dataesr/scientific-paragraphs-categorization huggingface dataset:
| DCE-MRI analysis Each DCE-MRI acquisition was examined by an experienced observer (C. C. , 10 years of experience in analyzing vascular images). The cardiac cycle phase was chosen as one where the inner and outer wall were clearly visible in all dynamic frames, with none, or negligible flow artifacts due to poor blood suppression. For this cardiac phase all temporal dynamic frames were traced using Osirix (https://www. osirix-viewer. com). Region-ofinterest coordinates from tracings performed in Osirix were processed using in-house Matlab software to extract dynamic curves. Signal intensity was converted to pseudo-concentration values using the validated linear relationship C t ð Þ / SI t ð ÞÀSI 0 ð Þ SI 0 ð Þ , where C(t) is the contrast agent concentration at time t, SI(t) is the signal intensity at time t, and SI(0) is the baseline signal before contrast agent injection.
12 Aortic sections were digitized using a Panoramic 250 FLASH II digital scanner (3DHISTECH; https://www. 3dhistech. com/), at 20 x magnification. After selection of the inner and outer contours of the aortic root using ImageJ (https://imagej. nih. gov/ij/), macrophage-rich areas were identified in digitized slides using a semiautomated thresholding procedure developed with in-house Matlab software. 23 Macrophage density was calculated as the ratio between macrophage-rich areas and vessel wall area and was expressed as a percentage.
D. 2. Online experiment As mentioned in Section 3. 2, the online experiment can be considered as a rather demanding robustness check of the lab experiment for multiple reasons: different subject pool, less control and asynchronous decisions. One of our main concerns was to get high quality data. We took three main measures to achieve high data quality. First, our financial incentives are quite large by Prolific standards. They imply average hourly earnings of £19. 61, compared to the recommended £7. 50 ($9. 60) and the minimum required payment of £5 ($6. 50). Second, we screened out inattentive subjects through attention checks in both parts of the study. In total, 6% of subject were screened out in the attention check in the type elicitation experiment and 8% of in the dictator game experiment.
Forget, pers. obs). Larger birds swallow these seeds, such as the guan (Penelope marail). The gizzard of this species is only slightly muscular, allowing the seed to pass through the whole digestive tract without being crushed  74, 75. Conclusion on toucans need to be tempered and could be explained by a possible site effect. Indeed, Mata Atlantica data were used to describe seed size consumed by toucans. However, seeds from Mata Atlantica are possibly smaller than in French Guiana, which could bias our results. This highlights the clear need for studies to evaluate their role in seed dispersal compensation. This study points out the importance of smaller vertebrates for seed dispersal and explores a new approach for studying arboreal frugivores with camera traps. Beyond the difficulty of finding the most suitable location for setting up cameras to maximise the chance of capturing animals, implementing this methodology involves very tedious work that requires time, climbing skills, specific equipment to access the upper branches in the tree crown, and the presence of at least two people capable of climbing to ensure safety  28.
Following the protocol of Niedballa et al. (2016)  81  , data (images) were first extracted and organised using the camtrapR® package on Rstudio® Version 1. 1. 463. Digikam® software tagged images displaying an animal to add the identified species name to the image's metadata. In some cases, the individuals photographed did not allow for species identification, so we tagged the pictures with the common name of the animal observed (e. g. , sloth, large marsupial, large rodent). Photographs that did not identify a particular animal (blurred image, only part of the organism photographed) were classified as unidentified species. Each identification was then used to determine the independent events obtained. An event is defined by recording an individual by a camera trap. It is considered independent of another at least 30 min apart, as is usually expressed in literature  [51] [52] [53] [54].
To avoid pseudo-replicates, counting events was performed by considering traps in the same tree as a single trap. If an individual of a species is recorded by two camera traps of the same tree within a 30-min interval, then only one event is counted. Only one event is counted if more than one individual is observed in the image. Because not all trees had the same number of camera traps, we analysed only the pictures collected by two traps on each tree to ensure consistent sampling effort among the 12 sites. The two traps that provided the most images were chosen to select them. To standardise the period of data recording on each tree, only images obtained between 01/12/19 starting at 18:00 and 24/01/20 until 06:00 were analysed, a period of 55 days. The overlap® package  82  was used to produce activity plots of the main frugivores.
