dataesr/scientific-paragraphs-categorization huggingface dataset:
In conclusion, despite the use of GEMA methods on this topic, only few studies have used GIS to objectively measure environmental features or to document time-varying effects of these components of environmental exposures  (Bollenbach et al. , 2022; Jacobson and Bhattacharya, 2022; Tao et al. , 2020). In the present study, we expanded the above body of work by collecting individual-level spatiotemporal location data and repeated measures of momentary depressive symptomatology with GEMA. Mobility paths (based on continuous timestamped and geolocated individual positions) were then used to objectively measure the time spent in different streetscape microenvironments over the two hours prior to participants completing EMA questionnaires. Our main hypothesis is that being exposed to a pleasant, familiar, legible, socially attractive, accessible, comfortable and safe streetscape environment over two hours will affect the momentary depressive symptomatology.
Study participants and data collection This study included a sample of 216 adults aged 60 years and over from the RECORD Cohort  (Chaix et al. , 2011) , selected for the HANC (Healthy Aging and Networks in Cities) and MINDMAP sub-studies  (Fernandes et al. , 2021). The study integrated traditional computerbased surveys, a web-based mapping application (VERITAS) questionnaire  (Chaix et al. , 2011; Naud et al. , 2020) , mobile sensing tools and a GPS-based web mobility survey  (Chaix et al. , 2019). Data were collected in the Paris region (France) from July 2019 to July 2021. Before the observation period, participants filled in a questionnaire assessing their health and socioeconomic profile. During their daily activities, participants wore a GPS receiver (BT-Q1000XT, QStarz, Taipei, Taiwan, with a 3-meter accuracy) and an accelerometer (tri-axial, wGT3X+, Actigraph, Pensacola, FL) on the waist on the right side and were provided a smartphone for 7 days.
Participants completed a paper-based travel diary on the places visited, as supporting information for the mobility survey conducted after the observation period. An EMA smartphone questionnaire was administered four times a day (at random time within the following slots: 9:00 am-12:00 am, 12:00 am-2:00 pm, 2:00 pm-4:00 pm, 4:00 pm-6:00 pm) to survey depressive momentary mood through the Eco Emo Tracker application developed for the study  (Fernandes et al. , 2021). Eco Emo Tracker is a smartphone application that allowed us to survey environmental perceptions and momentary mental mood states in real-time throughout EMA questionnaires. After the 7 days, GPS data were uploaded in the TripBuilder Web mapping application; visited places, trips, and transport modes were automatically identified with algorithms; and a web-based mobility survey was conducted on the phone to confirm or correct these visited places and transport modes, while considering the travel diary filled by the participants  (Chaix et al.
Streetscape micro urban environment and activity variables We quantified the time spent in microscale urban environments to which people were exposed over two hours prior to responding to each smartphone EMA questionnaire (see Fig. 1  for the whole process). We used the database of location points created by the research assistants during the mobility survey with the TripBuilder application (see above). This database was created by merging GPS points (collected each 5 s), points related to Google directions API (shortest street network trips generated with the TripBuilder software when there were no GPS data at all collected for a trip), and points related to trips manually drawn by the research assistant during the mobility survey when Google directions API could not be used (e. g. , for a trip through a park without street network).
For Google directions and manually drawn trips, we generated points (every 10 m) and corresponding imputed times along the tracks (with ArcMap Desktop 10. 1 and R 4. 0 software). Then, we selected the points corresponding to tracks and places visited over the two hours before answering each EMA questionnaire. All the points located outside the Ile-de-France region were removed from this selection. In total, the final database was composed of 1,333,759 (93%) GPS points, 1,929 (0. 1%) points manually drawn and 98,305 (6. 9%) points from Google direction API. We classified all GPS points as being outdoor or indoor based on the number of visible satellites. Our aim was to explore whether the association between streetscape elements and momentary mental well-being was stronger when environmental exposure was defined only on the basis of outdoor GPS points.
