dataesr/scientific-paragraphs-categorization huggingface dataset:
D. B. ). C. R. was supported by an  EU   Horizon 2020 Marie Sk1odowska-Curie Action fellowship  ( H2020-MSCA-IF-2014 #661527 ). K. M. G. and K. K. are jointly funded by the  Indian Council of Medical Research  (file no. 5/7/1508/2016  to K. M. G. ) and the  Federal Ministry of Education and Research  ( 01DQ17003  to K. K. ). Sequencing and analysis for individual 10 were provided by the  Broad Institute of MIT and Harvard Center for Mendelian Genomics (Broad CMG ) and was funded by the  National Human Genome Research Institute , the  National Eye Institute , and the  National Heart, Lung, and Blood Institute  grant  UM1 HG008900  and in part by  National Human Genome Research Institute  grant  R01 HG009141. Funding for the UDP-Vic was provided by philanthropic donation and the  Murdoch Children's Research Institute.
As a result, the application of marine strategies in the first cycle occurred principally at a national level, making inter-comparisons of datasets or approaches very difficult  [8, 9]  and creating a significant barrier to effective implementation (e. g. [6, 9] ). This apparent resistance to collaboration amongst MS may itself be driven by a lack of time, funds and expertise  [7, 9]. In some cases, assessments need to be to be made on elements for which the evidence is inconclusive, and for which data collection to improve the knowledge base is prohibitively expensive. To address this and in recognition of the significant financial burden in general for MSs of monitoring and measuring environmental status and trends over vast areas, Commission Decision 2017/848  [20]  made provisions for risk-based approaches to assessment of criteria and set out a clearer, more easily targeted elaboration of the GES descriptors.
State elements SCANS I, II and III reports for the Celtic Seas and Bay of Biscay  [26] , the ObSERVE report for part of the Celtic Seas  [55]  and the MISTIC Seas report for Macaronesia  [59]. Units individuals/km 2   Cetacean data for the Bay of Biscay were obtained thanks to the IEO PELACUS campaign. In this campaign density models for different species of marine mammals were acquired for the period 2007-2017.
Risk Evaluation Likelihood and Consequence of different species graphed to enable relative risk to be established prioritisation of specific management units Likelihood and Consequence overlaid geographically for a single species to identify areas of highest risk II and III and ObSERVE reports for the Celtic Seas and Bay of Biscay  [26, 55]  and the MISTIC Seas report and for Macaronesia  [59]. These data represent the most up-to-date density information for cetacean species within the study area, and most were collected during 2015 and 2016. In total the survey reports gathered sufficient data to estimate densities for 16 species in the study region. While some species cross geographic boundaries within the study area, others have more discrete distributions and consideration of GES in different regions would feature varying combinations of these species.
g. [24] ) are not freely available. An alternative approach is the use of proxies, where the spatial distribution of an activity is inferred to relate to the spatial distributions of their resulting pressures. In the absence of more reliable data sources at the appropriate spatial scales, ship traffic data therefore offer a lower cost (and lower confidence) alternative to assessing the approximate distribution of pressure. At the subregional scale, the European Marine Observation and Data Network, EMODnet (www. emodnet-humanactivities. eu) has developed a processed shipping dataset using AIS data for European waters, currently covering 2017-2019. At present, this represents the only freely available dataset that can provide shipping information at the appropriate scale to inform studies of continuous underwater noise.
The dataset expresses levels of shipping activity as ship hours per square kilometre per month with a resolution of 1 km. full details of the data processing used to derive the dataset can be found in Falco et al. [22]  This dataset was used in this study as a proxy for the distribution of underwater noise for the subregional scale analysis. The approach to risk assessment at a local scale in the Bay of Biscay was the same as that used at a subregional scale with continuous noise emitted by ship traffic also being the focus. However, in this case a modelled pressure map (as opposed to a proxy layer) reflecting ship noise was created. An annual AIS ship traffic dataset for the area was obtained, which contained information on speed over ground, length and tonnage of each ship. Ships were treated as acoustic sources by applying a Randi Model  [2]  for 10 kHz frequency.
In addition, seasonal variations of speed of sound in the water column were considered using Argo data (www. emodnet. eu) and the MacKenzie equation  [41]  was used to combine sound velocity with temperature, salinity and depth. Using all of these information sources, a noise model of the area was created and later validated with data from a passive acoustic monitoring campaign. Recordings were performed using SAMARUC passive acoustic monitoring device  [38, 46, 47]  in the Bay of Biscay (N43 • 36,682; W02 • 39,419) from June 20th, 2019 to September 20th, 2019, during which 25 days of acoustic data were collected. Ambient noise indicators of 1/3 octave at different frequencies (63 Hz, 125 Hz, 2 kHz and 5 kHz) were obtained  [38]  to assess the accuracy of the model. The type of exposure considered was based on the Communication Distance Reduction for this species at 1 kHz, 5 kHz and 10 kHz frequencies.