Graphs were centred either at noon or midnight depending on their activity (e. g. , kinkajou is a nocturnal frugivore, so its figure is centred on midnight) to stay consistent with their behaviour. Seed size analysis. The average length and width of seeds consumed and dispersed by neotropical frugivores were compiled from studies conducted in French Guiana. The major frugivore species were selected even though we did not observe them in this study. Almost no data existed for toucans in French Guiana. However, the species of toucans from Mata Atlantica are very similar to those from French Guiana because they are either the same species (e. g. , Ramphastos vitellinus) or species with comparable sizes and body mass (e. g. , Selenidera maculirostris (160 g) in Mata Atlantica vs Selenidera piperivora (150 g) the Guianas).
Therefore, we used a dataset for the group Ramphastidae from Mata Atlantica  83, 84  to compare seed size among frugivores visiting Virola trees. Data for Ramphastos vitellinus, Pteroglossus aracari, and Selenidera maculirostris were selected and combined to form the toucans' group. As normality and homoscedasticity were not verified (tested with Shapiro and Levene test), testing for differences in average seed size consumed by the 9 main arboreal frugivores was done using.
Acknowledgements We thank  Labex DRIIHM-OHM Oyapock, Fondation pour la Recherche pour la Biodiversité , UMR 7179  CNRS-MNHN, Association CAFOTROP, Hévéa, Petzl foundation  and the  Society Les Amis du Muséum  for the Financial support. We thank  Drs Tremaine Gregory ,  Jennifer Moore ,  Roland Kays , and two anonymous reviewers for their helpful and constructive comments on the manuscript. We thank all helpers on fieldwork that made this study possible.
Data analyses A multilevel analysis approach allowed exploring relationships between study variables. Multilevel models extend multiple regressions to nested (hierarchically structured) data  (Vacher et al. , 2017). Considering hierarchical structure of the data (Level 1: individuals; Level 2: training groups) allowed unbiased estimates of the parameters  (Singer and Willett, 2003). All the analyses were computed using the lme4 package of R. Firstly, the intra-class correlations were examined in computing the null models for the three dimensions of athlete burnout at T2 (i. e. , dependent variables of the present study). Secondly, we ran a series of multilevel models in which burnout symptoms at T2 were regressed onto levels 1 and 2 coping and recovery-stress states controlling for levels 1 and 2 burnout symptoms at T1.
Mean Standard deviation  noteworthy that T1 level 1 and 2 athlete burnout were controlled in the prediction of T2 athlete burnout. Another explanation refers to the timing of data gathering of athlete burnout (3 months between the two times). Because athlete burnout is considered to be an enduring phenomenon, substantial time could be needed to note changes  (Martinent et al. , 2014b). Thus, the present study could be replicated across the entire competitive season. Common method bias might have distorted the findings as all the study variables were measured using a single source of data (self-report questionnaires). Future research should complement self-report questionnaires with objective (e. g. , performance data) or physiological indicators of overtraining (e. g. , heart rate variability).
leefi lters. com). Different light intensities were achieved by varying lamp brightness settings and adding neutral density filters (0. 3 ND, 0. 6 ND and 0. 9 ND; LEE Filters). The absolute irradiance of downwelling light in the tanks were measured using a 1000 μm optic fibre connected to a Jaz spectrometer and SpectraSuite software (Ocean Optics) (Figure  1 ).
| Transcriptomic assessment of the opsin gene expression repertoire One transcriptome per time point per condition was sequenced for juveniles (N = 11; accession numbers provided in Table  S2 ) to verify species identity, confirm the opsin gene expression repertoire, and extract housekeeping gene sequences. Since these transcriptomes were solely used to establish and validate the methodology and no attempt to draw conclusions about differential gene expression was made, a single individual from each condition was sufficient. Firstly, retinal tissue preserved in RNAlater was digested using Proteinase K (New England Biolabs), total RNA was isolated using the Monarch Total RNA Miniprep Kit (NEB) and genomic DNA was removed using RNase-free DNase (NEB). Quality and yield of RNA was assessed using the eukaryotic total RNA 6000 Nano kit on the Queensland Brain Institute's Bioanalyser 2100 (Agilent technologies).
