dataesr/scientific-paragraphs-categorization huggingface dataset:
In the 'specific' dataset, most persons depicted on coins are identifiable by name, especially in the later periods. Treating them as individual motif types, separate from the generic, unnamed persons, adds to the diversity of motifs composing the designs. This contributes to the increase of the basic entropy of designs (see Figure  3 ), making them less predictable. Additional design types in the specific coding affect the mapping between designs and authorities or denominations (frequencies of unique denominations and authorities being constant for each of the coding alternatives), inflating the estimates on the amount of information in coin designs. This is why the 'specific' designs are overall more informative (indicated by lower conditional entropy values in the final periods) than the 'general' ones, especially about the issuing authorities (see Figure  1 ).
Conclusion The last decade has witnessed a growing number of digital resources, advancements in computer-assisted approaches, and the development of standards for annotating cultural data. This opened new possibilities for management and presentation of cultural heritage. Standardized databases present a way to organize the data, making it more reliable and available for research, suitable for testing different questions using a variety of approaches. However, many challenges remain, especially concerning the digitization of collections of material culture, where finding a generally applicable standard to describe graphic representations seems to be particularly difficult. In this article, we focused on coins, objects marked with graphic designs, produced in large quantities and disseminated mostly by political authorities, serving primarily as media of exchange.
Their official character, together with a substantial number of examples preserved since antiquity, makes them particularly suitable for standardization. In the field of ancient history, numismatics has been one of the early adopters of linked open data principles in developing standards for organizing digital collections and making them available to wider audiences. However, the focus of these standards is still on the technical properties of the coins, their provenance, and value. Although reliable online catalogues listing the types of coin images are available for some areas of numismatics, no general standard exists for representing coin iconography. This lack of standardized descriptions hinders the possibilities for studying large diachronic patterns of structure, evolution, and transmission of images using quantitative methods.
Generalized coding could thus be regarded as a necessary first step towards creating reliable and generally applicable standards for iconographic descriptions. Current attempts at creating standards for annotating coin images, based on semantic ontologies, strive to offer a certain amount of flexibility, while not risking losing any data. This approach is certainly appropriate for general databases intended for research purposes, provided the detailed concepts are wellaccepted. Otherwise, an overly specific standard might be less interoperable. Ideally, standardized iconographic descriptions should be based on a minimally extensive list of well-defined concepts, supplemented with references to the relevant literature. Achieving this basic level of standardization for the majority of currently available digital collections would enhance the potential of this rich data in developing quantitative approaches for studying images, adding to our understanding of the ancient world.
Patients were excluded if they had received involuntary treatment with PP3M or were switched to PP3M during a clinical trial. Patients were evaluable if they, and at least one other associated stakeholder, completed the questionnaire. Questionnaires were completed separately by patients and their physician, nurse and/or carer at a single data collection point. After receiving guidance on questionnaire completion, the questionnaire was completed by patients at the study site during a routine clinic visit and by carers at the same visit or within 2 weeks after the patient's visit. Physician questionnaires included an assessment of the patient's level of illness for correlation with questionnaire responses. Level of illness was assessed using the Clinical Global Impression-Severity (CGI-S) scale and the Positive and Negative Syndrome Scale (PANSS) item G12 (lack of judgement and insight).
Patients reported their level of illness using the European Quality of Life-5 dimension-5 level (EQ-5D-5L) questionnaire (patient-reported general health status), consisting of the EQ-5D-5L descriptive system and the EQ visual analogue scale  [15]. Data from each question were summarised separately for patients, physicians, nurses and carers; questionnaire items were presented descriptively using frequency and percentages for categorical data, as well as count, mean, standard deviation (SD) and minimum and maximum for ordinal responses. The following categories were used in the questionnaire: Impact on relationship/interaction between medical team and carer/patient: 13 items Involvement in treatment decision: two items Reasons for selecting PP3M: two items Impact on patient: ve items Impact on carer: eight items.
Patient characteristics In total, 228 patients were enrolled, of whom 227 were considered eligible for study participation. The evaluable cohort for analysis consisted of 224 patients who had a patient-completed questionnaire item along with an item completed by at least one other respondent (physician, nurse or carer). Three patients were not included in the analysis either due to missing patient data (n = 2) or to lack of other stakeholder data (n = 1). Patient demographics and disease and concomitant treatment characteristics are presented in Table  1  and Fig. 1. Overall wellbeing of patients was high, and patients' lack of insight was mild to moderate. Patients had been treated with PP1M for a mean (SD) of 23. 9 (21. 28) months before switching to PP3M treatment. Patients had received PP3M for a mean (SD) of 12.
Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request, subject to possible IP, privacy, regulatory and/or other constraints. An interim analysis of the PINC-Q study was presented at European College of Neuropsychopharmacology 2019 (Lahaye M et al. Presented at ECNP 2019, Copenhagen, 7-10 September 2019. Poster 391). A summary of the full analysis was presented as a poster at European Psychiatric Association (EPA) 2020.
Acknowledgments: I would like to thank prof. G. Radi  and prof. C. Tozzi  for having welcomed me and granted me access to the collections of the  University of Pisa. Also, my thanks to ex- Curator P. Simon  for granting me access to the  Museum of Monaco. I would like to thank  I. Rodet-Belarbi  for helping me in the identification of bones,  P. Anderson  who helped me in SEM analysis. I would also like to thank  Mr. R. Vargiolu  who analyzed my facsimile at the  laboratory of Tribology of the Ecole Centrale de Lyon.
Design and setting This was a two-arm parallel-group randomized controlled trial of research manuscripts submitted for publication to BMJ Open, a general medical journal. All research manuscripts consecutively submitted between June 1, 2019, and October 9, 2019, to BMJ Open, and sent for peer review, were screened for eligibility until the planned sample size was achieved. Research manuscripts sent for peer review were randomly allocated to the intervention or control group. An investigator (MG) indicated the manuscripts that were were assigned to the intervention group using a flag and an additional note in the journal's electronic tracking system (ScholarOne). This informed the handling editors of the manuscripts that needed to include additional instructions in the decision emails sent to authors alongside the peer reviewers' reports.
, Recommendations for clinical practice not justified by study findings (e. g. , calls for action that have not been evaluated). , consistency of coding, we conducted a pilot of 10 manuscripts prior to the start of the study. The agreement for the pilot between the two investigators was 80% or higher for each of the four spin items. The level of agreement between the two investigators (BY, ML) for scoring the primary and secondary outcomes are outlined in Appendix C. All manuscript versions were available on ScholarOne. MG provided BY and ML the de-identified abstracts of the submitted manuscripts and the de-identified abstracts and full texts of the revised manuscripts following peer review. After data extraction, BY and ML discussed all discrepancies to obtain consensus.
, 2015) ), providing new tools to improve charcoal kiln inventory  (Bonhage et al. , 2021; Trier et al. , 2021). Here, we present a case study using an innovative approach of inventory of charcoal kilns that combines for the first time both on-screen and algorithm-based detection complemented with field validation on a large area, to assess and explain the spatial distribution of kilns related to socio-environmental territorial attributes. Two detection methods were applied and compared: 1) an on-screen detection and 2) an object detection method, based on the analysis of a DEM. Moreover, it was developed an efficient and easily transferable charcoal kiln detection methodology to another dataset, using standard libraries and Application Programming Interfaces (API) such as TensorFlow  (Abadi et al.
LiDAR data acquisition The acquisition of LiDAR data was performed in two campaigns in The acquisition was done when broadleaved trees had shed their leaves to limit the influence of the tree canopy on the quality of the data. Individual images (1000 x 1000 pixels, 500 x 500 m) were produced, with a resolution of 4. 5 points. m -  and an altitudinal precision of 8 cm, resulting in the Digital Elevation Model (DEM; pixel side: 50 cm) used in this study. From the DEM, slope-derived images (using the "Slope" tool in the "Raster terrain analysis" processing toolbox in QGIS 3. 10) were also produced to help the on-screen inventory  ( Štular et al. , 2012). When undisturbed, kilns have a characteristic outline  (Bonhage et al. , 2020; Hirsch et al. , 2020; Ludemman and Nelle, 2002; Schneider et al.
Automatic detection inventory For the automatic detection, TensorFlow  (Abadi et al. , 2016)  Object Detection API, a widely used API, with several available pre-trained models was chosen. Among the available architectures, a Faster R-CNN with a Resnet 50 backbone was selected, because it is one of the smallest of the available models that have proven to be efficient at detecting small objects  (Nguyen et al. , 2020). Small object detection is a known limitation of object-detection architectures. Indeed, the size of a kiln is on the order of magnitude of 40 pixels depending on the DEM resolution, which is considered a small object for an object-detection task. The initial model was trained on the Ms COCO Dataset  (Lin et al. , 2014)  and fine-tuned on the on-screen inventory. The initial model being trained on RGB images, we first had to transform our DEM into RGB images.
