dataesr/scientific-paragraphs-categorization huggingface dataset:
The 2018 European General Data Protection Regulation (Le règlement général sur la protection des données -RGPD) severely restricts what types of data can be collected and how they can be stored. The RGPD forms part of the National Commission for Digital Rights and Freedoms (Commission Nationale de l'Informatique et des Libertés -CNIL 23 ). It is much more Kelleher and Bays Journal of Open Humanities Data DOI: 10. 5334/johd. 75 Open Science initiatives like TGIR Huma-Num, whose objective can be defined as "transparent and accessible knowledge that is shared and developed through collaborative networks" (Vicente- Extract 1 The TGIR Huma- Num Nakala data repository logo. 7 https://opidor. fr/ (last accessed: 16 March 2022). 8 https://www. scienceeurope. org/about-us/ (last accessed: 16 March 2022).
26 https://www. happyscribe. com/ (last accessed: 16 March 2022). 27 https://www. dublincore. org/specifications/dublin-core/dcmi-terms/ (last accessed: 16 March 2022). 28 See https://www. archivistes. org/ (last accessed: 16 March 2022). Kelleher and Bays Journal of Open Humanities Data DOI: 10. 5334/johd. 75.
Acknowledgements The  China Scholarship Council  is gratefully acknowledged for a predoctoral fellowship to  J. Wang. The work benefited from the facilities and expertise of the  Biophysical and Structural Chemistry platform at IECB ,  CNRS UAR3033 ,  INSERM US001 ,  Université de Bordeaux. We thank  B. Kauffmann  for assistance with crystallographic measurements and  F. Rosu  for assistance with MS measurements. Open Access funding enabled and organized by Projekt DEAL.
I. INTRODUCTION EMOTE sensing (RS) digital images are an essential part of big data. They are widely used in land cover classification  [1] , agriculture  [2] , ecology monitoring  [3] , roads extraction  [4] , and many other applications. Increased computing power allows applying various artificial intelligence algorithms to processing and analysis of RS-images  [5]. Moreover, an explosive hardware development provides huge RS-images databases (e. g. ,  [6] ). Also, modern sensors produce digital images of a very high resolution with a huge number of pixels  [7]. For this reason, memory, traffic and time expenses have increased significantly. In order to solve this problem, image compression methods are applied  [8, 9]. There are techniques and algorithms that can be used for compressing data of any type, whilst there exist methods that have been developed for compressing some specific data, in particular, images  [8, 9].
Hence, quantizing and encoding can be combined and executed in a single process. In other words, when applying a scan of matrices of DAT-coefficients and quantizing their elements, it is proposed to encode the integer values computed at once and put the obtained data to the DAC-file. Therefore, the need for the matrices QΩY, QΩCr and QΩCb is eliminated. Hence, it follows that instead of seven h×w matrices of floating-point values and three h×w matrices of integers, just four h×w matrices of floating-point values are required. Whereas, an output remains unchanged. The same modifications can be applied at decompression stage. Indeed, when decoding data, dequantization can be performed. i. e. the first two steps of decompression can be combined. As a result, the dequantized DAT-coefficients are obtained.
| -|; 3) root mean square error: = ∑ ( -) ; 4) peak signal-to-noise ratio: = 20 log ; where ( ,. , ) and ( ,. , ) are source and reconstructed data respectively. In this research, satellite images of European Space Agency are used  1. This test dataset consists of 100 high resolution images of a total size more than 5. 4 GB. We note that a presence of a lot of small objects and sharp changes of color intensity in combination with large areas of relatively constant color is an important feature of these data. Here, we use the same quality loss control mechanism as in  [20]. In order to compare DAC and its modified version, each test image is processed by these, actually, different algorithms with different loss of quality setting. The values of CR, MAD, RMSE and PSNR are computed in each case.
