dataesr/scientific-paragraphs-categorization huggingface dataset:
Subjects Data were collected from infants (N=8) of the well-habituated wild chimpanzees (N≈70) of the Sonso community  (Reynolds 2005)   1  for details). For further information about the study site, see  Eggeling (1947)  and  Reynolds (2005). We collected data from those specific infants because they could easily be followed in the forest, as they were born from well-habituated mothers, unwary of human observers.
Data collection Distress calling episodes were continuously videotaped in all-occurrence sampling  (Altmann 1974). Despite their acoustic variability, the short, tonal, and sometimes sequential production pattern of distress calls enables trained listeners to clearly identify these calls as distinct from other infant vocalizations such as grunts or barks (see ESM Figure  1  for a spectrogram and ESM for video and audio examples). The entire dataset, part of which was used in this study, was gathered by the first author and is composed of 271 call episodes (i. e. , a series of distress calls interspaced by no more than 10s). They were video-recorded using a Panasonic HC X909/V700 video-camera, with the sound captured by a Sennheiser MKE-400 shotgun microphone. To account for any missing information in the videos, all potential causes of infant distress calling, as well as other contextual information, were recorded.
Quantification and statistical analysis First, we pre-processed the raw audio files (wav format, 44100 Hz) by applying a band pass filter (filter order 10) from 100 to 700 Hz. We also filtered out background noise using a Vuvuzela denoising algorithm, implementing noise spectrum extraction, signal-to-noise ratio estimation, attenuation map and inverse short-time Fourier transform computation  (Boll 1979; Ephraim and Malah 1984). The dedicated MATLAB functions are freely available at MathWorks FileExchange  (Choqueuse 2020). Distress calls could be above 700 Hz but this was the maximal value of the peak fundamental frequency at mid-call of the first distress call of the episodes composing the entire dataset. Despite being very conservative, this pass band was appropriate for the localization of exemplars and to avoid false positives.
We subdivided the audio files into 10ms segments, determined the prominence of each exemplar time element's energy and applied a cut-off threshold at the 80th percentile of the energyprominence distribution. We then sorted out those extractions that were shorter than 40ms or longer than 120ms (corresponding to the range of durations extracted manually from distress calls, based on the first call of the distress call episodes of the entire dataset) and subjected the remaining extractions to a human-based validation process, leaving 1330 exemplar on-and offsets. We extracted the full frequency spectrum ranging from 50 to 4000 Hz of a given exemplar on-and offsets. We conducted a four-stage analysis, consisting of 'feature extraction', 'feature selection', 'classification' and 'feature evaluation':.
Feature selection Prior to classification, we conducted a feature selection procedure by reducing the number of features to a set of reliable features to explain the maximum variance in a given data set. We applied a t-test on each feature dimension by comparing values of the feature dimensions that were sorted by predefined class labels (e. g. , situation 'Threat' vs. situation 'No threat'). For each comparison, we used randomly determined 75% of the samples and re-ran the procedure five times. MATLAB's MathWorks webpage provides a tutorial on feature selection and all associated functions. Such feature selection procedure is called a filter approach, where general characteristics are evaluated for the selection without subjecting the dimensions to a classifier. The feature variance and feature relevance (i.
Classification We implemented support vector machines (SVMs) using the LIBSVM toolbox  (Chang and Lin 2011). Classification consisted of training and testing phases: first, 80% of the exemplars were selected to constitute a training dataset. Those exemplars were given attributes that indicated their 'class' (i. e. , exemplars are marked with an attribute that tells whether the exemplar was taken, for example, from situation 'Threat' = 1 or situation 'No threat' = 0) and the model was trained to separate optimally between classes. Next, the trained model was tested on the remaining 20% of the exemplars (i. e. , the test dataset) for which the attribute was unknown to the model. The performance of the model (i. e. , its capacity to assign the correct class or attribute to this exemplar of the test dataset) was then evaluated and compared to a baseline level (the proportion of exemplars pertaining to a particular class in the whole set of exemplars).