8. 4) using the genomics virtual laboratory on the Galaxy platform (usega laxy. org; [Afgan et al. , 2018] ). Species-specific cytochrome C oxidase subunit I (COI) were downloaded from GenBank (https://www. ncbi. nlm. nih. gov/genba nk/). Opsin gene coding sequences (CDS) for A. triostegus were provided by Fabio  Cortesi et al. (2015 Cortesi et al. ( , 2018) )  and their identity was confirmed by BLASTn (NCBI, Bethesda, MD, https://blast. ncbi. nlm. nih. gov/Blast. cgi) and comparison to the published sequences of a close relative, the spotted unicornfish, Naso brevirostris  (Tettamanti et al. , 2019). All gene extractions and expression analyses were conducted in Geneious Prime version 2021. 1. 1 (Biomatters Ltd). The species of origin of each retinal transcriptome was confirmed by mapping trinity assembled transcripts back onto the A.
, 2019). Housekeeping gene extractions were performed by mapping filtered paired reads to published CDSs of Oryzias latipes (actb; S74868) or Amphiprion ocellaris (elf1a; XM_023263215) with medium sensitivity settings. Matching reads were connected by following single nucleotide polymorphisms (SNPs) across genes with continual visual inspection for ambiguity and were extracted as paired mates to mitigate sequence gaps. The consensus of an assembly of these extracted reads was used as the reference for low sensitivity (high accuracy, 100% identity threshold) mapping. Partial CDS extractions were cyclically mapped using the low sensitivity approach to prolong and subsequently remap reads until a complete CDS was obtained (accession numbers provided in Table  S2 ). To confirm the identity of each gene, full coding sequences were checked using BLASTn.
| Statistical analysis Statistical analyses were conducted using the GraphPad Prism software version 8. 3. 1 (www. graph pad. com). Since normality tests have very little power to detect the distribution (i. e. , Gaussian or non-Gaussian) of data sets with small sample sizes  (Ghasemi & Zahediasl, 2012) , a more conservative approach to statistical analysis was employed. Specifically, all data sets were assumed to have non-Gaussian distributions and thus, all tests were nonparametric. For comparisons of two groups, data were analysed with two-tailed Mann Whitney tests. For comparisons of more than two groups, data were analysed with a Kruskal-Wallis test and a Dunn's multiple comparisons test. Statistical significance was considered as p <. 05. Graphs throughout the study were generated using GraphPad Prism.
DATA AVA I L A B I L I T Y S TAT E M E N T Newly identified sequenced and sequenced transcriptomes are available through GenBank and the SRA archive (see Table  S2  for accession numbers). All other data are available through the UQ eSpace at https://doi. org/10. 48610/ aafb371 (Fogg & de Busserolles, 2022)    or are provided in the main manuscript or Supporting Information.
Study Design The C3-Cloud study used a mixed method research design to gain insights into the usability, acceptance, and usefulness of the C3-Cloud system. The project has developed an innovative care planning system called "C3-Cloud," which was tested with patients, their informal caregivers, and health care professionals in the United Kingdom (South Warwickshire), Sweden (Region Jämtland Härjedalen), and Spain (Basque Country). The tests and evaluation activities generated data to assess the usability and usefulness of the C3-Cloud system as well as its acceptance and satisfaction among user groups. The study was designed to go through 4 evaluation phases. The adoption of phases corresponded to the study's aims to develop the C3-Cloud system together with its users in an iterative approach of testing, feedback, and subsequent improvements, which is in line with the UK's Medical Research Council (MRC) recommendations for carrying out complex interventions.
