dataesr/scientific-paragraphs-categorization huggingface dataset:
The BATS and QTAB datasets include behavioral and psychological measures (e. g. , assessments of cognition and behavior, self-reported mental health and well-being) for further investigation of accelerometry measures. BATS and QTAB data is available from the project owners upon request. The UK Biobank, previously discussed in Subheading 5. 2, collected accelerometry data in 100,000 participants between 2013 and 2016. Participants wore a wrist-mounted activity monitor to capture physical activity and sleep patterns for 7 days. Since 2018, repeat measures have been collected for a subset of participants every quarter to examine seasonal influences on measurements. Data is available in raw (measured every 5 s) and average (by day and hour) acceleration formats. The deep phenotyping of the UK Biobank has allowed for accelerometry-based measures to be examined alongside several other measures, including brain structure [181], mood disorders  [182] , and Alzheimer's disease  [183].
UK Biobank data is available online following registration (https://bbams. ndph. ox. ac. uk/ams/). The mPower study (2015-present), sponsored by Sage Bionetworks with funding from the Robert Wood Johnson Foundation, aims to establish the baseline variability of real-world activity measurements of individuals with Parkinson's disease. Data is collected through an iPhone application, with minimal interruption to the daily life of participants. The initial data release (collected over 6 months) included health survey and sensor-based activity (e. g. , gait and balance) data for ~8000 participants (with ~1000 selfidentified as having a professional diagnosis of Parkinson's disease). In addition, approximately 900 participants contributed at least five separate days' worth of data. mPower data is accessible through the data sharing service Synapse (https://www.
Acknowledgments and Fundings This research was supported by the  Australian National Health and Medical Research Council  ( 1 , 078 , 037, 1 , 078 , 901, 1 , 113 , 400, 1 , 161 , 356 , and  1 , 107 , 258 ), the  Australian Research Council  ( FT180100186  and  FL180100072 ), the  Sylvia and Charles Viertel Charitable Foundation , the program " Investissements d'avenir "  ANR-10-IAIHU-06  ( Agence Nationale de la Recherche -10- IA Institut Hospitalo-Universitaire -6 ) and reference  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute ), the  European Union   H2020 program  (project  EuroPOND , grant number  666992 ), the joint  NSF/NIH /ANR program  " Collaborative Research in Computational Neuroscience " (project  HIPLAY7 , grant number  ANR-16-NEUC-0001-01 ), the  ICM Big Brain Theory Program  (project  DYNAMO , project  PredictICD ), and the  Abeona Foundation  (project  Brain@Scale ).
ariesepigenomics. org. uk/ https://github. com/ MRCIEU/aries https://www. bbmri. nl/ node/24 https://www. ncbi. nlm. nih. gov/projects/ gap/cgi-bin/study. cgi?study_id= phs000724. v9. p13 Other omics Transcriptomics Transcriptomics, metabolomics Transcriptomics, metabolomics Smartphones and sensors NA NA Genomics Methylation Methylation Methylation Genetics (genotyping, exome, WGS, twins) Genotyping Genotyping, pedigree Genotyping, WGS EEG/MEG NA NA NA Neuroimaging (MRI, PET MRI) MRI (T1w, DWI, mcDESPOT (subset of offspring cohort)) NA MRI (T2w) Clinical Clinical evaluations, obstetric data, cognition, questionnaires Clinical information and bioassays (depending on the sub-cohort) Extensive clinical evaluations and bioassays, original focus on cardiovascular diseases Cross-sectional/ longitudinal Longitudinal (2 time points for mother, 3 for offspring) Longitudinal Cross-sectional (multi- generational) Country UK Netherlands USA Age range Average age: Mothers (antenatal = 28.
This approach -BFASTm-L2 -relies on linking a high-speed algorithm (BFAST monitor) with a time series similarity metric (Euclidian distance L2) sensitive to seasonal changes. The capacity of BFASTm-L2 to identify the date of change in different situations was tested on two data sets, and compared to the performances of three other algorithms (BFAST monitor, BFAST lite, and Edyn). The data sets are 1. a published benchmark data set composed of 25 200 simulated SITS with different change types and change magnitudes, and 2. the 2000-2020 MODIS NDVI SITS over a 200x200 pixels area in Senegal including different study sites which have undergone recent LULC changes due to agricultural large-scale land acquisitions (LSLAs) (as reported in the ground field database used in this study). The results show that BFASTm-L2 is efficient in accurately detecting in time most of the changes, and, in contrast with BFAST Lite and BFASTmonitor, to spatially highlight LSLAs-induced changes without the need of any prior knowledge.