Training and testing were always done on two classes. We used a radial basis function (RBF) kernel and 5-fold cross-validated the parameters C and Gamma with separate smaller data sets (for details see  Fedurek et al. 2016). We used the top 40 feature dimensions for classification (see 'feature selection' above), omitting all others. We obtained performance scores from the models that were trained and tested on the same labels (basic classification procedure) and from cross-comparisons of conditions, such as training on situation 'Threat' and testing on one type of maternal response. Previous work has shown that this method provides useful insights into the nature of information coding  (Caldara and Abdi 2006; Fedurek et al. 2016; Dahl et al. 2018). We compared the scores of correct classifications (i.
e. , the proportion of exemplars being assigned their true attribute [or 'class']) to a baseline level (i. e. , the actual proportion of exemplars that correspond to a given attribute or class in the set of exemplars) using onetailed two-sample t-tests. Because tests multiple tests are being carried out using the same dataset, two methods  (Holm and Benjamini-Hochberg)  were used to correct for multiple comparisons  (Holm 1979; Benjamini and Hochberg 1995). To ensure that no single individual unduly influenced the outcome of the classification, a leave-one-out method was used, in which the general classification procedure was re-run eight times by omitting exemplars attributed to one infant in each run. We tested for a significant interaction between leave-oneout runs and the individual comparisons using a two-way ANOVA test.
This is further exemplified by the fact that discriminations between exemplars found in situations 'Threat' vs. 'Separation' on the one hand, and 'Threat vs. No threat' on the other hand, relied on common features. Our results suggest that the acoustic information present in distress call episodes contain information about the type of external events triggering calling as well as the nature of the problem. In fact, distress calls encode certain information, at the situation level (threat versus no threat), at the problem level, and are associated with maternal responses. These results do not appear to be driven by the inclusion of particular individuals in the dataset (as indicated by insignificant leave-one-out procedures) or by the prominence of particular age classes for specific problems.
Acknowledgments We thank the  Uganda Wildlife Authority (UWA)  and the  Uganda National Council for Science and Technology (UNCST)  for permission to conduct the study, and  Geoffrey Muhanguzi ,  Caroline Asiimwe ,  Sam Adue  and  Monday M'Botella  for their support in the field. We are grateful to two anonymous Reviewers, the Editor and  Bahar Tuncgenc  for helpful comments on the manuscript.
Data analysis Quantitative variables were expressed as mean ± standard deviation, categorical variables as percentage. The internal consistency of each scale was assessed by the Cronbach's alpha coefficient. Bivariate analyses were conducted by logistic regression to investigate the relationship between the presence or absence of post-traumatic distress or burnout with each of the variables of interest (for post-traumatic distress: Hospital, work status, age, gender, marital status, children, tenant/owner, years in Saint-Marteen/Saint-Barthelemy, power outage, water shortage, internet outage, telephone outage, theft, assault, injury, psychological support, back home<1 month, and home damage. For burnout: Hospital, work status, age, gender, marital status, children, tenant/owner, years in Saint-Marteen/Saint-Barthelemy and post-traumatic distress).
Discussion To our knowledge, no epidemiological data have been published concerning psychiatric disorders following Irma. This first study reveals several factors associated with the emergence of post-traumatic distress or burnout among hospital staff in the aftermath of a hurricane. In addition, it allows us to estimate the significant psychological consequences of Irma on hospital staff in Saint-Martin and Saint-Barthelemy. Explanatory factors associated with the risk of developing post-traumatic distress included gender, home damage and electricity shortages over one month. Women were more likely to develop post-traumatic distress, with 37 of the total 38 cases of post-traumatic distress. This over-risk has already been described previously but to a lesser extent  [7, 15, 16]. Indeed, the literature usually suggests that women are twice as vulnerable to post-traumatic distress as men among victims of potentially traumatic events (all causes combined).