Step 3. Risk analysis 3. 3. 1. Risk analysissubregional scale The first step of the risk analysis was a preliminary analysis, the aim of which is to exclude areas or situations where risk is very low or nonexistent (see  ISO [31] ), which can then be eliminated from further examination. The EMODnet shipping dataset for 2017 clipped to the study area totalled 3,064,473 square km, representing the EEZs of Ireland, France, Spain and Portugal. As regards shipping density, 145,485 square km of the total area were found to have greater than one ship hour per month. Fig. 5 (a) shows that the majority of the grid cells with greater than 1 ship hour were located close to the coast and analysis showed that 74% were within 50 km of the coast. Although Macaronesia was the largest region by area, it had the smallest area (7910 square km) with a shipping density of greater than one ship hour (see Fig.
Exposure/likelihood To allow the cetacean density and shipping density to be examined together, the average density per cetacean species (those listed in Table  2 ) within each survey box was obtained from the relevant cetacean data source  [26, 55, 59 ] (See Fig. 2 ). The average shipping density (number of ship hours per month) was also obtained for survey boxes using the Zonal Statistics function in ArcGIS. To identify areas where cetacean densities and shipping densities were high, the average cetacean densities were then overlain with the average shipping densities for each survey box (see Fig. 6 ). A likelihood score per cetacean species per survey box was then calculated. This was done by obtaining a proportional density (relative to the whole study area) per species per survey box and multiplying this by the shipping density for this survey box.
Consequence/sensitivity Having established relative levels of exposure of Cetaceans to shipping activity, the aim of the consequence analysis was to assess the risk of adverse consequences from exposure of the cetacean populations (identified in step 1). Data describing cetacean's sensitivity to ship noise remains inconclusive (see Erbe et al. [16]  and Southall et al. [62] ) therefore the consequence analysis was conducted by performing a qualitative expert-judgement-based scoring process, designed to identify the species likely to be most affected by exposure (Box 1). Consequence analysis was applied to eight species, based on the inputs of a total of three experts. Where more than one expert completed the process, the mean of their scores was used. These species were chosen to illustrate how the process could be applied to a range of species with differing ecologies and geographical distributions.
The EMODnet shipping density data are centralised and free of charge, with data available for a variety of vessel types at a monthly scale for a number of years. McKenna et al. [43]  analysed the complexity of noise generated by different types of vessels, however at present the vessel categories used in EMODnet cannot be associated with noise profiles. In future, harmonising the ship types identified within this dataset with the categories of ships used to generate noise models could enable more effective and centralised assessment of noise pressures. Similarly joined-up approaches to the collection, harmonisation, and archiving of cetacean density could enable more effective regional assessment. While repeat surveys would go a long way towards understanding the complexity of Cetacean population distributions, the current data represent only a snapshot in time.
Conclusion The ISO standard methodology provides an ideal framework within which to nest regional and subregional comparisons not just for directives like the MSFD, but for marine ecosystems more broadly. It enables regional cooperation even where data availability is patchy or nonexistent and it can be used to identify knowledge gaps and steer capacity-building efforts in appropriate directions. For Descriptor 11, further work and development is needed to make best use of the benefits of the risk-based approach. A European-scale map of maritime transport (including offshore shipping) that can be accurately related to noise is required in order to improve the accuracy and utility of risk-based approach, and to this end alignment of the shipping categories in the EMODnet datasets with those used for the modelling of underwater noise might be a useful first step.
A harmonised database of cetacean density distribution is also needed; one that is spatially standardised, freely available, accompanied by appropriate metadata and crucially, that allows for temporal considerations to be taken into account. The latter is particularly important for migrating cetacean species such as the Fin Whale, for which risk levels will vary at different periods throughout the year. While work to understand the impact of noise on cetacean (and other) species will no doubt continue, this will take considerable time and resources, and in the meantime an expert judgement-led sensitivity process provides a realistic and effective solution.
Acknowledgements This work was supported by the  Directorate-General Environment of the European Commission  (Grant no. 110661/2018/794607/SUB/ENV. C2 ). We are very grateful to all partners of the  RAGES (Risk Based Approaches to Good Environmental Status) consortium  for discussions during the development of this work. We are grateful for the contributions of  Paulus Jacobus Wensveen  and  Rocio Prieto Gonzalez  to the cetacean sensitivity process.