Global approach In this study BFASTm-L2 is proposed as a method to select, in long and dense NDVI time series with multiple breakpoints, the optimal breakpoint linked to the most important land use change, that is the one related to the most important "pattern" change within time series. Because the breakpoint selection is based on the breakpoint magnitude, we tested a magnitude metric based on the Euclidean distance (L2), a time series similarity metric proposed by  Lhermitte et al. (2011) , which is prone to be more sensitive to seasonal changes than to abrupt and gradual changes. BFASTm-L2 relies thus on the sequential running of BFASTmonitor for the fast detection of breakpoints, jointly with the Euclidean distance (L2) for the breakpoint selection. Because long and dense time series most of the time include multiple changes of different types (often combined) and intensities, a benchmark dataset of simulated seasonal time series including a unique change was used to answer the first research set of questions, aiming to assess the temporal accuracy and sensitivity of the tested change detection algorithms to different types and intensities of change.
More specifically, a sub-sample of the benchmark dataset provided by  Awty-Carroll et al. (2019)  composed of 25 000 simulated SITS was used, including different single change types and multiple noise/change intensity levels (see Table  1 ). To answer the second set of questions, related to the performance of the change detection algorithms in detecting and mapping LSLAs in Senegal, the MODIS 16-day NDVI time series (MOD13Q1 v. 6) acquired over the 2000-2021 period in different study areas with known LSLAs were used. Results were validated with ground observations. The flowchart of the data and methods is illustrated here below in Fig. 1.
Benchmark dataset and preprocessing This study made use of the simulated NDVI time series (10-year 2006-2015 at 16-day temporal resolution) data set created by Awty-Carroll (  2019 ) and available at https://osf. io/taf9y/. The 16-day temporal frequency makes this dataset suitable for the purposes of this study which uses the MODIS satellite imagery. In this study, only the gap-free simulations were used consisting of 25 200 seasonal time series generated to represent a large range of ecosystem dynamics based on the previous work of  Verbesselt et al. (2010b). This dataset includes a large range of unique trend, abrupt (positive and negative), seasonal (amplitude, length of season (LOS) and number of seasons (NOS)) changes, with eight levels of noise (random value from a normal distribution with a mean of 0 and a standard deviation ∈ [0: 0.
Study area and ground dataset. Senegal is in the west part of the Sudano-Sahelian zone and is characterized by an overall low average annual rainfall, but with high inter-and intra-annual variability that constrains the vegetation growth. The precipitation shows an increasing gradient along the North-South direction. A distinct seasonality is present, with a long dry season and a short rainy season spanning from late June to early October  (Abel et al. , 2019). As many African countries concerned by Large Scale Land Acquisitions (LSLAs) (https://landmatrix. org), Senegal had in 2016 around 3% of its total arable land (270 908 ha) declared under contract by foreign investors from 12 countries  (Harding et al. , 2016). However, as>50% of those deals have an area under contract smaller than 5 000 ha, the country is less affected by the so called "megadeals" (>50 000 ha, only 2 out of 19 in 2016).
Because of those climatic and LSLA characteristics, Senegal represents an interesting and difficult study case for the detection of land use system changes such as those induced by LSLAs. The Senegalese Institute of Agricultural Research (ISRA) conducted in 2019 an extensive field campaign on LSLA. >700 records and corresponding attributes were initially recorded in a database (M. Dieye, personal communication, 2022). Attributes consist in all kinds of information related to the identified deals, such as: name, deal type, coordinates, negotiation status, implementation status, year of transaction, size, previous land use, previous land tenure etc. From this database, and using observations from Google Earth/Sentinel Hub images, a selection was done to keep only active agricultural deals with growing crops (some deals are abandoned, others are still in negotiation), and with an implementation date after 2003 (year as from which changes may be detected from MODIS SITS, considering the training period length needed by the detection algorithms).
From this ground-field database subsample, four individual (points) LSLA study cases with different land processes under different ecoregions (see Fig. 2 ) were selected for algorithm testing purposes. Point is an example of a conversion from small agriculture to LSLA, points 2 to 4 are examples of conversions from natural vegetation to LSLA. More specifically, study case 1 (16. 1118 (Tappan et al. , 2004) ) and are within pivot irrigation areas belonging to a concession growing vegetables. Changes occurred from the end of 2011 for point 1, and the start of 2016 for point 2. Point 3 (14. 6444 • N, 17. 0271 • W), in the "Agricultural Expansion Region" as defined in  Tappan et al. (2004)  (rainfall: 600-700 mm), is within one of the production blocks belonging to a concession specialized in vegetable production, and changes are observed as from 2008.
