dataesr/scientific-paragraphs-categorization huggingface dataset:
Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/essd-13-4191-2021-supplement. Author contributions. TD and CG designed the study. TD, SD, NE and CG produced emission data. TD, YL, XS and TW collected the datasets. TD, IB, ST, BG and TS analyzed the developed adjustment factors (AFs). BG, AD, FL, ST, IB, YL, TW and TS tested the AFs in atmospheric models. TD, CG and GB wrote the original draft. All authors provided critical feedback and helped shape the research, analysis and manuscript.
Acknowledgements. We acknowledge the AQ-WATCH (Air Quality -Worldwide Analysis and Forecasting of Atmospheric Composition for Health) program, the support of the ESA (European Space Agency) project and the SEEDS (South-Eastern European Data Services) European project. The CAMS-GLOB-ANT dataset has been developed with the support of CAMS (Copernicus Atmosphere Monitoring Service), operated by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission as part of the Copernicus program. The EC-CAD (Emissions of atmospheric Compounds and Compilation of Ancillary Data) database is supported by the French AERIS (Data.
(2005)  to describe combinations of mathematical and IT competencies needed in technical workplaces. The engineering studies should contribute to the development of these competencies, but how?  Van der Wal et al. (2019)  observe that a project-based course using a rich technological environment associated with relevant teaching practices can contribute to this development. The students are invited to present and comment their use of software (e. g. calculation done with Excel), which is collectively discussed. The students develop a reflective use of the software, not limited to calculations, but also used for example to control the correctness of a solution. Indeed, there are a number of studies on how to integrate particular software into university mathematics education (e. g. CAS,  Marshall et al.
Acknowledgements The concept of the publication and its final layout was created thanks to the involvement of J. D-L, R. R and T. B. Contribution in the preparation of chapters and tables, in writing publications: M. L. B. ; P. B. ; J. C-R. ; Y. v H. ; J. K. ; J-F. L. ; M. R. L. ; B. O. A. ; B. P. ; J. R. ; M. P. ; P. R. ; J. R. ; B-W. R. ; Y. S. ; P. T. ; R. T. ; A. W. ; R. R. ;  J. D-L. All  authors read and agreed on the final version. the transcription factors NF-κB and  NRF2 ' by  Drs. Yoav Sharoni  and  Jean-Francois Landrier. Part about Carotenoid metabolites and retinoids as activators of nuclear hormone receptors' by  Ralph Rühl  and  Joanna Dulińska-Litewka. Sub-chapter 'Bioavailability of carotenoids and alternative mechanisms of carotenoids ' by  Torsten Bohn. Sub -chapter 'Disease-related mechanisms potentially modulated via carotenoid-gut microbiota interaction' by  Marisa Porrini ,  Patrizia Riso  and  Torsten Bohn.
| Measurement of the supercooling point To determine the SCP, we placed the spiders in centrifuge tubes, which were submerged in a cryostat bath (Polystat CC3, Huber Kältemaschinenbau AG, Germany) filled with heat transfer fluid (Thermofluid SilOil, Huber, Germany). The temperature of the bath was slowly reduced at a rate of 0. 5°C/min to reach a target temperature of -30°C. To monitor the temperature of the spiders, we placed a K-type thermocouple in direct contact with the spider opisthosoma, secured with Parafilm® and connected to a Testo 175T3 temperature data logger (Testo SE& Co. , Germany). We recorded the temperature every ten seconds. The SCP was defined as the temperature at the onset of the freezing exotherm produced by the latent heat (see Figure  2  for representative exotherms), and we considered spiders dead if they did not move in the 24 hr after SCP test.
| Measurement of spider body size We measured the spiders' body size after the SCP experiment to avoid injuring the spiders and biasing the results. We took a picture of the spider' back together with a measuring tape for measuring the body size later in the ImageJ software  (Schneider et al. , 2012). We measured the highest length and largest width of the carapace (prosoma) which are commonly used as proxy for whole body size, fitness, and metabolic rate in spiders  (Jakob et al. , 1996; Penell et al. , 2018).