2 ). Streetscape built environment attributes and sociodemographic characteristics were measured by geoprocessing data obtained from the National Institute of Statistics Economic Studies (i. e. census data, the FiLoSoFi database, the Permanent equipment database), the Regional Institute of Paris [i. e. street and road network, urban tissue data (TUF), and land use data (MOS)] and the Bruitparif centre (2017 LDEN -Day-evening-night level -strategic noise pollution map for road, railroad and airborne traffic, 5-meter resolution, using the NMPB 2008European method (SETRA, 2009)). Aspects of the pleasantness of the environment were measured by the percentage of space covered by green and blue spaces. Distinctiveness and attractiveness were measured by the percentage of space covered by landmarks and architectural elements and by the presence of commercial, leisure and cultural attractors.
Statistical analysis After calculating descriptive statistics, considering the nested nature of our data, we fitted Bayesian linear mixed effect models with Monte Carlo Chain using the Stan modelling language  (Carpenter et al. , 2017)  and the brms R package (R Core Team, 2014)  (Bürkner, 2019). Based on the item response theory  (Embretson & Reise, 2013; Gibbons et al. , 2008) , we modelled the data at the item levels (items are nested within questionnaires, nested within days, nested within participants). The fundamental premise of the item response theory is that every response to an item provides some inclination about the individual's level of the latent trait or ability, here the CES-D 8 score. To distinguish between the items, we controlled for indicator variables corresponding to each item, allowing to assess the so-called severity of the items in their ability to discriminate between individuals with different levels of momentary depression symptomatology.
Results We collected data from a sample of 216 people aged 60 years and older. Participants were more often males (64%), living in couple (67%) and retired (85%). The sample was distorted towards high education levels (Table  2 ). Participants responded to 4830 EMA depression questionnaires corresponding to 9689 questions (i. e. , momentary depressive symptomatology items). More than one million (1,082,047) points in space were available to measure relations people have with the built environment over time. Each individual answered an average of 22. 3 questionnaires and 48. 6 questions over a week. Overall, participants responded to 84% of the a priori planned questionnaires. For each of the 8 depression items, we collected on average 1211 responses, while each item was answered 5. 7 times during the week by the same individual.
This novel study by AINSWORTH et al. [5]  used a patient-centred approach to explore whether there are discrepancies between patient and clinician perspectives of the most important aspects of severe asthma (e. g. severe asthma barriers)  [4]. This study analysed data from a cross-sectional survey that was sent to physicians from severe asthma clinics in seven European countries (the Czech Republic, Denmark, Greece, the Netherlands, Slovenia, Portugal and the UK). The physicians were recruited via the European Respiratory Society Severe Heterogeneous Asthma Research collaboration. Physicians completed the first part of the online survey without having access to the patients' identities. The second part of the online survey was completed by eligible patients who did not have access to the physicians' identities.
EBS involves the use of data streams from informal sources. In the medical domain, the extraction and use of epidemiological indicators from new data sources have become a hot research topic over the last few years. A large range of sources can be used in human medicine, such as chief complaints  [7] , electronic medical records  [8]  or more informal sources, such as social media  [9]. Symptoms are extracted from unstructured textual data and gathered into syndromes, manually or through text mining methods. A syndrome can be defined as ''a combination of clinical signs that repeatedly occurs in different observations, indicating a possible presence of disease"  [10]. Monitoring those syndromes should allow the detection of an outbreak before an outbreak has been diagnosed. However, optimal syndrome definitions adapted to each specific data source have not yet been determined  [11].
g. , singular form for nouns) to be analysed as a single item. Both lowercasing and lemmatisation aim to compute the occurrence of derived forms of a term (which are semantically similar) as a single item. In this work, all of the preprocessing steps were conducted using the NLTK Python library  [31]. To remove stop words, we used the list of 318 English stop words provided by the library. After preprocessing, the features can be selected according to their POS tag (Fig. 1 ). For instance, only the terms that are verbs are retained. The methods mentioned above are illustrated in Table  1. There is no gold standard procedure for preprocessing steps that should be applied to a text corpus. The choice of whether to include normalisation depends on the task and the nature of the corpus (e. g. , its language)  [28].