Methods Our study included three stages: (i) preliminary analysis with secondary literature, databases and exploratory interviews, (ii) timeline construction from interviews and workshops, and (iii) timeline interpretation. The first stage included two steps in order to obtain an overview of the SES dynamics with the main social and ecological trends and events since 1900. First, we analysed the history of the valley from existing grey and scientific literature and databases on demographic and social data  (INSEE 2016) , agriculture (Agreste), climate (temperature and precipitations-Météo-France), risk (ONF-BD-RTM) and municipality archives (for details on data used see appendix 1). Second, the interviews of thirteen inhabitants were done in June 2018 (farmers, tourism professionals, elected officials, association representatives and multisector people) who had lived at the site for at least 20 years and knew its history well (details in appendix 2).
These semidirected interviews elicited descriptions of the SES and its subsystems, the past social, political, economic, ecological changes and their impacts. These information and data fed the timeline construction, by the identification of relevant themes. In the second stage, we applied a timeline analysis, which is a deductive qualitative method for the systemic and temporal analyses of the past trajectory of a SES  (Bergeret et al. 2015; Spiegelberger et al. 2018). The chronosystemic timeline construction follows four steps. First, we defined the problem addressed with the associated spatial and temporal scales, which here is the past adaptation of the SES of Pays de la Meije from 1900 to 2015. Second, we identified main themes where change has occurred over this period. Based on our preliminary analysis, we selected eight themes: external policies, local governance, social dynamics, agriculture, tourism, landscape dynamics, ecosystem management and climate.
The elements of the timeline identified by the participants were, as far as possible, compared with information obtained from the literature, public information and databases available and were integrated into the narrative. The chronosystemic timeline obtained was presented to local stakeholders during a feedback event, allowing us to validate our construction, enrich and correct it where necessary. In the third stage, for the four periods of change, we investigated the processes that led to adaptation by identifying (1) the drivers that induced changes in the SES, (2) the impacts of the drivers on capitals, NCP or quality of life and the resulting vulnerability or opportunities and (3) the adaptive responses of the SES in terms of reconfiguration of NCP co-production (Fig. 1a ). Once these elements and their causal relationships were identified, we investigated their implication for the three steps of NCP co-production within the IPBES framework  (Diaz et al.
Acknowledgments This paper is a contribution from the  Transformative Adaptation Research Alliance  (TARA, https//research. csiro. au/tara/),an international network of researchers and practitioners dedicated to the development and implementation of novel approaches to transformative adaptation to global change. We warmly thank inhabitants of  Villar d'Arène  and  La Grave  for participation in workshops and interviews. We warmly thank the young researchers' group from  CDP Trajectories  for their investigations on the historical data and the trajectory of the  Pays de la Meije. We also thank  Jardin du Lautaret  for hosting the research team ( UMS 3370 Univ. Grenoble Alpes-CNRS ) a member of the  Zone Atelier Alpes and eLTER network and of AnaEE-France  ( ANR-11-INBS-0001   AnaEE-Services, Investissements d'Avenir frame ).
Funding This research was funded through  French Agence Nationale pour la Recherche projects MtnPaths  ( ANR-16-CE93-0008-01 ),  Investissements d'Avenir CDP Trajectories  ( ANR-15-IDEX-02 ) and  TRASSE  ( ANR-17-CE32-0012-01 ) and the  CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA)  with financial support from the  CGIAR Fund.
The trial was registered on ClinicalTrials. gov (NCT02784444). Meta-regressions of NASH clinical trials were performed to examine associations between biomarker and histological changes within treatment groups, and to additionally examine associations between treatment effects on biomarkers and on liver histology. Trials that met the following criteria were included: • Randomized controlled clinical trial • Paired biopsies performed at baseline and follow-up, scored using the NASH CRN system.
EudraCT and ClinicalTrials. gov were searched for the keyword "nonalcoholic steatohepatitis" with results further filtered for phase 2 or 3, interventional, terminated, or completed trials. These three sources returned 265 (PubMed), 153 (ClinicalTrials. gov), and 56 (EudraCT) results, which were each individually reviewed for inclusion in the analysis. Data extracted by one analyst from each source that included means and SDs at baseline, end of study, and changes from baseline to end of study for continuous variables were verified and corrected by a second analyst. Where SDs were not available, any provided estimate of variation was used to derive the SD where possible; a plot-digitizer program was used where data were presented in figures only. Data for dichotomous endpoints were extracted as the number of subjects with the event and corresponding sample size.
Statistical analysis Multivariable models. Individual patient data from the 339 subjects with paired biopsy results in EMMINENCE were used to construct univariable and multivariable analysis of covariance (ANCOVA) models for the 12-month change in each histological component (steatosis, inflammation, ballooning, and fibrosis). Candidate predictors included age, sex, type 2 diabetes (T2D) history, and baseline and 12-month changes in weight and laboratory parameters (liver injury markers, cholesterols, insulin sensitivity markers, ELF, CK-18[M65]). Nonlinearity of the association between each continuous predictor and each outcome was examined by assessing the significance of the nonlinear components of a restricted cubic spline transformation for each predictor. Appropriate transformations (quadratic, cubic, linear spline, log2) were applied where deemed necessary based on the review of the Akaike's information criterion and visual examination of plots of the predicted outcomes against each predictor.
