblatex 3. 16, bitset 1. 3, caption 56771 (revision) , courier 35058 (revision), csquotes 5. 2l, datetime 2. 60, ec 1. 0, enumitem 3. 9, environ 0. 3, epstopdf 2. 28, etoolbox 2. 5k, fancyhdr 4. 0. 1, fmtcount 3. 07, fontaxes 1. 0e, fontspec 2. 7i, footmisc 5. 5b, fp 2. 1d, kastrup 15878 (revision), lastpage 1. 2n, latexpand 1.
Figure3. Emergent-to-reference model scale factor ratios e / r for ansimulation without addition of a perturbation (dotted line) and e / r for an simulation with an initial invariant perturbation I i = 0. 001 (solid line). The expected FLRW evolution e FLRW / r is also plotted as a dashed line, but is visually indistinguishable from the numerically measured value. The residual (Eq. (22)) shows the numerical differences between these two curves. Top: EdS reference model; bottom: ΛCDM. Plain-text data for this figure and Fig. 4at zenodo. 5806028/inhomog_scale_factors_EdS. dat, zenodo. 5806028/inhomog_scale_factors_LCDM. dat.
Figure5. Scale factor ratios e Φ i =0 / r for a standard simulation (dotted line); e Φ i =0. 001 / r for a simulation with an initial potential perturbation Φ i = 0. 001 (solid line); and the expected FLRW evolution of e FLRW / r (as defined in Sect. 2. 3. 5; dashed line), for the decaying mode (negative square root in Eq. A9). Top: EdS reference model; bottom: ΛCDM. The sudden increase in value for the perturbed simulation (solid curve) at an early time step is the effect of the Φ i offset being inserted into the simulation, and this datapoint is removed from residuals to improve readability. The expected and actual curves match prior to this initial peak in the curve, by construction. Plain-text data for this figure and Fig. 6at zenodo. 5806028/gevolution_scale_factors_EdS. dat, zenodo. 5806028/gevolution_scale_factors_LCDM.
Conversion between reference and expected FLRW models. Initial first invariant I i ( ) and initial potential perturbation Φ i (); initial reference model epoch r i ; reference model r and expected FLRW model e current-epoch cosmological parameters; expected ( e FLRW ( r = 1)) and numerically measured ( e ( r = 1)) final scale factor. r 0 and e 0 are in units of km/s/Mpc; all other parameters are dimensionless. Plain-text version available at zenodo. 5806028/flrw_ref_eff_constants. dat.
Before the beginning of the study, the purposeful sampling methodology was used to obtain a variety of predetermined patient profiles by age, sex, disease duration, socio-professional status, type of IA (RA, SpA, PsA), type of DMARD (methotrexate or biologics) and number of previous DMARDs used. Assessment of medication adherence was not an eligibility criterion, nor was previous use of an mhealth app. After the interviews had begun, the sample was completed with the progression of the data collection, according to the approach of the grounded theory model  [33]. A standardized semi-structured interview schedule (Appendix 1 in S1 File) explored 3 main areas: (1) daily life with IA, (2) practices of pharmacological and non-pharmacological treatments, and (3) the patient's social and work relationships.
App assessment The app's use was assessed by collecting data from app stores: Google play store for Android operating systems (https://play. google. com/console/) and Apple app store for iOS systems (https://appstoreconnect. apple. com/login). Both stores provide analytics features to monitor the performances of apps. The collected data included the number of installations from October 2017 to September 2020, defined as monthly installations minus monthly uninstallations; the number of regular users, defined as people whose app was in use at least once a month; the number and type of features used from June to November 2020; and the type of requests for daily situations sought via the app from March to November 2020. Scores (0 to 5-star rating) were collected at the Google Play and Apple stores in January 2021.
Type of application. Considering legal issues, the steering committee and the French Society of Rheumatology decided that no personal information would be collected. Consequently, the app was a self-administered app for self-management and was not registered as a medical device. Patients' data were not to be directly communicated to rheumatologists or general practitioners, but patients could make screenshots of their app to communicate with their physician during consultations. The app was free of charge. Terms and conditions of use informed the patients of the app type and that information content intended to help in daily situations but did not provide medical advice or consultation; did not constitute personal advice, diagnosis, aid to diagnosis, treatment or aid to treatment; and did not constitute or Data are n (%) unless otherwise indicated.
