dataesr/scientific-paragraphs-categorization huggingface dataset:
5% of R&D budget). FP7  (2007) (2008) (2009) (2010) (2011) (2012) (2013)  extended this move and introduced the Science in Society label; with a budget that increased sharply (330 M€). H2020 (FP8 2014-2018) had a new label, Science with and for Society (SwafS) with a significantly increased budget (462 M€). In 2011, the label 'Responsible Research and Innovation (RRI)' was taken up by the Directorate-General (DG) Research of the European Commission and defined as a cross-cutting issue of the new EU Framework Programme for Research and Innovation Horizon 2020. RRI appeared as the centrepiece of the SwafS programme. Schmidt (2008; 2010)  and  Schmidt and Radaelli (2004)  have shown that the European Commission heavily draws on discourse framing as a way to overcome legitimacy deficit. Considering the changes in the Commission's discursive practices that led to RRI, one can distinguish 4 phases: public information; citizens' awareness; participation and governance; science in society  (Felt 2010 ).
We provide maps of the semantic landscape of scientific responsibility and its intellectual roots, as viewed through the lens of scientometric methods. The 'rri' corpus gathers academic articles sourced through the Scopus database whose content is close to the ideas of 'responsible research' and 'responsible innovation'. As compared to the genealogical analysis performed by Pestre, we do not aim at characterising a new discursive regime, but we rather want to identify the multiple meanings of scientific responsibility. Whereas scholars have elaborated on the polysemy of responsibility  (Reber and Pellé 2013; Vincent 2011 ) the originality of our approach lies in the systematic identification of meanings of scientific responsibility in use through the systematic analysis of the corpus of texts.
This is due to a combination of different platforms used to source the documents and the markedly different volume of texts in each, such that a direct comparison across the two corpuses was notand could not bean objective of the study. There follow three tables. The first two (Tables  1  and 2 ) list the procedural steps taken in creating and analysing the rri and the RRI corpora, respectively. Our aim in setting out the analysis in this form is to make the procedural steps of the analysis as transparent as possible. So, each lists the steps of database selection, search criteria and decisions, data analysis and factors that influence the mappings (column 1, Tables  1  and 2 ), provides a (2) Selecting the database We evaluated the two databases to choose which one was the best to work with.
We did not want to merge the two to have consistency across the data. We compared the number of results obtained for both databases. The Web of Science reported 124 references while Scopus had 207. The overlap was 40% and documents specific to Scopus represented 47% of the total references. We chose to keep the Scopus corpus. Only 29 documents that were specific to the Web of Science were not included in the corpus. (3) Corpus enlargement We sought to enlarge this initial corpus to get a picture of the scientific landscape of responsibility in research and innovation. Numerous procedures have been developed to enlarge an initial corpus. They mostly consist either in (1) broadening the list of keywords used to search on the database, or (2) following the cited references and citing references of the initial corpus, (3) mixing both approaches.
Cluster 6 (and to a lesser extent cluster 7) Table  1. Continued. Step Justification Description of the process Implications (6) Co-word mapping This approach is similar to the previous one except that the map produced is not based on the co-citations but on the main terms present in the Title, Keyword and Abstract fields. It is not historical. We created a map based on the co-word occurrences of the main terms of the corpus built in step (4)(See Figure  2 ). A threshold has to be set on the number of terms displayed. For readability purposes, the map is limited to the terms that appear at least 10 times in the corpus. Table  2. Procedural steps followed to gather and analyse the RRI corpus. Step Justification Description of the process Implications (1) Selecting the database Google Scholar is the best available source for 'crossover' literature: policy briefings, discussion papers, and other 'grey literature'.
A high number of the academic authors within this corpus worked on recently commissioned European Commission H2020 projects (and before them, FP6 and FP7 projects). The analysis confirms, therefore, that the responsible innovation community, up to 2014 at least, comprises a relatively small discourse coalition with its disciplinary heartland in STS, and shows limited reach-out to form strategic bridges with other coalitions and disciplines, yet the significance of the individuals where bridging between rri and RRI does occur may reflect a situation of the strength of weak ties. However, particularly significant, surprising and puzzling is the apparent absence of linkages between this STS community, and cognates within innovation studies that share the normative premises and values of rri/RRI.