Based on a varied dataset collected using many different nets,  Brun et al. (2016)  found that body size declined weakly at warmer temperatures, but there was little or no effect of temperature between 10 and 30°C. However, the temperature effect was dwarfed by the effect of food, which surprisingly led to a decline in body size with increasing food availability. This contrasts with many other studies that have shown increasing body size with food concentration in copepods  (Vidal 1980 ) and other ectotherms  (Pafilis et al. 2009 , Huston and Wolverton 2011 , Andriuzzi and Wall 2018). In a recent study,  Evans et al. (2020)  found marine copepods in the North Atlantic conform to Bergmann's Rule. Although they found that temperature (2-27°C) had a more profound relationship with body size than described in  Brun et al.
These tests are facilitated at a nearglobal scale by virtue of the continuous plankton recorder (CPR) survey dataset, the largest (~100 000 samples), most consistent (collected using the same device), global dataset on marine copepods  (Richardson et al. 2006 , Batten et al. 2019). We used a spatial comparative analysis to identify statistical relationships over environmental gradients across space  (Brown 1995, Gaston and Blackburn 2008).
Sample collection We chose the CPR dataset, assembled by the Global Alliance of CPR Surveys  (GACS 2011 , Batten et al. 2019) , because it provides the largest, consistent, most spatially-extensive, species-resolved plankton dataset  (Richardson et al. 2006). Data were sourced from four surveys: the North Atlantic CPR Survey, the Scientific Committee on Antarctic Research (SCAR) Southern Ocean CPR Survey (Hosie 2020), the North Pacific CPR Survey and the Australian CPR Survey (Fig. 1 ). Although CPR surveys have better coverage in polar and temperate regions than in subtropical and tropical regions, samples have been collected in waters as warm as 30°C. All surveys use similar methods to collect and count copepods  (Reid et al. 2003 , Richardson et al. 2006  for more details). Specifically, all CPRs use the same mesh size (270 µm), the same mesh material (silk), the same size mouth opening (1.
Copepod data Copepods are identified to species whenever possible, and shared training and staff exchanges amongst surveys ensure comparable data. Names of all copepod taxa were updated using the World Register of Marine Species (WoRMS) (<www. marinespecies. org/>). Data were available for 388 taxa and from 97 830 samples. To test Bergmann's rule, we calculated the mean length of copepods in each sample and related this to environmental drivers. Maximum and minimum lengths of different copepod taxa were obtained from the Marine Planktonic Copepod Database from the Observatoire Océanologique de Banyuls-sur-Mer (<https://copepodes. obs-banyuls. fr/en/ index. php>; Razouls et al. 2020 ) and from  Richardson et al. (2006). Because juveniles are more difficult to identify to species and reliably assign a size than adults, they were not included in the estimate of mean copepod size for each sample, although adults tend to be more common in CPR samples  (Richardson et al.
Environmental data We used sea surface temperature (SST) as an estimate of ocean temperature for the near-surface CPR samples, and chlorophyll-a concentration (Chl-a) as a proxy for food availability to copepods. Chl-a is correlated with copepod growth and fecundity in herbivorous and omnivorous species  (Richardson and Verheye 1998 , 1999 , Hirst and Bunker 2003 , Bunker and Hirst 2004) , and more Chl-a leads to more of these grazers and thus more carnivorous zooplankton  (Richardson and Schoeman 2004). As an index of food availability to copepods on each sample, we considered using the phytoplankton colour index, which is a visual assessment of the greenness of the silk in four levels (no colour, very pale green, pale green and green)  (Richardson et al. 2006 ). This index is useful for analysis when averaged in time and space  (Raitsos et al.
2005 (Raitsos et al. , 2014)) ; however Chl-a from satellite is more accurate for individual samples  (Richardson et al. 2006). Further, temperature is rarely measured on CPR tows, so we used remotely-sensed Chl-a and SST data to ensure measurements were consistent among samples. We matched the location and time of collection of CPR samples with estimates of SST and Chl-a averaged over eight days prior to sampling to limit loss of data due to cloud cover and to represent feeding conditions over the recent past. For SST, we used daily Group for High Resolution SST data, a cloudfree global product based on satellite, buoy and ship data, and interpolated at 0. 2° × 0. 2° resolution (<https://doi. org/10. 5067/GHCMC-4FM02>). For surface Chl-a from September 1997 until the end of 2016, we used daily data from the European Space Agency Ocean Colour Climate Change Initiative data (<https://doi.