Acknowledgments The authors would like to thank  F. Moreno ,  C. Blanchard ,  S. Masse ,  J. Hamlet ,  G. Berry  and  G. Rodriguez , administration members of the hospitals of  Saint Martin  and  Saint Barthe  ´lemy;  A. Leroy ,  I. El Mahfoudi ,  N. Lislet ,  N. Curon  and  C. Cazalet  from the department of psychiatry; PM. Linet and  E. Meigne  from the department of emergency medicine; B. Tressière from the  Clinical Research and Innovation Department of West Indies and French Guiana ;  MH. Pepin ,  R. Gromat  and  A. Delsol  from the  French Meteorology Department Meteo France. Our special thanks are extended to all the participants for their time and cooperation.
Acknowledgements This chapter is part of the project " Migration and Transnational Social Protection in (Post)Crisis Europe (MiTSoPro) " that has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant agreement No. 680014 ). In addition to this chapter, readers can find a series of indicators comparing national social protection and diaspora policies across 40 countries on the following website: http://labos. ulg. ac. be/socialprotection/.
Statistical analysis Results are expressed as number (percentage) or mean (percentage) ± standard deviation. Cost estimations are expressed as percentage of the real cost. A correct estimation is defined as between 50 and 150% of the real price. Two variables were compared using a t-test on XLSTAT® software. For more than two comparisons, data were compared using ANOVA. Using multivariate analysis, we determined parameters which were associated with the o of an incorrect estimation. Odds ratios (OR) were estimated with a 95 % confidence interval (95 % CI). A p-value less than 0. 05 was considered significant.
By combining the extensive knowledge and experience of the NORMAN network with the financial and policy-related strengths of the PARC initiative, a large step towards the goal of a non-toxic environment can be taken. Keywords: NORMAN network, Suspect screening, Non-target screening, Contaminants of emerging concern, Environmental monitoring, High-resolution mass spectrometry, Effect-based methods, Chemical risk assessment and prioritisation to foster validation and harmonisation of measurement methods and monitoring tools; (iv) advanced data analysis tools to deal with less-investigated substances in support of a European early warning system to detect emerging chemical risks to the environment, and (v) a system for harnessing, combining and sharing expertise among research teams, national reference laboratories and environmental agencies in innovative methods in support of chemical risk assessment.
Perspective and recommendations Continue to develop the NORMAN Database System (NDS) as a reference database that brings together, in a single platform, widely differing chemical monitoring data acquired using various techniques and in different matrices, thereby ensuring a harmonised approach for data collection, storage, quality control, curation and exchange among NORMAN members and more widely. Future platform development will be guided by the FAIR principles (Findability, Accessibility, Interoperability, and Reuse of data). The NDS is complementary to the EC Information Platform for Chemical Monitoring (IPCHEM)  [8, 9]  in harvesting chemical target monitoring data, while at the same time paving the way for the development of a new European infrastructure for handling data coming from innovative methods, such as non-target screening (NTS) and effect-based methods (EBM).
Rationale The crucial task of gathering and managing environmental CEC exposure data to support chemical risk assessment has been the core activity of the NORMAN Association from its start in 2005. The current NDS  [10]  is an open-access platform of interconnected databases able to assist effective and rapid screening and risk assessment of contaminants in the environment. The unique feature of the NDS is that it provides a comprehensive set of data on CECs together with a range of innovative applications for their hazard and risk assessment. These tools range from physico-chemical properties, use characteristics, mass spectral information, and exposure data from target and non-target screening in all environmental compartments, to ecotoxicity data and in situ bioassay signals reflecting mixture toxicity.