Hospital wastewater urban wastewater emerging contaminants ecotoxicological risk assessment wastewater treatment plant sludge receiving water body groundwater a b s t r a c t The Bellecombe pilot site -SIPIBEL -was created in 2010 in order to study the characterisation, treatability and impacts of hospital effluents in an urban wastewater treatment plant. This pilot site is composed of: i) the Alpes Léman hospital (CHAL), opened in February 2012, ii) the Bellecombe wastewater treatment plant, with two separate treatment lines allowing to fully separate the hospital wastewater and the urban wastewater, and iii) the Arve River as the receiving water body and a tributary of the Rhône River and the Geneva aquifer. The database includes in total 48 439 values measured on 961 samples (raw and treated hospital and urban wastewater, activated sludge in aeration tanks, dried sludge after dewatering, river and groundwater, and a few additional campaigns in aerobic and anaerobic sewers) with 44 455 physico-chemistry values (including 15 pharmaceuticals and 14 related transformation products, biocides compounds, metals, organic micropollutants),  2 193   Each value in the dataset is associated with the analytical method used, as well as with the conditions at the time of the sampling (date and duration of the sampling).
Data accessibility Eleven files are available on Zenodo in the SIPIBEL dataset zip file: -the SIPIBEL data set description (one PDF file); -the SIPIBEL metadata (five CSV files: 1) sampling points, 2) physico-chemistry methods, 3) bioassay methods, 4) microbiology methods, and 5) hydrobiology methods); -the SIPIBEL data sets (five CSV files: 1) samples, 2) physico-chemistry data, 3) bioassay data, 4) microbiology data, and 5) hydrobiology data). Data set doi: 10. 5281/zenodo. 5176067.
Data This data paper presents the results of a 7-year study (2011-2017) at the Bellecombe pilot site -hereafter SIPIBEL -that aims at the characterisation, treatability and impacts of hospital effluents in an urban wastewater treatment plant  [1]. The SIPIBEL observatory provides data on both (i) conventional water quality parameters, and (ii) pharmaceuticals, transformation products and surfactants presence, and (iii) biological indicators that enable the long-term evaluation of risks for the environment and health: • Physico -Chemistry (PC): • • NGBI (Normalised Global Biological Index) • BID (Diatom Biological Index). Each value in the dataset is associated with metadata: sampling point, analytical or measurement method, LoD (limit of detection), LoQ (limit of quantification), validation mark, date and time of sampling campaigns, etc.
2  ). During the days of experimental campaigns: • The measured HWW daily volume ranged from 48 to 408 m 3 /d with a mean value of 176 m 3 /d: when treated separately in the 1 280 m 3 aeration tank, this corresponds on average to a 13. 75% hydraulic load and to a 7. 3 day residence time. • The UWW mean daily volume measured during dry and low rain days of experiments ranged from 2 897 to 13 504 m 3 /d, with a mean value of 5 322 m 3 /d corresponding to a 79% hydraulic load and a 1. 3 day hydraulic residence time. For the year 2013, according to internal data provided by the Syndicat des eaux des Rocailles et de Bellecombe (operator of the wastewater treatment plant, partner of SIPIBEL), compared to the dry weather design capacity, the hydraulic load was 81. 5% and the pollutant loads were the following ones: 63% for COD, 56% for BOD 5 , 54% for TSS and 51% for TKN.
Assessment of data quality Data quality is assessed for each measured value according to the quality of i) the sample, ii) the analysis, and iii) the results of blank samples. • The sample quality is evaluated according to 7 indicators ( Fig. 3  ): • sampling equipment: conformity (1), rinsing protocol (2). • type of sampling (3) (discharge proportional, time-proportional, instantaneous grab sample), sampling incidents (4) (non-existing, medium, important). • reconstitution of mean sample (5). • sub-sampling: homogenisation (6) and distribution of the sample (7). • Each of the seven above indicators can be declared as "Correct", "Incorrect" or "Uncertain". The sample quality is declared: • Correct, if all seven above indicators are correct. • Incorrect, if at least one of above indicators is incorrect.