Study Development and Timeline After the initial evaluations and trials, the C3-Cloud system has been deployed at the pilot sites, which was followed by pilot phase 3 with a larger number of users compared with phases 1 and 2. The project aimed for a 6-month exploratory technology trial (phase 3), followed by a few months for system acceptance analysis and impact assessment of C3-Cloud in phase 4 (Figure  1 ). All test participants' data as well as patient and clinician data were retrieved anonymously or anonymized and aggregated in the pilot sites before sharing the data sets for analysis. Control group data for the period of phase 4 were extracted from care centers in the pilot sites in April 2020. The anonymously retrieved information was on health care resource consumption. To ensure that data cannot be traced, the data extracts did not include demographic descriptors and identifiers.
Study Setting A technology trial was used to test the C3-Cloud system with MDT members, patients, and their informal caregivers (when available). The technology trial ran for 6 months and took place in 3 European pilot site regions: Basque Country, Spain; Jämtland Härjedalen, Sweden; and South Warwickshire, United Kingdom. Study settings included various locations that are relevant for the provision of health care, for example, health care centers, general practitioner offices, hospitals, and patients' homes. The technology trial is registered at ClinicalTrials. gov (identifier: NCT03834207).
It was anticipated that several patients that were approached for participation would decline from the outset. Accordingly, the number of patients that were approached for participation (ie, the "approaching goal") was 16% larger than the recruitment goal, summing up to 610 intervention patients across the 3 pilot sites. The number of comparator patients whose resource consumption data were monitored anonymously matched the intervention patient numbers at each pilot site. Potential candidates were selected through each pilot site screening their databases for eligible patients who met the inclusion and exclusion criteria (Multimedia Appendices 4 and 5). No inclusion criteria for informal caregivers have been defined; however, exclusion criteria will be applied (Multimedia Appendix 4). Once the pilot sites have provided a list of eligible patients, they were randomized as study candidates to avoid selection bias.
Study Procedure Early in the pilot technology trial a training was offered for all participants on how to use the platforms. The pilot technology trial was used to evaluate the user experience, satisfaction, and acceptability of the C3-Cloud application as well as the patient training material (phase 3). It also served to obtain anonymous patient data on resource usage for impact modeling and sustainability planning for upscaling C3-Cloud in phase 4. At the start of the trial, the patients had a care plan created on the C3-Cloud system that they developed and managed with their health care professionals during the study. Once the patient's care plan was prepared, they were given access to the C3-Cloud system to view and update their care plan whenever they wished. Moreover, patients were able to send messages to their care team members via the system.
The patients care plan in the C3-Cloud system was reviewed and adapted each time they visited a health care professional who was also taking part in the technology trial. In the final phase of the project a comparison was made on the care and treatment received by patients that have used the system and those that have not (the comparator patient group). The comparator group data were taken from similar patients and retrieved anonymously from the local health care systems. Initial screening showed that a sufficient number of similar patients was available for data retrieval in the systems. These data contributed to determining the full impact of C3-Cloud by assessing the use of health care resources and medication prescription across both groups of patients (phase 4).
Overview This phase evaluated the user experience, satisfaction, and acceptance of the C3-Cloud application and patient training material by collecting evaluation data. Data were collected from a subset of participants (150 patients and 52 MDT members) from questionnaires they completed. Data on user experience and satisfaction were collected from the training material questionnaire and the validated Questionnaire on User Interaction Satisfaction (QUIS). The data collected on acceptability of the technology were obtained through a refined version of the validated Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire. The questionnaires were administered as an online survey a few weeks after the trial start and at the trial end. Trial participants were able to obtain the link to the online survey via the messaging service of the platforms, which ensured that no participant was contacted by the evaluation team directly, thereby avoiding confidentiality breaches.
The Unified Theory of Acceptance and Use of Technology Questionnaire A C3-Cloud-adapted version of the UTAUT questionnaire  [15]  was used, covering some of the original UTAUT modules. The UTAUT is developed to predict individual adoption and use of new information technologies (ITs). It posits that individuals' behavioral intention to use an IT is determined by 2 beliefs: perceived usefulness, which is defined as the extent to which a person believes that using an IT will enhance his or her job performance, and perceived ease of use, which is defined as the degree to which a person believes that using an IT will be free of effort. A shortened version of UTAUT was administered at the beginning of the pilot trial, just after participants have had training sessions on how to use the C3-Cloud components.