Acknowledgments We thank all patients who participated the qualitative and quantitative studies; patients and rheumatologists who participated in the users' tests and app improvements;  Mme Nathalie Robert  from the association Spondyl(O)action for users' feedback; Mahed Iqbal (Unknowns) and  Alexandre Galatioto  ( Unknowns ) for development contribution;  Damien Carnet  ( ANDAR ) for posting the quantitative study questionnaire;  Laura Smales  for reviewing the English version; and  Frederic Liote, Hopital Lariboisière, Paris , who contributed to patient recruitment in the second qualitative study. Membership of Therapeutic patient education group of the  French Society of Rheumatology  and  Club Rhumatismes  et Inflammations are available at www. cri-net. comand https://sfr. larhumatologie.
Common signatures Although taxonomic signatures in the gut microbiome have been described for different chronic diseases, coherent and reproducible disease-specific signatures are limited  (Duvallet et al. , 2017) , except in certain diseases, e. g. , colorectal cancer, IBD, and PD. A cross-disease meta-analysis of case-control microbiome datasets has suggested that certain taxonomic alterations reflect a shared response to disease  (Duvallet et al. , 2017). Conditionspecific signatures qualified by enrichments in either opportunistic pathogens or commensals appear less consistent across studies and diseases, likely due to different confounders  (Duvallet et al. , 2017). Inter-individual strain-level variation likely plays an important role in the context of the functional diversity and redundancy observable among gut microbiota (Heintz-Buschart and Wilmes, 2018).
Functional unknowns Substantial differences exist between predicted/annotated functional potentials based on metagenomic data and actual microbial phenotypes in the gut (Heintz-Buschart and Wilmes, 2018). Although microbial proteins are well-known antigens, the immunogenic capacity of commensals and pathobionts is largely unexplored. Between 40% and 70% of microbiome-borne genes encode proteins of unknown function (depending on the prediction method) and such proteins constitute half of the proteins that are identifiable in proteomics data from fecal protein extracts  (Heintz-Buschart and Wilmes, 2018). Similarly, a substantial fraction of gut microbiome-derived small molecules (>90%) does not have any match in public databases  (De Saedeleer et al. , 2021). Furthermore, RNA transcripts are known to reflect microbial activity and affect antibody responses but microbiome-derived extracellular small and large RNA in the gut remain largely unresolved with respect to their identity and effects on the host side  (De Saedeleer et al.
Background & Summary Open-ocean pelagic ecosystems are under-represented in databases of marine biodiversity, despite holding the largest biomass of organisms on Earth 1. The open-ocean pelagic community predominantly consists of fish, crustaceans and cephalopods that inhabit mesopelagic (200-1000 m) and bathypelagic (1000 m to >4000 m) depths 2. Many pelagic species undertake diel vertical migration (DVM), moving from the depths to shallower waters at dusk and in the reverse direction at dawn, possibly constituting the largest animal migration on the planet 3. This has implications for carbon sequestration and climate regulation, as organisms actively transport organic carbon from the surface to the deep ocean  [3] [4] [5] [6]. Mesopelagic fishes are a central component of open-ocean pelagic communities dominating global vertebrate biomass with estimates of up to 10 billion tons  4, 7, 8.
Often, the methodological metadata (such as net-type or depth of trawl) are also lacking, further limiting the scope of the analyses  1, 30. Decades of data from net sampling of the pelagic environment exist for localised regions of the Southern Ocean as part of multiple national Antarctic research programs  31. Here, we take the important step of integrating and standardising the format of published and unpublished survey data on the abundance, biomass, biodiversity, and methodological metadata for mesopelagic fishes of the Southern Ocean. We collected and synthesised data from the Scotia Sea  14, [32] [33] [34]  , the Antarctic Peninsula  35, 36  , the Lazarev Sea  37, 38  , the Kerguelen Plateau  [39] [40] [41] [42] [43]  , East Antarctica  44  , Macquarie Island  45, 46  and unpublished data from the Indian Ocean from Japan's national Antarctic program into a single dataset called Myctobase (Fig.