• Correct if: • There is no significant difference between values as explained above. • The difference is significant but lower than 15% of the measured value of the samples. • Uncertain if the difference is significant and in the range of 15% to 25% of the measured value of the sample. • Incorrect if the difference is significant and larger than 25% of the measured value. Finally, data quality is declared: • Correct, if sample quality, analysis quality and blank checking quality are all correct. • Incorrect, if sample quality, analysis quality and/or blank checking quality is/are incorrect. • Uncertain, in any other case. During the project, a dedicated database, named DoMinEau and built with Excel files, was established to collect and centralise all data and make them available for all partners of the project.
Acknowledgments The  SIPIBEL  project has been made possible thanks to  SRB -Syndicat des Eaux des Rocailles et de Bellecombe  (WWTP operator), local authorities and GRAIE (non-profit association coordinating the  SIPIBEL  project). Financial support was provided by numerous partners, including the  Rhône-Méditerranée-Corse Water Agency , the  French Office for Biodiversity , the  Rhône-Alpes Regional Council , the  Haute-Savoie Council , the  European Fund FEDER , the  Ministries in charge of Environment and Health , the  Rhône-Alpes Health Agency  and the involvement of more than 12 research teams. This work was realised within the  Graduate School H 2 O'Lyon  ( ANR-17-EURE-0018 ) and  Université de Lyon (UdL) , as part of the  programme "Investissements d'Avenir " run by  Agence Nationale de la Recherche (ANR).
The southern Lake Tana region in Ethiopia, the case study area on which this article focuses, has a diverse geomorphology and is home to many geosites, such as Lake Tana and its islands (with monasteries and churches), the Zegie peninsula and its monasteries, the Bezawit viewpoint, and the Blue Nile Falls  [2]. The region is part of the Lake Tana Biosphere Reserve, which was registered by UNESCO in 2015. Furthermore, the area is strategically located in the northern tourist route of the country. For a detailed description of the geotouristic offer of this area, see Tessema et al. [2]. In the years between 2007/8 and 2018/19, over 28,000 international tourists on average every year visited this region (data from Bahir Dar City Culture and Tourism Office). In addition, official data from the Tourist Information Center at Tis Abay town shows that in the years between 2013/14 and 2019/20, an average of ca.
Segmenting Tourists to Geosites Geosites offer the opportunity for the development of geotourism. This study adopted the definition of geosites as "geological or geomorphological features with one or more intrinsic values (i. e. , scientific, educational, scenic, and recreational) as well as associated cultural and ecological values"  [2]  (p. 2). In order to give a brief insight into geotourist typologies, we identified published studies relating to geotourist typology from online sources, mainly Google Scholar. Additional studies were gathered from bibliographies of published papers. The keywords used to identify published papers were: "geotourist typology", geotourist segmentation", "clustering geotourists", "segmenting geotourists", and "geotourism market segmentation". These studies were qualitatively analyzed for their research objectives, research methods and data sources, segmentation bases, and the resulting segment types (Table  1 ).
This sampling technique was used by many tourist-segmentation studies (e. g. ,  [30, 41, [50] [51] [52]. To make the sample size representative of the population, tourists with the largest presence in the destination (based on the major tourist origin countries) were considered. As there was a lack of data on international tourist arrivals based on origin country, we estimated the sample size based on national tourist flow data and five months of tourist flow data to the Blue Nile Falls (see Figure  A1  in Appendix A), as well as on the lead author's knowledge (based on his personal observations and frequent contacts with tourist service providers such as tour leaders, tour guides, hotel managers, and tourist information center employees). The data were collected during the peak season of international tourist flows.