The NDS currently consists of 12 modules (Fig. 1 ), of which eleven (Substance Database (SusDat); Suspect List Exchange (SLE); Chemical Occurrence Data (EMPO-DAT); Ecotoxicology; Bioassays Monitoring Data; Mass-Bank Europe; Digital Sample Freezing Platform (DSFP); Indoor Environment; Passive Sampling; Substance Factsheets; Prioritisation) are accessible, interlinked and populated with data. The 12th is an antibiotic-resistant bacteria and genes module (ARB&ARG) that is still under development, while a new module hosting data on microplastics is currently being designed. A selection of the NDS modules most relevant to PARC is presented below.
Rationale A common, harmonised list of chemical compounds shared among all parties in research and regulation is one critical requirement for enhanced cooperation among existing regulatory frameworks and shifting towards a "one chemical, one assessment" paradigm. However, current chemicals lists are fragmented collections, with researchers and regulators all using their own lists. We believe that the combination of NORMAN Suspect List Exchange (SLE)  [12]  and the merged NORMAN Substances Database  [13]  of the NDS could be a globally leading model for collaboratively working towards such a list. Numerous organisations, national and international regulatory agencies and research groups from Europe and North America already contribute to this initiative. NORMAN SLE is a platform to share lists of substances potentially responsible for emerging risks to ecosystems and human health.
The submitted lists are shared with US EPA CompTox Chemicals Dashboard  [14, 15]  and PubChem  [16, 17]  and are published on Zenodo  [18]. By acting as a data collector, the NORMAN SLE has become an important source of specialised research information for major chemical databases such as PubChem and CompTox, beyond the realms and means of individual researchers. In return, the integration of the NORMAN SLE into major chemical databases adds enormous value to the original contributions, offering up new functionality for all parties. The merged list (without duplicates) is known as NORMAN SusDat  [13] -a curated compound database (65,697 compounds as of April 2020), where substances are merged by the Standard InChIKey, which acts as the unique identifier. This is accompanied by other structural information such as CAS numbers and SMILES, as well as physico-chemical properties.
SusDat also contains mappings to the equivalent "MS Ready" forms  [19] , as well as other mass spectrometric information for the identification of compounds with NTS techniques, estimated (in silico) Predicted No-Effect Concentrations (PNECs), and other information required for the prioritisation and risk assessment of substances. Since 2016, SusDat has been used to interlink all NORMAN databases among themselves, as well as the NDS with major external databases.
Rationale We propose to share the NORMAN Ecotoxicology Database  [20]  for harmonised ecotoxicity assessment within the PARC partnership. The database provides a transparent tool to guide experts in: (i) the identification of the reliable ecotoxicity studies, based on the CRED (Criteria for Reporting and Evaluating ecotoxicity Data) classification system  [21] ; (ii) the online derivation of a set of quality standards for each matrix and regulatory framework based on selected 'reliable' ecotoxicity studies, using a built-in software tool implementing the requirements of the EC guidelines  [22] , and (iii) the final selection of a single, common PNEC value, agreed upon as a result of Europe-wide expert consultations. At present the database comprises, for almost all Sus-Dat substances (i. e.
> 65,000), at least one in silico PNEC  [23]  based on predicted acute effects for each of the three basic trophic levels of the fresh water compartment (fish, daphnia, and algae), which are used when experimental toxicity data are insufficient or not available. In 2019, a semi-automated tool for retrieving experimental (eco) toxicity data from the US EPA ECOTOX Knowledgebase allowed the import of > 125,000 experimental data on standard (eco)toxicity endpoints for about 5000 Sus-Dat substances in a format compatible with the metadata requirements of the NORMAN Ecotoxicology Database. Additional experimental (eco)toxicity data and threshold values will be retrieved from other databases such as the REACH portal, the ETOX database of the German Federal Environment Agency, as well as existing PNECs and Quality Standards (EQS) from various regulatory sources.