Tank water level and water table evolution Tank monitoring was performed for two climatically contrasted years  (2012) (2013). During the driest one (  2012 ), bathymetric measurements were made using a Trimble DGPS inside the tank (Figure  1 ). Surrounding topography was defined using MNT 50 m around it. Water level measurements were made using a CTD Diver pressure sensor with continuous record (15 min time step) fixed to a scale (accuracy 1 cm) implanted in a concrete block. Validity of the measurements was verified by direct scale reading once a week. Area of the tank was measured regularly by GPS traking using a GARMIN 60Cx. Daily rainfall was recorded from the rainfall station of the MRO (Mandal Revenue Office) of Maheshwaram and evaporation data from the ICRISAT meteorological station.
Remote sensing data analysis methodology Remote sensing is a common tool to quantify surface water extension (e. g. Feyisa et al. ,2014; Ji et al. , 2009; Klein et al. , 2014; Peña-Luque et al. , 2021; Vanthof & Kelly, 2021). Tank area evaluation from past years is performed using LANDSAT data. The LANDSAT satellite has a periodicity of 16 days, and Cloud free (0%) images and gap free (over the studied zone). A total of 26 images were gathered  The estimated tank area is further compared to the area measured using GPS tracking as explained in the section ("tank monitoring").
Minimum and maximum results of the computations within the range of parameters given in table 1 are presented in shaded grey for uncertainty evaluation. Despite uncertainties on the parameters the produced water balance shows limited variability except for three periods  (2003; 2006; 2010). The relatively strong variations during theses periods comes from a Long term monitoring of rainwater harvesting tanks threshold effect related to the change of the effective drainage area changing from local runoff to large runoff. However, the LANDSAT data (58 500 m 2 in 2003 and two points at 0 m 2 in 2007) helps selecting the best solution since only few parameters allow fitting these events. This selection was not possible using only the 2012-2013 data. Using this LANDSAT information, the range of acceptable parameters can be reduced to [CN: 78-79; q: 3.
, 2004: 63%)  and gives confidence on the general results of the water balance. Long term monitoring of rainwater harvesting tanks This yearly budgets shows that direct rainfall in the tank remains limited and is consistently lower than the evaporation. The main control of the tanks filling is the runoff. Over the studied period, 6 extreme raining events (>50 000 m 3 ) contribute to most of the tank filling. The last documented event in the time series (  2013 ) caused large floods in the nearby Hyderabad city. Runoff days and dry days are presented figure 10. On average, 18. 5 days of runoff per year occurs, with a maximum of 29 days in 2010 and a minimum of 10 days in 2011 (2014 was not taken in account as the year was not complete in the dataset). However, the runoff intensity can vary significantly.
Methodological improvement The methods and concepts developed in this paper are relatively simple. However it provides, by the conjoint use of direct and precise field data with remote sensing over a long period of time, a clear enhancement of understanding the impact of RWH and MAR structures. Indeed, although various studies address this issue through remote sensing and GIS  (Arshad et al. , 2020; Pekel et al,2016; Rahman et al. , 2012; Sallwey et al. , 2018; Senthilkumar et al. , 2019; Yeh et al. , 2016)  a clear lack of field data exists for an accurate assessment, as deplored by numerous authors. To our knowledge, no studies use both methods for assessment on such time-scale. The present approach is developed on a single tank, however the methodology can be applicable to other tanks as soon as bathymetry and meteorological data are available.
The temporal constancy for the parameters such as surface runoff, percolation coefficient and CN number can also be questioned. On a 14 year's duration scale those parameters are unlikely constant. Runoff area and CN are affected for example by infrastructures construction, variation of land use, soil moisture, previous raining events. However the data fitting quality with only two parameters on this time scale assumed that changes in landuse are relatively minimal. This agree with field observation since the watershed is monitored since 2000  (Maréchal et al. , 2004). However, LANDSAT data on the period 2000-2001 seems less well reproduced than the later years. This may come from an unidentified modification in the area of interest. Percolation coefficient, which may vary depending on the soil type, soil moisture, depth of the saturated zone, clogging at the tanks bed is likely to vary in time and space.