The decision step is first performed using unimodal features in the context of late fusion. Then, the outputs of the decision step are combined into a single final dataset. The advantage is that the features are combined at the same representation level (e. g. , similarity  I n f o r m a t i o n P r o c e s s i n g i n A g r i c u l t u r e x x x ( x x x x ) x x x Please cite this article as: S. Valentin, R. Lancelot and M. Roche, Fusion of spatiotemporal and thematic features of textual data for animal disease surveillance, Information Processing in Agriculture, https://doi. org/10. 1016/j. inpa. 2022. 03. 004 matrices). The main limitation is the increase in computing time and the potential loss of correlation  [37]. A weight can be applied to control the impact of early and late fusion.
An epidemiologist read each of the 438 documents to detect all disease events they contained. To ensure a consistent and reproducible annotation, events found in the documents were compared to a gold standard database, i. e. , the EMPRES-i database. Each detected event was labelled using the unique EMPRES-i identifier. When the epidemiologist could not link an event to an official event, she created a new event identifier and manually recorded the epidemiological features (location, date, disease and host). News articles containing at least one event represented 53% of the corpus (n = 229/438). Among these news articles, 52% (n = 127/229) reported several events, with a median number of 3 events (Table  4 ). One news article contained a maximum number of 208 events due to the reporting of 200 avian influenza outbreaks in Taiwan on 28 January 2015.
Acknowledgements This study was partially funded by  EU  grant  874850 MOOD  and is catalogued as MOOD009. The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the  European Commission. This work has also been funded by the  French General Directorate for Food (DGAL) , the  French Agricultural Research Centre for International Development (CIRAD) , the  SONGES  Project ( FEDER and Occitanie) , and the  French National Research Agency  under the Investments for the Future Program, referred to as  ANR-16-CONV-0004  (#DigitAg).
5 million inhabitants (18% of the population). In India, metropolitan areas cover all cities having more than 1 million inhabitants, which includes 9 megacities with more than 6 million and 70 cities with more than 1 million inhabitants (30% of the population). The South African team has not been able to advance on such disaggregation between metropolitan versus non-metropolitan areas. metropolitan areas, down from 93% today. For India, constrained mobility has been assessed at a very low level, around 24%, and not distinguished by geographic areas. This is mainly due to the lack of detailed datasets and that inter-city or long-distance travels have been determined to be mostly non-constrained activity. While such data are useful to develop specific activity-and geographical-based policies, this example reveals the difficulty to source these data in developing countries.
Regarding the deployment of electro-mobility, the analysis of barriers highlighted the vested interests of the fuel and automotive industry in Brazil and Indonesia, and focused on the economic challenges for both individual and collective transport service operators on the costs of purchasing their vehicles. In the context of the Global Stocktake, developing such analysis of the barriers and enablers for the transition and sectoral conversations with the different stakeholders will be vital to foster cooperation and identify how national ambitions and actions can be raised. With respect to further research building on this paper, we note a range of limitations that require additional focus. First, the cross-country comparison of the mobility patterns based on trip purpose is challenging. Indeed, the project suffered from lack of a structured database related to the trip purposes in different geographies, and we were only able to confidently assess it in Brazil and Indonesia, based on studies in key urban areas.
Funding This work was supported by the  Agence Nationale de la Recherche of the French government  through the  Investissements d'avenir  [ ANR-10-LABX-14-01 ] programme, by the  International Climate Initiative (IKI)  of the  German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU)  as part of the ' Climate Action After Paris' project  (nr. 18_I_326 ), by the  Michelin Corporate Foundation  and by the  South African National Energy Development Institute (SANEDI)  for funding the development of the  South African  transport model.