org/10. 5285/9c334fbe6 d424a708cf3c4cf0c6a53f5>). Because this is a merged product from MERIS, Aqua-MODIS, SeaWiFS and VIIRS satellites, these data provide better coverage than products from a single satellite, and are more accurate globally  (Mélin et al. 2017). For 2017 and 2018, we combined data from Aqua-MODIS (<https://doi. org/10. 5067/AQUA/MODIS/L3M/ CHL/2018>) and VIIRS (<https://doi. org/10. 5067/NPP/ VIIRS/L3M/CHL/2018>), the only available satellites. Chl-a data were retrieved at 4-km resolution and aggregated to 0. 2° × 0. 2° resolution to limit loss of data due to cloud cover and match the resolution of SST data. As there are no robust satellite Chl-a data before 1997, all analyses used CPR samples collected after this time. To investigate whether oxygen is a driver of Bergmann's rule  (Forster et al.
Thus, the negative relationship between copepod length and Chl-a could be the result of a combination of copepod behaviour and how samples were collected. This would be an artefact of the study, and this hypothesis requires further testing. In our dataset this explanation is probably more likely than copepods being larger in response to having to forage further (and deeper) to find food in areas where food is scarce, as suggested in other studies  (Belovsky 1997 , Brown et al. 2017 ). An alternative explanation is that copepods might grow larger in response to seasonality of their food supply  (Brun et al. 2016). For example, copepods grow larger in systems with short seasonal pulses of food (e. g. Chla) by accumulating lipids for survival when food is limited  (Kattner et al. 2007 ). Thus, being larger and having greater reserves could allow better survival during periods without food.
Nevertheless, we believe we were able to estimate the relative contributions of the drivers of Bergmann's rule because of our large number of samples, and through the use of random effects to adjust for spatial and temporal variations. Using BIC allowed us to select robust models that explain the data well without overfitting  (Schwarz 1978 , Aho et al. 2014) , which is important when analysing very large datasets. We are more confident in the relationship at temperatures typical of temperate regions (5-20°C), and at lower Chl-a, where we have most data. Unfortunately, we have a paucity of data in tropical regions, although these are the largest areas in the ocean.
2019) , end-toend ecosystem models  (Griffith et al. 2011 (Griffith et al. , 2012) )  and statistical models  (Grieve et al. 2017). There is considerable opportunity to include well-tested macroecological principles such as Bergmann's rule in future modelling efforts focused on climate change. Our analysis shows that these principles could substantially influence future projections. This study also highlights the utility of using large global datasets for testing macroecological theory. Datasets such as the CPR, which have been collected consistently for decades have predominantly been used to understand ecosystem dynamics or describe global change  (Edwards et al. 2010 ). There is great potential for comparative analyses with similar consistent, global datasets. Further, with the advent and increasing accessibility of powerful statistical techniques such as GLMMs -that make it possible to test multiple predictors whilst adjusting for spatial and temporal autocorrelation -there is increasing opportunity for providing robust and nuanced tests of macroecological relationships through spatial comparative analyses  (Bolker et al.
North  Pacific Marine Science Organisation (PICES)  and comprising the  North Pacific Research Board  (NPRB),  Exxon Valdez Oil Spill Trustee Council (EVOS TC) ,  Canadian Department of Fisheries and Oceans (DFO)  and the  Marine Biological Association, UK. AusCPR data were sourced from the  Integrated Marine Observing System (IMOS). IMOS is a national collaborative research infrastructure, supported by the  Australian Government. SO-CPR data were sourced from the  Scientific Committee on Antarctic Research (SCAR) sponsored Southern Ocean CPR (SO-CPR)  Survey Database, hosted by the  Australian Antarctic Data Centre (AADC). The AADC is part of the  Australian Antarctic Division (AAD, a division of the Department of Agriculture, Water and the Environment (DAWE)  of the  Australian Government).