Overview In Phase 4 modeling for large-scale impact of C3-cloud implementation after the technology trial was performed. The health and economic benefits of the intervention at the population level were evaluated to gain insights into savings that C3-Cloud could generate systemically in the long term. The digitalization of clinical patient histories and the coding of all contacts between patients and their MDTs into the EHR allowed to better understand the health demand of a population and to quantify the health and social burden of the disease. For this quantification, health care resource usage data of all patients were used and compared using modeling techniques with anonymous comparator patient data. The modeling tool used for this analysis has been developed by merging discrete event simulation modeling methods with a cost-benefit assessment tool  [16].
The conceptual model included not only the health system but also the complete set of stakeholders. Model parameterization was a challenge as data required for all stakeholders could not be obtained from evidence-based sources. The data focused on health care resource utilization, frequency of use of C3-Cloud components, and service satisfaction. The data needed for this type of modeling were taken from administering additional questionnaires to participants: the eCare Client Impact Survey (eCCIS) for patients; the eCare User Impact Survey (eCUIS) for MDT members; and a few additional questionnaire items for patients, MDT members, and informal care givers. In addition, C3-Cloud log files and EHR exports were taken from EHRs of the intervention and comparator patients to evaluate the differences in health care resource utilization during the trial.
Results The pilot testing (phase 3) was carried out from November 2019 to April 2020. Difficulties were experienced in recruiting the envisaged number of trial participants, specifically with the intervention patients (Figure  2 ). The technology trial protocol was submitted in several revisions to the 3 regional ethics committees in reflection of updates regarding project information that would be communicated to trial participants, the methods of accessing and using control group patient data, the recruitment procedure of patients, trial participants training, or adaptations to some questionnaires. Data collection was completed in April 2020 with a total of 230 patients and 125 MDT members. The evaluation results of the pilot technology trial were analyzed and reported in a project report in June 2020  [21].
Acknowledgments Beyond the authors who contributed to the research protocol, we send heartfelt thanks to the whole C3-Cloud research team and the contributing authors of project deliverables D9. 1, D9. 2, D9. 3, and  D9. 4. From INSERM:  Damien Leprovost ,  Ariane Assele-Kama ,  Mikael Dusenne ,  Rosy Tsopra ,  Eric Sadou ,  Eugénia Lamas ,  Laurent Toubiana ; from empirica:  Veli Stroetmann ,  Jess Vogt ,  Reinhard Hammerschmidt ,  Daniel Schmidtmann ; from  Region Jämtland Härjedalen :  Marie Sherman ,  Anna Eriksson ,  Karin Gärd ; from South Warwickshire:  Lei Zhao ;  George Despotou ; from  Software Research & Development Consultancy (SRDC) :  Bünyamin Sarıgül ,  Alper Teoman ; from  Osakidetza: Antonio de Blas ,  Nicolás González ; from Kronikgune:  Ane Fullaondo ; from  South Warwickshire NHS Foundation Trust: Phil Goode ; from Cambio:  Goran Ekestubbe ,  Eneimi Allwell-Brown ,  Mattias Fendukly ,  Manuel Palacio ,  Rong Chen ; from EuroRec:  Robert Vander Stichele ,  Geert Thienpont , Pascal Coorevits.
Auxiliary data The auxiliary data consist of 1) GEDI Level 2A products; 2) the forest mask from the French National Institute of Geographic and Forest Information (IGN) (BDForêt® V2, https://geoservices. ign. fr/bdforet); and 3) a digital terrain model (DTM, BD ALTI® 25 m, https://geoservices. ign. fr/bdalti) over the study area. GEDI Level 2A product provides footprint information of multiple layers, including beam types, sensitivity, geo-located elevation and height metrics. Both full-power and coverage footprints were used in this study. Height metrics include standard relative height (RH) percentiles from 0 to 100 (e. g. , percentiles 95 and 100 are RH 95 and RH 100 ). According to the literature, multiple GEDI RHs can be used as an assessment of forest height  (Duncanson et al. , 2022).