In addition, to meet most tourists who visited the study area during the data collection period, places where tourists could be typically found (such as restaurants and lobbies of hotels, as well as attraction sites) were selected. Moreover, at restaurants and accommodation establishments, the best times for meeting tourists (i. e. , lunch time and evening time) were selected for distributing the questionnaire. The respondents were asked to complete the questionnaire at the end of their tour of the study area. The questionnaire was self-administered. Two places were identified for intercepting tourists for data collection: attraction sites (i. e. , the Blue Nile Falls and Zegie peninsula), and accommodation establishments (in the lobby and cafeteria) and restaurants in Bahir Dar city. Collecting data from tourists at attraction sites was quite difficult because they were in a hurry, tired, and there was no place to rest and complete the questionnaire.
Profiling the Segments Demographic, trip, and behavioral variables (Table  3 ) were used to profile the four segments identified in this study using Welch's two-sample t-test. This test was conducted in each segment and compared to the rest of the dataset to explore segment differences. The categorical variables were transformed into numerical variables for the sake of t-value calculations, as shown in Table  3. The segments differed in most variables related to demographic, trip, and behavioral characteristics. The only exceptions that showed nonsignificant differences among the segments were two trip-related variables: mode of transport used to travel to the destination and whether they visited the major attractions only (i. e. , Lake Tana and/or the Blue Nile Falls).
Acknowledgments: The authors would like to thank those who helped in the data collection (Martina Marcon, Carlotta Migliori, Yiahlem Kebete, Tefera Ewunetu, Endalew Tegegne, Belachew Kassahun, Tadesse Bekele, Lake Abebe, Wagnew Eshetie, Kassa Mitikie, and Negesse Teshager). The authors are also grateful to managers and other staff in hotels and restaurants, tour guides, and tour leaders who were cooperative in the data-collection process. Special gratitude is extended to Allard van Riel of UHasselt for his valuable comments on an earlier draft of the paper. Last but not least, the authors would like to thank the anonymous reviewers for their constructive comments.
Further details on the experimental protocol are provided as "Supplementary Information". Video analysis and statistics. When analyzing the videos of the sessions, we coded for the presence/ absence of yawns (Yawn Response, YR) and the amount of time the tested subject looked frontally at the screen when showing the video stimulus (Frontal Looking, FL; Fig. 1 ). The other conditions shown in Fig. 1  and those in between them (in terms of head orientation) were parsimoniously not coded as FL. We also recorded selfdirected behaviors (SDB, scratching, self-grooming, head-shaking) during the sessions, considered as good indicators of anxiety in primates  38. We ran two Linear Mixed Models (glmmTMB 1. 2. 5042 package on R) to evaluate which variables affect the rates of FL and of SDBs. Then, we ran a Generalized Linear Mixed Model (glmmTMB 1.
This committee is registered with the "Comité National de Réflexion Ethique sur l'Expérimentation Animale" under the number 07. The current research protocol has been approved by the CREEA Ethic committee under the reference APAFIS#24418-2020012319192625 v2. This study is reported in accordance with ARRIVE guidelines. All human volunteers filmed to constitute the video stimuli gave an informed consent to the use of their images for this research project and were informed about their rights concerning the treatment of their personal data, in accordance with the General Data Protection Regulation (RGPD) compliances. The treatment and storage of these personal data were declared and approved by the Data Protection Officer (DPO) of the University of Rennes 1. Human ethic committees equivalent to IRBs exist at national level but no such approval was required for our study design according to the French regulations since we just used video recordings of humans.
Acknowledgements We thank  Marine Mergen  and  Philippe Bec  for the help given during the experiments and  Dimitri Giunchi  for his helpful suggestions on statistical analysis. Thanks also to  Filippo Bigozzi  and  Marco Germain Riccobono  for providing us with the videos of hamadryas, and to the human volunteers for their participation. Many thanks to the talented  Fosca Mastrandea  for Fig. 1 illustration.
(2012). A76, page 2 of 56 References. NGVS  (Ferrarese et al. 2012) ; GUViCS  (Boselli et al. 2011) ; VESTIGE  (Boselli et al. 2018). Using the deep data covering the entire cluster, the NGVS catalog is the most up-to-date catalog of the Virgo cluster  (Ferrarese et al. 2020). For every source in the NGVS catalog, the cluster membership probabilities are designated into three categories as a certain, likely or possible member. This was done using a rigorous algorithm involving multiple distance indicators to compute the probability that a given galaxy is a member of the cluster. This process utilizes several scaling relationships (of magnitudes, colors, and structural parameters), photometric redshift estimates, and visual inspections to confidently identify potential Virgo cluster members.