The  SO-CPR Survey  and database are also funded, supported and populated by the  Australian Government  through the  DAWE-AAD  approved  AAS  project  4107  and the  Integrated Marine Observing System (IMOS)  funded by the  Australian Government National Collaborative Research Infrastructure Strategy  and the  Super Science Initiative , the  Japanese National Institute of Polar Research (NIPR) , the  New Zealand National Institute of Water and Atmospheric Research (NIWA) , the  German Alfred Wegener Institute (AWI) , the  United States of America -Antarctic Marine Living Resources programme (NOAA US-AMLR) , the  Russian Arctic and Antarctic Research Institute (AARI) , the  Brazilian Programa Antártico Brasileiro (PROANTAR) , the  Chilean Instituto Antártico Chileno (INACH) , the  South African Department of Environment, Forestry and Fisheries (DEFF) , the  French Institut polaire francais Paul-Émile Victor (IPEV)  and  Université Pierre et Marie Curie (UPMC).
Hernia-club registry This study was executed conducted within the French Hernia-Club registry, which is a collaborative, prospective, anonymized online database of all surgical procedures for abdominal wall hernias. The French Hernia-Club registry complies to the General Data Protection Regulation and is approved by the French 'Commission Nationale de l'Informatique et des Libertes' (CNIL registration number: 1993959v0). Because this study is registry-based and guarantees completely anonymized data, additional participant approval and consent was not required according to the French and Dutch national ethical standards. Surgeons specialized in abdominal wall surgery performed all operations. Each dedicated hernia surgeon must perform at least 100 inguinal and 50 ventral hernia repairs annually.
Furthermore, each surgeon must accept and sign the Charter of Quality, which states that: 'all input must be registered in a consecutive, unselected and exhaustive manner and in real time. ' A total of 191 parameters were collected by the operating surgeon and the blinded, independent, clinical research associates, using online forms. Parameters comprise data from screening, pre-, peri-, and postoperative periods. Participants consent to random peer review of original medical charts to ensure high-quality data. The medical records were checked in the case of any discrepancies. All collected parameters in this database were fully compatible with the EuraHS international online platform and the European Hernia Society (EHS) classification of abdominal wall hernias  [10, 11].
Data extraction Relevant baseline, surgical, and functional outcome variables were extracted from the Hernia-Club database. Extracted baseline variables comprised: age, sex, body mass index (BMI), American Society of Anesthesiology (ASA) classification, diabetes mellitus, number of previous hernia re-recurrences, smoking status, IH location (medial vs. lateral), EHS width classification, mesh location (IPOM, sublay, onlay, no mesh), emergency surgery, synchronous repair of multiple defects, wound classification (clean, clean-contaminated, contaminated, dirty), and follow-up time. Surgical outcomes comprised: IH re-recurrence, IH repair surgery, radiological re-recurrence only, postoperative complications (surgical and medical) and Clavien-Dindo classification grade. Functional outcomes were assessed with a follow-up survey after approximately one-year post-surgery.
e. , not related to the outcomes or study groups). Multiple imputations were performed to allow the use of all available data. The following variables were included in the imputation model: age, sex, BMI, ASA-class, hernia location, EHS width classification, mesh location, emergency surgery, synchronous repair of multiple defects, diabetes mellitus, wound class, smoking status, previous hernia re-recurrence, IH re-recurrence at 1-year follow-up (predictor only), and any complications (predictor only). Continuous variables were imputed according to the predictive mean matching method, discrete variables with the use of logistic regression. In total, ten imputations for each missing value were performed. Propensity scores were calculated for each imputed dataset. The following variables were included in the propensity score model: age, sex, BMI, ASA classification, hernia location, EHS width classification, mesh location, emergency surgery, synchronous repair of multiple defects, diabetes mellitus, wound class, and smoking status.
The propensity scores of the imputed datasets were pooled. Subsequently, cases (patients operated after two or more previous re-recurrences) were matched 1:3 to control patients. Cases were matched according to the nearest neighbor method, matching from the highest to lowest propensity scores. Cases and controls with propensity scores outside the region of common support were discarded. For all variables included in the propensity model, the performance of the matching model was assessed visually by plotting the mean of each covariate against the propensity score (Online Resource 2, 3, and 4). Additionally, the balance was assessed with the use of the chi-square, Student-T test or Mann-Whitney U test as appropriate. Finally, the outcomes were assessed with simple univariable analysis in the raw and matched study sample.