1 ). The long-term aims of Myctobase are (1) to provide a single findable, standardised, and citable resource for multiple combined datasets; (2) to provide the baseline data upon which to measure the status and future trends of the mesopelagic fish assemblage; (3) to support further research such as investigating the patterns and biophysical drivers of diversity; and (4) to provide the broadscale spatiotemporal perspective necessary for the holistic management and conservation of the open-ocean pelagic ecosystem in the Southern Ocean. The dataset is available on GBIF, OBIS and Zenodo to support the management of the open-ocean pelagic ecosystem. Researchers wishing to contribute to this project should contact the corresponding authors. We hope that this database will continue to grow, providing a resource for ongoing monitoring of the Southern Ocean pelagic ecosystem.
Survey designs (for trawl locations) included randomised design, designated stations, and target trawls on acoustically detected aggregations of fish (see Online-only Table  1  for more information on sampling methodology). Once onboard, samples were sorted to the lowest taxonomic level possible using published guides  41, 47  and personal/institutional reference collections. Taxonomic identities were verified in home laboratories. Standard length (mm) and wet weight (g) measurements were taken onboard with a motion compensated balance or in home laboratories. Samples were preserved in either ethanol, formalin or frozen for further analyses. Detailed information on methodologies utilised for each research cruise can be found in the relevant citations listed in Online-only Table  1. Data collection and processing.
Potential data holders were identified by compiling a metadata catalogue of scientific publications and existing survey data for Southern Ocean mesopelagic fishes. Data holders were invited to contribute published and unpublished data to the Myctobase project. A standardised template for the collation of multiple datasets was created using Darwin Core terms  48  where possible. The terms used in the template alongside definitions for each term can be found in Table  1. Data were collated with R statistical software, version 4. 0. 4  49. Units of measurement were standardised across datasets. Length and weight measurements were converted into millimetres and grams, respectively. The taxonomy of each observation was verified and associated aphia IDs (globally unique and stable identifiers for each taxonomic name) were retrieved from the World Register of Marine Species using the R package, WoRMS  50  (Fig.
2 ). Abundance was calculated for each net haul by species per filtered volume of water. This was performed by dividing the species count (n) by the volume filtered (m 3 ). This was not possible for all net hauls and species as count and volume filtered information were not always recorded or technical difficulties were encountered during the research cruise. Methodology for calculating volume filtered varied between datasets. Several cruises used mechanical flow meters while others used a calculation where the nets filtering area (m 2 ) was multiplied by the distance it was towed (m). The filtering area of the MOHT net was calculated by multiplying the net mouth area by a coefficient estimated from calibration tows in calm seas. The coefficient was estimated under a calm sea condition using a net frame with a flowmeter which was vertically deployed up to 100 m wire out.
The net frame was retrieved slowly to the sea surface and a count from the flowmeter was recorded. This procedure was repeated five times and an average value was calculated to obtain the coefficient. For some datasets, volume filtered was not previously calculated, but the information needed to calculate the volume filtered had been recorded. In these instances, we retrospectively calculated the volume filtered using this information. This was shown in a separate column to the values that were obtained at the time of a cruise, this is labelled volumeFiltered2 in the database. Detailed information on the methodologies utilised to obtain volume filtered, including our retrospective calculations, for each cruise can be found in Online-only Table  2. The solar position and day/night information was added for each observation based on the date, time and the start latitude and longitude of each net haul using the R package, maptools  51  (Fig.
Data Records The dataset is comprised of three comma-separated files which are freely available at Zenodo  54. Filenames adhere as closely as possible to the naming convention set out by the Darwin Core Standard 48. The first file (event. csv) describes the survey methodology. Each row has its own unique event ID, which consists of the institute, cruise, event number (as recorded in the voyage logbook) and the net number (institute_cruise_event_net). An event ID represents the sampling event or net haul and contains the details of the event including date, time, position (latitude, longitude, and depth), sampling protocol, net type, net mesh size, tow speed, volume filtered and haul type (station, routine, target, test, surface or failed). The second file (groupOccurrence. csv) contains the catch data linked to the survey methodology by an event ID.