Due to issues with estimating the associations when severe multicollinearity exists among predictors in the model, we excluded ALT (collinear with AST) and waist circumference (collinear with weight) as candidate predictors. Multiple imputation with 10 imputed datasets and assuming multivariate normality was used to handle missing values. The occurrence of missing data for any of the predictors was relatively low ($5% missing one or more predictor values). Rubin's algorithm was used for average estimates for predictors across the imputed datasets. Backwards selection was used for each outcome, with predictors remaining in a majority of the 10 imputed datasets at the 0. 05 significance level (>5) included in the final model. Each model was adjusted for the baseline value of the outcome. If a change from baseline predictor was included in the final model, then the baseline value for the parameter was also included in the final model.
Standardized mean changes (mean/SD) or standardized mean treatment differences (mean difference/pooled SD) were used for continuous outcomes and Freeman-Tukey double-arcsine transformed proportions or log odds ratios were used for dichotomous outcomes. Meta-regression results were computed using the metafor package available in R. 30  P values <0. 05 were considered statistically significant. Composite outcome. From the meta-regression results, ranks for the association with responses and treatment effects for each histologic outcome (changes in ballooning, inflammation, steatosis, and fibrosis; NASH resolution without worsening of fibrosis, and fibrosis improvement without worsening of NAS) were assigned across seven biomarkers (AST, GGT, HbA1c, insulin, CK-18, ELF, and alkaline phosphatase) for both the slope and the mediator P value (ranks 1-7, with 7 assigned to the biomarker with the greatest slope or lowest P value).
It is possible that other more accurate measures for NASH resolution exist. The combined score of AST, HbA1, and CK-18 was found to be associated with biopsy determined NASH improvement. Hence, it is limited by the lack of reliability of liver biopsy in identifying NASH and changes in NASH, and further research into methods to increase this reliability is needed. The baseline-adjusted change in the weighted average of standardized values is proposed as a comparative measure of treatment response in the context of a clinical trial, where the magnitude of observed changes is gauged relative to the mean and variance in the study population. A composite biomarker reflecting the liver histology of an individual patient might be developed but would require access to larger clinical databases. Any such composite biomarker would ultimately require validation against clinical events.
Method The Color Game We address these questions in an online smartphone application, the "Color Game", designed to evolve an artificial language through communication between its players  (Morin et al. , 2020). The Color Game was freely available on the Google Play Store and Apple App Store for a runtime of roughly one year. All hypotheses, the exclusion criteria and analysis plan were preregistered before conducting any of the analyses and can be inspected here: https://osf. io/c8nme/. The processed data files and code of the current study can be accessed on the Open Science Framework (https://osf. io/a8bge/). Importantly, the project presented here was part of a larger registration that involved six projects related to the results of the Color Game in total. The application was created with all six projects in mind, and when controls or manipulations concern other projects we outline them as such.
Participants The survey was set up online on Prolific, a platform enabling researchers to recruit participants all over the world in exchange for payment. This study and the Color Game thus used a different sample of participants. For each of the three languages that showed robust sample sizes in the Color Game data (English, German, and French), we got survey data for 50 individuals. Only native speakers of the specific language were able to access the respective online survey. Participants were paid £1 in exchange for their help and time (about 5 min), and gave their informed consent before starting the survey.
Since we needed it for the baseline on the colour term structure in the respective languages, we start by summarising the results of the online survey. The resulting data was restructured to represent one row for each participant-term pair, i. e. each unique combination of participants and colour terms (e. g. participant 1/red, participant 2/ red, participant 1/green, etc. ; compare also  Jäger, 2012). We can think of this structure as a "profile" for each participant, recording the number of times that each of the 32 colours were associated with a specific term in their language. Then the exploratory factor analysis (EFA from here on) was applied using R version 3. 5. 1 (R Core Team, 2018). EFA is an exploratory, i. e. data-driven, clustering technique that can be used to reduce datasets that show high dimensionality to a pre-set lower number of dimensions, called "factors", while describing the data as well as possible.
Prediction 1 We then proceeded with confirmatory factor analyses (CFA from here), trying to replicate the structures found in the EFA by directly fitting it on the communicative data from the Color Game. CFA is the complementary approach to EFA, used to confirm a pre-defined structure by fitting it in a structural equation model. As such, it is able to provide a test of our native language structures' fit on the artificial language communication data. We subset the cleaned-up Color Game data to all trials from senders of the three languages that had played at least 100 trials in that role. This data was arranged so that each row represented a unique pairing of a symbol and sender, recording, for each colour and each symbol, whether the symbol was used to indicate the colour (thus mirroring the structure used for the EFA).