Questionnaire 2: Emotions in the context of confronting experimental results Participants were invited to think about their personal emotional experience in the context of the following scenario: (S) Your experiment is completed, and its analysis is done. Now, for the first time, you see the full results of your experiment. The task was to estimate how often participants would think about their experimental result using one of the options provided in the item list (Figure  2 ). The scaling was between 1 ('never') and 5 ('very often'). A text box was provided at the bottom of the questionnaire in case respondents would want to share some comments in a free form. After the data was collected, the central tendency was calculated in the form of a median for each of the items. The list of items contained a variety of emotions, supplemented by two control items that would indicate a lack of pronounced emotional experience.
Besides, just like emotions, meta-cognitive feelings, and action tendencies can have a certain phenomenological profile: they are felt as something. Hence, participants were invited to reflect upon some such experiences that might accompany the moment of seeing the complete result of their experiment. Participants had to choose how often, if at all, they might have one of the listed experiences. After collection, the data were processed as before (Figure  3 ). At the bottom of the questionnaire, a text box was provided in case respondents wanted to share some comments in a free form. The list of items was compiled from science-associated cognitive experiences, such as epistemic feelings. Epistemic feelings are targeted at one's mental processes and capacities; their examples include feelings of knowing, error, familiarity, the sense of agency over thoughts, and some others (De Sousa 2009; Arango-Muñoz and Michaelian 2014).
, 2019; Marquet et al. , 2022 Marquet et al. , , 2020;; Rodríguez et al. , 2012). From a public health point of view, objectively-measured data on behaviors, locations and exposures are critical to examine whether specific neighborhood environmental attributes enhance or hinder accumulation of sedentary time  (Cerin et al. , 2016). In this study, we explored the environmental correlates of sedentary behaviors using an activity space approach based on GPS tracking, and accelerometry-based measures of sedentary behaviors. We explored three hypotheses, based on matched GPS and accelerometer data and GIS-measured built environment exposures. First, we hypothesize that built environment attributes are associated with total, location-based and trip-based sedentary behaviors (Table  1  describes each environmental attribute and potential direction of association expected with sedentary time) (H1).
Population This cross-sectional study is part of the Luxembourgish case study of the CURHA project (Understanding the Role of Contrasted Urban contexts in Healthy Ageing)  (Kestens et al. , 2016). The participants, aged years and more, were selected by stratified random sampling from Social Security files ("Inspection Générale de la Sécurité Sociale") to ensure uniform distribution across age categories, gender and along spatially contrasted strata representing different degrees of urbanity. Between April 2015 and January 2016, 471 participants completed a face-to-face questionnaire focusing on self-reported data on socio-demographics, health, well-being, perception of residential neighborhood and overall physical activity habits. The participants wore a SenseDoc 2. 0 (Mobysens technologies) device -which includes a GPS tracker and a triaccelerometer -at the right hip for 7 consecutive days.
Data Visited locations and trips were extracted from the GPS tracks using a validated kernel-based algorithm  (Thierry et al. , 2013). This algorithm detects peak densities in GPS coordinates to identify visited locations, and then assigns dynamic portions of tracks as trips segments  (Brondeel et al. , 2019; Klein et al. , 2021). Raw accelerometer data in gravitational units were extracted from the SenseDoc devices using SenseAnalytics (version 1. 9), and then matched to GPS locational data (trips segments and locations) based on the timestamps. As performed elsewhere  (Brondeel et al. , 2019) , the raw data were converted into activity counts in MATLAB (version R2017a)  (BrØnd et al. , 2017) , and non-wear time was defined as 90 min of consecutive zero activity counts, allowing for min of non-zero counts  (Choi et al.