Each row has its own unique occurrence ID, which is the event ID and aphia ID (retrieved from WoRMS) combined (eventID_aphiaID). An occurrence ID contains taxonomic information (eg. phylum, class, order, and family), the number of individuals (n) and estimated abundance (n_m 3 ) for the associated sampling event. The final file (individualOccurrence. csv) contains measurements of individuals. Each row contains the event and occurrence ID, which links each measurement to the first and second file, and a 'catalogNumber' , linking the data to the original dataset for traceability. Rows also contain taxonomic information, standard length (mm), weight (g), life stage and reproductive maturity of the individual (following definitions in  55  ) and sex, where available. Additional notes on the preservation method and whether the specimen was measured before or after preservation are included.
The presence of NA values in Myctobase are indicative of missing data. See associated metadata record for definitions and units for each variable (Supplementary Table  1 ). Citations for all datasets held in Myctobase can be found in Online-only Table  1. The highest concentration of data points is within the Atlantic Sector (45% of net hauls) spanning from the Antarctic continent to the Polar Front at 50° S (Fig. 1 ). The East and West Pacific Sector stands as a major gap in the spatial coverage of Myctobase (21% and 1% of net hauls in the East and West, respectively).
Spatial and temporal coverage. Vertically, data extend from the surface down to a maximum depth of 2000 m. More than half of the trawls took place in the epipelagic layer at 0-200 m (n = 3713). Data from the mesopelagic (200-1000 m) and bathypelagic (>1000 m) layers were also recorded from stratified oblique trawls. Data span from 1991-2016 and 2019, with no data for 2017 and 2018 (Online-only Table  1 ). The year 2006 contains the highest number of net hauls (11%). Cumulatively, the datasets within Myctobase cover data for every month (NB. not every month of every year), with 76% of net hauls occurring in the summer months (November to March).
Records of occurrence. The four most abundant fish families in Myctobase are Paralepididae, Nototheniidae, Myctophidae and Bathylagidae. Myctophidae make up the highest number of records in Myctobase for both the Atlantic and Indian sectors (Fig. 3 ). In the Indian Ocean sector, the highest number of records for Myctophidae are in the Subantarctic zone. Conversely, in the Atlantic Ocean sector the highest number of records for Myctophidae are in the Antarctic zone. There are a similar number of records for Bathylagidae, Nototheniidae and Paralepididae in the Indian sector for both the Antarctic and Subantarctic zones. In the Atlantic Ocean sector, the highest number of records for Bathylagidae, Nototheniidae and Paralepididae are in the Antarctic zone. Fig. 3  The number of records of occurrence for the four most abundant fish families in the groupOccurrence data record of Myctobase.
Technical Validation The final dataset was subject to quality control and validation processes. Rather than removing ambiguous or incomplete records altogether, two extra columns were added to the event information (event. csv). The first column labelled validation is an index used to indicate whether the record passed quality control measures (0 = fail, 1 = pass). The second column labelled validationDescription details the reasons for a failure. Indexing the data in this way prevents the loss of potentially valuable data. For example, some events are missing latitude and longitude information, however a more general sampling location is often available and may be enough for regional analyses (Fig. 2 ). The R package obistools  56  was used to validate sampling locations and dates of events.
Sampling locations on land or at depth values higher than the bathymetry raster used in obistools did not pass the quality control step and thus were given a '0' in the validation column. Further, records missing latitude, longitude and date/time data were similarly identified. Records where technical difficulties were recorded, such as net failing to open or close were also allocated a '0' in the validation column (Fig. 2 ). Abundance was standardised to number of individuals per m 3 (n_m  3  ) for all datasets. In some instances, abundance could not be calculated due to missing count or volume filtered data. Additionally, abundances were not calculated for US AMLR Program as this data were considered bycatch and not the sole focus of the sampling program. Subsequently, this data is important for documenting the species observed and overall distribution in the region of the Antarctic Peninsula.
Important information regarding the use of abundance values is available in 'Usage Notes'. Although the purpose of the dataset is to document the occurrences and relevant metadata of mesopelagic fish species, we also retained records of cephalopods. Occurrence records of cephalopods alongside those of fish were included in some of the datasets contributed to Myctobase. Cephalopods are similarly important species of the open-ocean pelagic community and are a key group supporting the flow of energy from primary producers to higher order predators in Southern Ocean food webs  [57] [58] [59]. Data on squid are limited due to their low catchability with scientific nets  59. As such, we retained records to maximise availability of valuable data. The data are structured to enable users to easily filter out cephalopod occurrences if they are not of interest.