For this, the DEM was split with a 300 × 300 pixels sliding window, with a stride of 100 to perform data augmentation. The resulting float images were converted into RGB images with a jet colormap encoding to minimize loss of information. Then, the geographic coordinates of the on-screen detected kilns were converted into pixel coordinates in those images, and a 40 × 40 pixels bounding-box around those points was labelled as a kiln. Finally, the dataset obtained was composed of 2828 images and 5824 bounding-boxes. Then, the dataset was split into three: training set, evaluation set, and test set. The splitting was not random because of the data augmentation technique used: a same kiln would have appeared in different sets if the images were randomly sampled. Several parameters were tested to fine-tune the detector.
The best performing ones were selected based on its results on the evaluation set, on COCO metrics. Those metrics are the standard when comparing object detectors performances. The parameters selected were as follows: the training was done on 60,000 steps, with a momentum optimizer and an initial learning rate of 3e-04, dropped every 10,000 steps by factor of 10. Since the bounding boxes in the dataset have a fixed size of 40 × 40 pixels, this same size was used in the Region Proposal Network: the grid anchor generator parameters for height and width were 40 and the first stage features stride of the extractor was eight. The following data augmentation options were selected: random horizontal flip, random vertical flip and random rotation of 90 degrees The original images created were not resized during training.
Analysis of kilns inventories and spatial distribution Analysis were done to compare the two methods of kiln detection to field prospection in a selected sector of the study area where both methods were available. In addition to computing the error rate of each method, we also sought a relationship between topographical variables and the efficiency of the methods according to six possibilities ("field detected", "false positive on OSD", "false positive on AD", "OSD validation", "AD validation" or "OSD&AD validation"). This dataset was analyzed by a partial least square discriminant analysis (plsda, MixOmics package,  (Rohart et al. , 2017) ). Discriminant analysis is a supervised method that aims to separate individuals according to a qualitative variable, and to identify the independent variables that drive this separation.
Between-group differences were then assessed with a permutation test  (Westerhuis et al. , 2008). Then, an analysis was performed to assess the spatial distribution patterns of the inventoried kilns. These variables were socio- environmental territorial attributes selected among available datasets for the study area and from the GIS datasets (with Zonal Statistics tool in QGIS 3. 10; Table  1 ). Among the available datasets, these variables were selected because they appeared relevant to explain variations in charcoal kilns' distribution. Three grid datasets (of 125, 250 and 500 m side) were generated to explore commonalities between charcoal kiln distributions. To identify the spatial distribution patterns of kilns according to the selected socioenvironmental variables, a multiple correspondence analysis (MCA) was performed with the ade4 package  (Dray & Dufour, 2007)  in RStudio  (R Core Team, 2000; RStudio Team, 2016).
The test area where automatic detection was performed did not cover all the categories of contemporary forest cover dominant groups (five out of nine categories present). Broad-leaved and Fagus forest formations dominated the detection numbers in terms of both area and features detected. The MCA computed for this area failed to identify specific relationships between number of kilns and the socio-environmental variables, since the ellipses for the five modalities of this variable were superimposed. Considering the three grid sizes analyzed, the variance/mean ratio was higher than one for each class of mean slope, mean elevation and elevation standard deviation, for OSD as well as AD, therefore, a strong aggregative distribution of kilns within the prospected area can be surmised. Focusing on the 250 m grid size with the OSD dataset (the largest one), trends could be identified, with a higher aggregation in low elevation, high slope and high standard deviation in elevation.
AD algorithm performance The results of the automatic detection algorithm on the test dataset 89. 69% for the Recall and 84. 47% for the Precision. The algorithm was thus able to detect nearly 90% of the charcoal kilns in a given area. In 15% of the cases, the features detected were not kilns. Table  3  Summary of the potential charcoal kilns validated by sector (sector 1: validation in after on-screen detection only; sector 2: validation in a flat area; sector 3: validation after on-screen and automatic detection).
Kiln inventory The spatial distribution of charcoal kilns detected by either OSD or AD showed strong aggregation. The aggregation measured on the basis of OSD dataset, the most consistent one, showed a trend towards a higher aggregation at low elevation, i. e. , valleys, suggesting that the available space for charcoal kilns was reduced in these areas since these valleys areas are occupied by roads, paths and rivers. However, the lower aggregated pattern observed at high elevation and in smooth areas points to the difficulty in detecting charcoal kilns in such topographical features and must be taken with caution. In the flat area, the four potential charcoal kilns detected, proved to be false positives, and the newly-detected kilns in the field were not perceivable in the images. This indicates that bias was caused by methodology and not by preferences of the people when choosing the area to build a charcoal kiln or topographical constraints  (Krebs et al.