Rationale A game changer for next generation chemical risk assessment is a system able to provide comprehensive information on the exposure of humans and the environment to large numbers of chemicals during the entire life cycle of products, including waste and recycled products. With the EMPODAT database module  [24]  of the NDS, the NORMAN Association has already established a collaboration with IPCHEM, the official European repository of monitoring data produced by national monitoring programmes and EU-funded research projects in all matrices and compartments. EMPODAT today hosts approximately 10. 3 million geo-referenced target monitoring data of more than 3100 substances in water (surface, ground, and waste water), sediment, biota, soil, sewage sludge and air matrices. The data are publicly accessible and provide an overview of benchmark values on the occurrence of contaminants of emerging concern across Europe.
Suspect screening of pre-defined lists of tens to tens-of-thousands of known substances in each sample (supported by NORMAN SLE and NORMAN SusDat) is presently the recommended way forward. In this context the Digital Sample Freezing Platform  [26]  is a key tool developed by NORMAN to support suspect and non-target screening. This novel technology allows the storage of thousands of high-resolution mass spectra (fingerprints) of all chemicals, metabolites and transformation products detected in each of the analysed samples. Thanks to this platform, it is possible for users to search retrospectively for a large number of compounds (e. g. those in SusDat; see above) in all the "digitally frozen" samples stored in the database and obtain reliable qualitative and semi-quantitative data on their occurrence in the investigated samples.
• MassBank Europe, an open-source, open-access database of mass spectra to support higher confidence identification of suspects and non-targets  [27, 28]. Based on MassBank Japan, MassBank Europe was founded in 2011, arising from a NORMAN initiative. Today MassBank contains over 80,000 unique mass spectra for > 14,300 compounds (database release 2020. 05 [29] ), including mass spectra of tentatively identified compounds. MassBank Europe is a core service for NORMAN as well as for other initiatives such as HBM4EU (Human Biomonitoring for Europe) initiative  [30] , ELIXIR  [31] , the German Network for Bioinformatics Infrastructure (de. NBI)  [32]  and the German National Research Data Infrastructure Initiative for Chemistry (NFDI4Chem)  [33] ; • A Retention Time Index (RTI) prediction model  [34, 35]  allowing for tentative identification of each compound in SusDat as a combination of its exact mass, MS/MS fragments and the predicted RTI value, reduces the number of false positives in suspect screening.
Thanks to all the above-mentioned interconnected tools, DSFP can provide reliable qualitative and semiquantitative data on the occurrence of already identified as well as novel CECs, thereby providing exhaustive insight into the spatial and temporal distribution of contaminant mixtures in the environment, making NOR-MAN DSFP a virtual environmental observatory on chemical contamination. Extensions of DSFP for additional chemicals captured in SusDat (e. g. highly polar molecules and gas chromatography-only amenable substances) are under way.
Thanks to DSFP and the set of fully integrated tools and databases developed by NORMAN, it is now possible to obtain an overview of the state of knowledge (spatial distribution of contaminants, degree of exceedance of threshold values based on semi-quantified data, etc. ) of a dynamically updated list of > 60,000 chemicals, including many never studied before, and to identify priority substances/groups of substances for which further actions need to be taken. This approach fits well with the requirements of an Early Warning System, where the data to correctly identify an emerging risk at an early stage are typically limited or of poor quality. In this context, it is important to use a transparent and rational approach for signal identification and characterisation that is able to deal with the knowledge gaps that still prevent proper risk assessment and risk ranking of most emerging substances.
This should be followed by a comparison of the estimated ∑TU or BEQ from the component-based assessment with the TU and BEQ derived from the bioassay testing. If EBTs are exceeded and the component-based assessment cannot explain the activity detected in the bioassay, an Effect-Directed Analysis (EDA) protocol should be performed in order to identify the risk drivers  [54, 60, 61]. In this context, NTS-based approaches are key to improving the identification of risk drivers and facilitating compound/mixture prioritisation in different matrices. As a matter of fact, large datasets from non-target screening and effect-based methods can be explored using multivariate statistics and pattern recognition methods to identify peaks that co-vary with detected effects (virtual effect-directed analysis).