Those small ranges show limited uncertainties on the presented results. Such dynamics is comparable to other tanks in the country or elsewhere. The strong variations in levels and volumes observed between the years 2012 and 2013 are similar to the variations observed in other studies for the country (e. g  Massuel et al. , 2014)  or other tanks in the watershed (Figure  1 ). These comparisons give confidence on the representativeness of the Tumulur tank to provide general trend. This methodology also presents the advantage of being cheap, since the LANDSAT data are free, the GPS tracking can nowadays be made through a smartphone and the pressure sensor can be replaced by a direct reading on a scale.
Such analysis can be performed everywhere to assess the long-term impact of such tanks and requests a limited financial investment, since LANDSAT data surface water are freely available. In addition, most recent missions Sentinel offers improved spatial resolution (10 meters rather than 30) and revisit frequency: 5 to 12 days for respectively optical multispectral data Sentinel-2 and radar data Sentinel-1. True bathymetries from very high-resolution digital elevation model are also a way to generalize this modeling approach at regional scale  (Pascal et al. , 2021). Comparison with other similar schemes show the representativeness of the approach and may help in the assessment of larger programs of MAR around the country and abroad. Figure Caption.
the images are built with a 30 m spatial resolution in the visible bands (i. e. blue, green, red, nearinfrared, and mid-infrared). From the May 31 2003, the Scan Line Corrector (SLC) in the ETM+ instrument failed and images have come with "no data" strips. Therefore, a part of the data is not always available. Moreover, LANDSAT data are sensitive to cloud cover hence limiting the data availability in such cases. Images, both Thematic Mapper (TM) & Enhanced Thematic Mapper Plus (ETM+) were downloaded from the USGS online database (http://glovis. usgs. gov/) with two criteria:.
Acknowledgements This study was carried out at the  Indo-French Center for Groundwater Research (BRGM-NGRI)  under the  Saph Pani Project  supported by the  European Commission  within the  Seventh Framework Program  Grant agreement No. 282911. ICRISAT Hyderabad  is acknowledged for providing meteorological data. Mr Wadjiduddin ,  Mr Viossanges , and  Mr Schwarz  are kindly acknowledged for the field work. 10991085, ja, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1002/hyp. 14759by  Cochrane France, Wiley Online Library on  [ 09/11/2022 ]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions)on  Wiley Online Library  for rules of use; OA articles are governed by the applicable Creative Commons License Long term monitoring of rainwater harvesting tanks.
Background Extracting clinical phenotypes from large electronic health record (EHR) databases, also known as clinical data warehouses, is a key step for several medical applications from epidemiological research  [1]  to prognosis prediction  [2, 3]  and therapeutic decision support  [4, 5]. Reliable automatic extraction of patient phenotypes from large EHR databases remains a challenge, especially in languages other than English  [6]. The actual identification of patients' phenotypes is still largely done via the International Classification of Diseases, Ninth/Tenth Revision (ICD-9/ICD-10) code extraction, reading of clinical notes, or extraction of entities via regular expressions. However, as shown by Farzandipour et al  [7]  on more than 300 EHR ICD-10 codes, 22. 7% presented errors in principal diagnosis codes, of which 33.
3% were major errors. Benkhaial et al  [8]  also showed in a study of 200 patients, ICD allergy codes were present for 18 patients, while 51 had allergy information in a written note, indicating that only 35% of the allergies were correctly coded. These identification methods thus lack precision and require important human control. With the improvement of natural language processing over the last 10 years, new language models such as Word2vec  [9] , GloVe  [10] , FastText  [11]  and, more recently, Bidirectional Encoder Representations from Transformers (BERT)  [12]  have allowed significant progress for various natural language processing tasks such as translation, question-answering, and named-entity recognition via an efficient word representation. Named-entity recognition corresponds to the extraction of certain classes of entities in a raw text.
Multilabel Classification To improve clinical interpretability and to analyze patients along several medical dimensions (ie, labels), we chose to perform a multilabel classification of all the terms. The corresponding class is all the MeSH-C head chapters, corresponding to 22 medical fields: infections, ophthalmologic, stomatology, cardiovascular, digestive, respiratory, nervous, etc. A BERT model for the sequence classification was used and trained on all annotated entities and all MeSH terms and their synonyms. Synonyms of MeSH terms were obtained by extracting all the French terms sharing the same code unique identifier in the unified medical language system defined by their authors as a "set of files and software that brings together many health and biomedical vocabularies to enable interoperability between computer systems"  [25].