Creation of stamps We collected the photometric data from the GUViCS, NGVS, and VESTIGE surveys (FUV, NUV, u, g, r, i, z, and Hα NB) to create separate cut-out stamps for all the sources in each band. This procedure was done using the Montage tool in Python  (Jacob et al. 2010) , which co-adds the multiple exposures of the same source to a single stamp. For comparison purposes, all the stamps, including the UV images (with GALEX pixels of 1. 5 ), were projected onto a pixel scale of the optical images (NGVS and VESTIGE) with pixels of 0. 187. From here onward we call the optical stamps (excluding the GALEX ones) the "high resolution" (HR) stamps, keeping their original resolution. We also convolved them with Gaussian kernels to match the GALEX spatial resolution (see Table  1  for the resolution of the data sets) to obtain a set of "low resolution" (LR) stamps all matching the GALEX resolution.
Extraction of surface-brightness profiles The surface-brightness profile measurements were separately performed for the HR and LR stamps using the Ellipse task in the Photutils python package  (Bradley et al. 2019). This procedure measures the average flux along concentric elliptical isophotes on the stamp of each galaxy. We fixed the geometrical parameters of the galaxy (central coordinates, position angle, and axis ratio) as given in Table  D. 1 based on the NGVS catalog. The choice of concentric elliptical isophotes (rather than trying to fit the geometrical parameters in each band) was made to be consistent with previous studies  (Muñoz-Mateos et al. 2011; Boissier et al. 2016) , which allowed us to compare various band measurements made in the same physical area, which is important for color analysis and comparison to model predictions (Sect.
Surface-brightness profile decomposition We performed a simple two-component decomposition of all the profiles into a de Vaucouleurs central component (Sérsic with index n = 4) and an exponential disk, using the Profit python routine developed by  Barbosa et al. (2015). The fitting algorithm performs a weighted χ 2 minimization procedure, with a Gaussian point spread function (corresponding to the FWHM of the data given in Table  1 ) convolved with the model light profiles (both HR and LR). The initial guesses for the Sérsic and disk components in the fitting were provided based on the values from the NGVS catalog. For the majority of the sources in the sample, this procedure provided a good decomposition (about 93% of the sample has a reduced χ 2 ν < 3 in the g band). Figure  3  shows two examples of decomposition, one for a galaxy with a bright central core, and another one for a galaxy more typical of our sample with a low central-to-total-light ratio.
Color variation with the cluster-centric distance Exploring the properties of cluster galaxies as a function of their distance from the cluster center is another useful tool in studying the role of the cluster environment in their evolution. Figure  6  shows the variation of the ui color of the sample with respect to the projected cluster-centric distance. There is an indication of a color variation, with redder sources found more frequently toward the cluster center, whereas bluer sources more in the cluster outskirts (outside half the virial radius). This visual impression was further verified by a linear regression fitting of the entire sample using the linmix Python package  (Kelly 2007) , as shown in Fig. 6. The linear fit has a slope of -0. 21 ± 0. 18 and an intercept 1. 73±0. 18 (see Table  4 ).
H I gas content The Arecibo Legacy Fast ALFA (ALFALFA;  ). The solid black line and the red-shaded region give the linear regression best fit and the 3σ scatter, respectively, obtained using the linmix Python package  (Kelly 2007). Sources with only an upper or lower limit in the color are excluded from the plot. and found that only about 8% of our sample (5 galaxies) have an H i counterpart within the beam of Arecibo (∼3. 5 diameter). This means that the majority of the galaxies in our sample have a H i mass <10 7 M. Table  2  and Fig. 7  shows the 5 galaxies with ALFALFA H i detection. All of the H i detected galaxies belong to the diffuse galaxy subsample (none of the UDGs in our sample have an H i A76, page 9 of 56 A&A 667, A76 (2022)   6 ) Offset of the ALFALFA beam centroid with respect to the NGVS coordinate of the source.