Activity spaces exposure measures thus encompass all locations visited during the 7-days of tracking. Eight environmental variables related to the natural environment, walking and biking infrastructure, and motorized environment infrastructure were considered. Table  1  presents the definition of built environment variables and a priori hypotheses of related effects on sedentary behaviors. As environmental variables were mostly non-normally distributed, they were categorized into tertiles (i. e. , low, medium, high), or into binary variables based on the median value (i. e. , low vs. high) when relevant. All environmental variables were estimated from Open Street Map (June 2017) using ArcMap 10. 5. Weather covariates. Mean temperature per day in Celsius degrees and mean precipitation per day in millimeters were collected from  Wunderground open data and used as covariates for each day of sedentary time monitoring.
Statistical analyses Analytical sample. From the initial sample of 471 participants, 24 participants with no GPS records were excluded. Seven other participants were excluded for spending more than 10 % of their time outside Luxembourg or residing outside Luxembourg. Among the 440 remaining participants, a total of 3238 days of accelerometer data were recorded. After excluding days with <10 h of wear time (n = 51), and missing values in control variables (n = 29), the final sample included 360 participants and 1,881 valid days of recording. Regression models. The time spent sedentary per day was modelled using two-level mixed models, with a random effect accounting for within-individual correlation  (Boschmann and Brady, 2013). Model selection was based on the Gaussian and the Negative Binomial distributions of the outcomes.
Acknowledgements The  CURHA  project ( Contrasting Urban Environments and Healthy Ageing: The International Longitudinal CURHA Study ) is an international study conducted in  Luxembourg  ( LISER -Luxembourg Institute of Socio-Economic Research) ,  Canada  ( CRCHUM -Centre de Recherche du Centre Hospitalier de l'Université de Montréal)  and  France  ( INSERM -Institut National de la Santé et de la Recherche Médicale). It is cofounded by  FNR (Fonds National de la Recherche ,  INTER/JCRA/12/ 6542889 ),  IRSC (Instituts de Recherche en Santé Canada   TEA-124969 ), and  FRSQ (Fonds de Recherche du Québec , # 28837 ). YK holds a  CIHR   Chair in Urban Interventions   and Population Health. We are grateful to the people from the  Survey Team at LISER.
25  This random survey of the size of libraries in the pre-industrial age is far from complete and represents mainly collections of the ruling elites, since we do not have sufficient data for other social groups. Even if some of the figures may not be wholly reliable and the relationship between codicological and textual units is not always clear,26 the results point to a threshold of thirty or forty thousand units for a large library, which is rarely exceeded, and if so, only in imperial or royal collections usually consisting of more than one repository. This number did not change substantially even after the advent of print and the spread of the hall library in Western Europe: the Herzog August Bibliothek in Wolfenbüttel housed 36,000 volumes at the end of the seventeenth century, most of them prints.
Book collections and organizing knowledge A few sources inform us that even before the late Middle Ages, the royal household was in possession of books owned by both male and female members of the royal family. 10 As the topic of this volume is libraries in the East and the West, it is 7  Stratford 1987, 329-350. 8  On the existing manuscripts, see the database europeanaregia. eu/en/historical-collections/ library-charles-v-family (accessed on 30 October 2021). 9 For the general history of the Louvre library, see  Kopp 2016a; Delisle 1907; Potin 1999, 25-36; Avril/Lafaurie 1968. 10 Kopp 2016a, 38-46; Avril/Lafaurie 1968. relevant to highlight one earlier royal book collection in Paris, from the mid-thirteenth century, which was said to have been inspired by the East. The royal biographer Geoffrey of Beaulieu notes that during the crusades in the Near East, Louis IX heard about the Muslim sultan in Egypt possessing a huge library open to philosophers.
Acknowledgements This volume is part of the results of the ERC project  670628   SICLE  ( Saadian Intellectual and Cultural Life ,  2016-2021 ) under the  European Union Funding for Research and Innovation  ( Horizon 2020 ). We would like to thank  Kristen de Joseph  and  Philip Saunders  for turning the text into correct English;  Luca Farina  for his careful treatment of tables, images and texts; and last, but not least,  Caroline Macé  for her attentive readings of the texts and boundless patience throughout the long editing process of this book.