Ethics Approval The results shown in this study are derived from the analysis of the AP-HP data warehouse. This study and its experimental protocol was approved by the AP-HP Scientific and Ethical Committee (IRB00011591 decision CSE 20-0093). All methods were carried out in accordance with relevant guidelines (reference methodology MR-004 of the Commission Nationale de l'Informatique et des Libertés [19]). All medical records have been pseudonymized. Patients are informed by the AP-HP data warehouse that the data are pseudonymized and that they can object to their sharing. Their consent was therefore collected prior to our study.
Summary In this study, we developed a novel end-to-end algorithm from raw clinical notes to cohort similarity extraction. We have shown that we can cluster very specific phenotypes on an annotated data set and build similarity cohorts with good mean average precision results. These phenotypes and diseases were chosen as a proof of concept, with 2 general phenotypes such as osteoporosis and lung infection and 2 very specific phenotypes with nephritis in SLE and ILD in scleroderma. However, our algorithm can be applied to other phenotypes or diseases as well. Furthermore, our system can be applied to any other data warehouse and does not contain any handcrafted rules. An interactive demo is available online  [28] , and all our codes are available on GitHub  [29].
Comparison With Previous Work Other studies have focused on patient similarity cohorts; for instance, in the French language, Garcelon et al  [30]  used a patient representation and a similarity measure to try to find patients with rare diseases in the Dr Warehouse database  [31]. Although their objective is quite similar to ours, they used a different representation based on the term frequency-inverse document frequency weights of the extracted concept in each clinical note, and the concept extraction is based on handcrafted rules. They obtained a percentage of 71%-99% of indexed patients returning at least one similar true-positive patient within the first 30 similar patients, and the average number of patients with exactly the same disease among the 30 patients was 51%. In a second study based on the same term frequency-inverse document frequency similarity metric, they evaluated the association between clinical phenotypes and rare disease and measured the relevance of the first 50 similar patients by a domain expert a posteriori; they obtained average precision from 0.
For example, "glomerular disease with SLE" has the ICD-10 "M3214" but in the entire database of 39 different hospitals, no patient had this particular code. This is because the coding is primarily done to describe the severity of the patient being managed, and this last code, in particular, does not reflect the severity of the renal involvement (in our case, codes for nephritis usually used would be N03, N04, or N05 and M320, M321, M328, and M329 for SLE). Similarly, scleroderma with pulmonary involvement has an ICD-10 code M348 that also does not appear in our database. Assuming that an important clinical fact is repeated several times in a clinical report (eg, a patient hospitalized for acute coronary syndrome will have many cardiovascular terms linked to his/her cardiac condition), our distance computation from equations 1 and 2 depends on the number of terms in the document.
Acknowledgments The authors would like to thank the  Assistance Publique-Hôpitaux de Paris data warehouse , which provided the data and the computing power to carry out this study under good conditions. We would like to thank all the medical colleges, including the college of internal medicine, especially  Prof Jean-Emmanuel Kahn ,  Dr Guillaume Bussone ,  Prof Sébastien Abad ,  Dr Virginie Zarrouk ,  Dr Noémie Chanson ,  Dr Antoine Dossier ,  Prof Luc Mouthon , and  Dr Geoffrey Cheminet  from the department of rheumatology. We would also like to thank  Dr Augustin Latourte ,  Dr Florent Eymard ,  Prof Xavier Mariette ,  Dr Gaétane Nocturne ,  Prof Raphaele Serror ,  Prof Sébastien Ottaviani ,  Prof Francis Berenbaum ,  Prof Jérémie Sellam ,  Prof Yannick Allanore ,  Prof Jérôme Avouac ,  Prof Maxime Breban ,  Dr Félicie Costantino , and doctors from the dermatology, nephrology, pneumology, hepato-gastroenterology, hematology, endocrinology, gynecology, infectiology, cardiology, oncology, emergency, and intensive care units, who gave their agreements for the use of the clinical data.