Moreover, the validation and prospection in the field in such areas can be made more difficult due to the presence of vegetation covering the charcoal kiln (e. g. , Hedera helix as observed in the area) or densely-wooded areas where access is very limited or virtually impossible. The occurrence of false positives derived from the OSD in the sector 1 is, surely, overestimated due to the factors described above. The AD method led to lower number of charcoal kilns detected and a lower frequency and density in flat areas compared to steep areas. The automatic detection rate needs to be improved to avoid biases caused insufficient data (i. e. , the referential collection of ground truth data) to cover all the specificities of the charcoal kiln outlines in these areas. The quality of the template dataset provided to the algorithm to perform the AD is determinant to produce confident and reliable detections.
A key step to achieve the most accurate and reliable algorithm detection is also to provide the largest possible collection of kilns contours to cope with the variability of these features  (Bonhage et al. , 2020; Hirsch et al. , 2020). The use of template databases supported by OSD can lead to misidentification or incorrect labeling of charcoal kilns. Field validation of the detections, both by OSD and AD must be routinely performed to increase the Precision and the Recall of the detections. Field validation of the on-screen and automatically detected charcoal kilns is an important step in the overall development of an automated methodology. Both methodologies missed the detection of charcoal platforms, and the false positives also occurred. Validation of on-screen detected kilns revealed that this method was highly precise but missed several charcoal kilns.
The precision reached 98. 4% (only one false positive verified) but the recall was only 63. 9% suggesting that the expert was conservative in terms of which kind of landforms could potentially be kilns and only marked them when the level of confidence was high. The performance of the AD algorithm must be looked at closely. The presence of false positives (15%) can be connected, to some extent, to labeling errors on the training dataset, and other natural or manmade structures misidentified as charcoal kilns outlines. After validation in the field, the Recall, i. e. , how good the network is at finding charcoal kilns reached almost 90%, similar to other works such as  Toumazet et al. (2017)  in Central France where template matching methods were applied. Nevertheless, in the study cited, the precision was lower than the one achieved by the approach in this study (c.
Distribution patterns Charcoal kiln detection and inventory are important steps in the assessment of the impacts and extent of former exploitation of forest resources  (Rutkiewicz et al. , 2019). It is important to improve methodologies to detect these past activities, and better estimate their quantity, density and distribution. Other structures than charcoal kilns can be detected and the number of kilns can be under-or overestimated due to several factors such as image illumination, the operator's experience in performing the OSD of images or a reference database that is not sufficiently vast to perform the AD. In this study, an area of more than 5700 ha was extensively and exhaustively examined by OSD to estimate charcoal kiln number as accurately as possible. The inventory resulting from OSD provided a density of 0.
Further perspectives The continuous acquisition of high-resolution LiDAR data presents an invaluable opportunity to explore and implement methodologies of detection and mapping on large scale. The automatic detection method used here is very flexible, allowing for reproducibility. Further perspectives after this study include the testing and evaluation of the algorithm performance in different areas and at different spatial scales with available high-resolution LiDAR data. We will also include more reliable field-validated data to increase the quality of the reference dataset used for object detection.
The approach allows the user to apply the method in an area of interest without needing to invest much time to acquire specific skills. To do so, it can be mentioned that it is not necessary to pre-process the images (a DEM is sufficient) or deal with data augmentation steps, since the network does it by default. The training dataset is enriched with both positive and negative examples (examples with or without kilns), leading to an increase in precision, and a more generalized method. To build on these promising results, the network can be refined in order to achieve a better performance. The development of easy and user-friendly methodologies increases our understanding of these historical activities such as charcoal production. Exhaustive charcoal kiln inventories are key to correctly gauge the extent of former historic activities impacts where most studies rely on local-scale studies.
Acknowledgments This work was supported partly by the  French PIA  project " Lorraine Université d'Excellence" , reference  ANR-15-IDEX-04-LUE. The authors thank  Philippe Wagner  and  Paul Massard  for their contribution during fieldwork. The authors are also thankful to  Jean-Pascal Franco  from the  Office National des Forêts , and the team of the  Observatoire Pérenne de L'Environnement.
Fieldwork and geodesy Coring was performed using an Atlas Copco Cobra TT vibracorer. Open steel auger heads (outer diameters: 6 and 5 cm, respectively) were used to probe the stratigraphy. In the field, the cores were sampled at constant intervals (10-15 cm). DGPS leveling (<2 cm resolution) was performed using a Topcon HIPER Pro device. The local coordinate system (LCS) applied in Enez and used in this study is based on UTM (zone 35N) using WGS84. Ellipsoidal heights were transformed to orthometric ones by applying the geoid model EGG97. The altitude correction of the LCS is undergirded according to the ITRF08 standard by processing the data with the web service of the Geodetic Survey Division, Canada Centre for Remote Sensing, Natural Resources Canada. Our sealevel measurements by DGPS, during fieldwork in 2013-2015, supported the LCS sea-level heights.