Patient characteristics Seventy-six patients operated after two or more IH re-recurrences were available in the Hernia-Club registry dataset, and 763 control patients who underwent primary IH repair with similar follow-up data were available (Table  1 ). In the raw, unmatched sample significant differences were present between the control and study groups. BMI was higher among the re-recurrence group (control: 27. 8 kg/ m^2 vs. C 2 re-recurrences: 31 kg/m^2, p \ 0. 001). Slightly more patients were operated for a laterally (around linea semilunaris) located IH in the control group (control: n = 129, 18% vs. C 2 re-recurrences n = 6, 8%, p = 0. 010) and slightly more for a combined lateral and medial hernia among the re-recurrence group (control: n = 45, 6% vs. C 2 re-recurrences: n = 10, 13%, p = 0.
Acknowledgements We would like to thank all the members of the  Hernia-Club  for collecting patient data. Members of the Hernia-Club:  Abet E  ( Centre Hospitalier, La Roche sur Yon, France ),  Ain J-F  ( Polyclinique de Val de Saone, Macon, France), Arnalsteen L (Hopital PriveLa Louviere, Lille, France), Baraket O (CHU, Bizerte, Tunisia), Beck M (Clinique Ambroise Pare, Thionville, France), Bellouard A (Clinique l'Archette, Olivet, France), Benizri E (CHU, Nice, France ),  Berney C (Bankstown-Lidcombe Hospital, Sydney ,.
Follow-up schedule Once enrolled, pregnant women are followed both at home and at the health facility. Home visits are conducted every three months until 32 weeks of gestation, then every two weeks. After 36 weeks, home visits alternate with phone visits every week until birth. Postpartum women are followed from birth to 42 days post partum with scheduled home visits on days 0 (birth), 6, 28 and 42 after birth. Newborns and children under-2 years are followed on days 0 (birth), 6, 28 and 42 and months 6, 12 and 24. Participants who present to health facilities for antenatal care, postnatal visits or sick visits (outpatient or admission) also have data collected by study data collectors at the health facility. Participants are censored from the study with the following events: out-migration from the catchment area, death, lost to follow-up and at the end of the follow-up period (figure  1 ).
In addition, all participants (including pregnant women, postpartum women and children under-2) are concurrently followed on a quarterly basis by the underlying HDSS, which includes basic health questionnaires and an under-2 year old questionnaire on child morbidity. Pregnant women enrolled into the cohort receive an ultrasound for gestational age dating at their earliest antenatal care (ANC) visit. Information on the pregnancy (birth spacing, antenatal care visits) and pregnancyrelated symptoms, including history of vaginal bleeding, dysuria, headache, fever, abdominal pain and fetal movement are collected. Maternal weight and observations of clinical signs such as pallor, jaundice and oedema are collected. For newborns enrolled in the cohort, data on labour and delivery (location of delivery, delivery method, duration of labour, complications of birth, birth weight) and immediate newborn care (breast feeding, cord-care, bathing, skin emollients) are collected as soon as possible with a target of within 24 hours of birth.
Data collection To ascertain the cause of death, verbal autopsies (VAs) are conducted to obtain information on the symptoms, signs and other relevant events during the illness leading to death. Three VA questionnaires (for deaths 0-28 completed days of life; deaths of children between four weeks and 11 years of age; and deaths of persons aged 12 years and above which includes pregnancy-related questions for women of reproductive age) adopted from the standard 2016 WHO VA questionnaires are used. 16 erbal autopies were developed to provide information on causes of death in communities where there is limited access to healthcare and medical certification of causes of death. In such situations, the main source of information about the event is from caregivers of the deceased, most often family members.
Electronic data collection system and data management We use the Birhan electronic data collection system, built from Open Data Kit (ODK),  17  for longitudinal and relational data collection. ODK is a free, open-source application used to facilitate mobile data capture. ODK can be coded using SQL to facilitate data collection, transfer and storage, as well as the development of electronic questionnaires used for data collection. Data can be collected on a mobile device offline. Data are uploaded daily using mobile data to a central database. Data quality checks are built into the data entry tools and data system. Data are hosted on an encrypted central database on the cloud and are deidentified. The data system is developed and maintained by two data system developers. Data are managed by a team of data scientists and data managers in Stata (V.