Target population Four populations were included: poultry (broilers and layers) farms, pig farms, fish farms (corresponding to farms with fishponds and/or fish-cages) and livestock-fish farms corresponding to pig-fish and poultry (layer, broiler or duck)-fish farms. Commercial farms, that is farms of any size where eggs, meat or fish products are sold in markets, restaurants or companies, were targeted. Backyard chicken and pig farmers keeping livestock for their own or friends' consumption were not included. Large integrated production systems, where farmers are contracted by a large private company, were also not included as no official data of these farms were available. Thoulakom and Phonhong) were selected based on the large number of farms in these areas (Figure  1 ).
Data collection Two principal investigators and 11 students from the Faculty of Agriculture of Nabong of Lao PDR, who were trained prior to the start of the survey, interviewed farmers over a 3-week period from 3 to 24 April 2018. Interviews were conducted in Lao. Responses were entered on electronic devices with Sphinxdeclic (Le Sphinx) software. The interviews lasted for 1 h on average. Photos were taken of products (e. g. veterinary drugs and feed) that farmers were willing to show during the field interviews. The presence of antibiotics on a farm was identified by examining the label, leaflet or packaging of drugs and feed present on the farm at the time of the visit.
Data management and analysis Responses of the poultry, pigs, fish and livestock-fish farmers were merged into a single database. Responses were duplicated for livestock-fish farms, with one observation (row) corresponding to a poultry or pig unit, and one observation corresponding to fish unit. Initial descriptive analyses, in effect, showed that the responses (regarding KAP) of livestock-fish farmers differed greatly depending on the type of unit which was considered (fish vs. livestock). The data obtained from the sixth section of the questionnaire (type of antibiotics, number of treated animals and methods of administration) were stored in a separate database. Of the 102 variables corresponding to the responses to the questionnaires, 14 were removed because they were deemed irrelevant to the analysis or because there were too many missing responses.
First, a multiple factor analysis was performed with the 88 remaining variables. Multiple factor analysis is a method used to summarize and visualize a complex dataset containing variables split into groups, taking into account the contribution of all groups to define the distance between individuals. These analysis synthesized in a lower Euclidian multidimensional space the information contained in structuring and supplementary variables  (Husson et al. , 2017). Among the 88 variables, were used as supplementary variables. The multiple factor analysis was followed by a hierarchical clustering analysis. The hierarchical clustering analysis aggregated observations into clusters based on their locations in the Euclidian multidimensional space derived from the multiple factor analysis. The representation of modalities of the variables in each cluster was assessed with a hypergeometric test, the v-test.
, 2021). Despite these limitations, 364 farmers were interviewed in 2 different provinces, representing 4 different farmer populations (poultry, pig, fish and livestock-fish commercial farms). Our study thus included the diversity of farms described in Lao PDR  (Poupaud et al. , 2021)  and therefore provides an accurate picture of the current patterns of ABU in the livestock sector in this country. Data were collected based on questionnaires, reporting the respondents' statements and we acknowledge that farmers' declarations may differ from their actual practices  (Maiman & Becker, 1974). In order to limit bias, the interviewers adopted a neutral attitude towards the farmers. Measuring ABU was particularly challenging in this study given the fact that, unlike in other contexts, there is no farm register in which on-farm treatments are recorded.
ACKNOWLEDGEMENTS Thanks to the  Food and Agriculture Organization of the United Nations Regional Office for Asia and the Pacific , the  Fleming Fund  and the GREASE platform in partnership (https://www. grease-network. org/)for their financial support. Thanks also to the  Faculty of Agriculture (Nabong)  at the  National University of Laos  for their field and technical collaboration. Finally, thanks to the provincial (Vientiane and Vientiane Province) and district ( Xaythany, Naxaythong, KeoOudom, Phonhong ) offices of agriculture and forestry and to village-chiefs, farmers and households for their collaboration. We also thank  Grace Delobel  for verifying our written English. Fleming Fund, the  Food and Agriculture Organization of the United Nations Regional Office for Asia and the Pacific ,  LOA/LAO/2018-003 -GCP/GLO/710/UK , GREASE platform in partnership (https://www.