MODIS NDVI data and pre-processing. With its global coverage, moderate spatial resolution (250 m) and high temporal resolution (1 to 2 days), the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor allows for the detection of subtle changes in land cover. A set of MODIS NDVI 16-day composites at 250 m resolution (MOD13Q1, collection 6) acquired over Senegal for the period 2000-2021 was preprocessed in Google Earth Engine. The 16-day composite NDVI product was chosen to reduce NDVI variability due to meteorological distortions like clouds. Series were gap-filled with linear interpolations and smoothed using an optimized weighted Savitzky-Golay filter  (Chen et al. , 2004)  in order to reduce the noise. Weights were computed following the approach developed in  Piou et al. (2013) , and are function of the pixel's reliability (i.
This study made use of the Python package bfast0. 7, available at https://pypi. org/project/bfast/, that provides a parallel implementation of the BFASTmonitor algorithm. The parameters used were: h = 0. 25 (the MOSUM bandwidth), k = 3 (default and minimal number of harmonic terms possible), and threshold level = 0. 05. To detect multiple breakpoints, the algorithm was iteratively run on the MODIS NDVI 2000-2021 image stack, each 3 months, using a 3year training period (after testing of two training period lengths hl = 2 vs. hl = 3 years) and a monitoring period of one year given that the BFAST monitor change magnitude is relative to the monitoring period length. No "penalty" period after each detected breakpoint (as opposed to Awty-Carroll (  2019 )) was applied to avoid missing significant changes.
Finally, and with respect to the breakpoint magnitude, while originally given as a standardized value (i. e. the residual value divided by the chart's control limits, and then rounded down to the nearest integer value), the magnitudes in this study correspond to the residual values at the breakpoints' location. The reason for this choice is that, when ran continuously, more than one breakpoint may have the same standardized value impeding the selection of a unique breakpoint. In this study the same values as in Awty-Carroll (2019) were applied (λ = 0. 3, persistence = 6). Their R version of Edyn (available at: https://github. com/klh5/season-trend-comparison/tree/master/ewmacd) was used, adapted from the original R script  (Brooks et al. , 2014)  and Edyn  (Brooks et al. , 2017). This version enables selection of stable (without breakpoints) sliding windows of fixed length (2 years) that are used as training periods, thus allowing continuous monitoring.
Temporal accuracy of the breakpoint BFASTmonitor, BFAST Lite, Edyn and BFASTm-L2 were tested on the benchmark dataset (cf. section 2. 1) to assess the performance of each method to accurately detect in time the different types of change (occurring in January of 2011). The breakpoints with the highestmagnitude were selected, and their temporal distributions relative to the true change are presented in Fig. 4  (cf. section 3. 3. 1). As a first observation one can see that most of the highest-magnitude breakpoints (of almost any change intensity), and with the exception of BFASTmonitor (4. a), are close in time to the true real change (less than 6 months). The algorithms have however slightly different sensitivity to the different types of changes: -Regarding the seasonal changes (amplitude, LOS and NOS), BFASTm-L2 breakpoint magnitudes (4.
83. 4% for both BFASTm-L2 and Edyn). -Finally, regarding the no-change type, Edyn (4. c) was the only algorithm to correctly detect no change in almost 80% of the cases (as seen with the NA class). Those first results show that selecting the BFASTm-L2 highestmagnitude breakpoint to correctly detect in time seasonal changes is effective. However, as one can expect multiple breakpoints detected in long and dense time series triggered by different types of change, the next question that comes up is: how comparable are the breakpoint magnitudes induced by the different change types? As a small parenthesis, it is worth to note that the performances of Edyn, BFAST Lite and BFASTmonitor are different than in  Awty-Carroll et al. (2019). This is explained by the fact that: 1-A subsample of the original dataset was used (dataset without missing data), and smoothed; 2-BFASTmonitor was run continuously without considering any "penalty" period after each detected breakpoint; 3-Only the distributions of the highest-magnitude breakpoints were evaluated; 4-The "correct" detection period considered here for the abrupt changes is larger than in Awty-Carroll et al.
Assessment on individual (pixel) study cases In this section, the capacity of the four algorithms to detect real seasonal changes on the four study cases (distributed from arid to humid conditions, see Fig. 1 ) is evaluated. The overview of Fig. 6 , on which are overlaid the 2000-2021 MODIS NDVI time series and the period of change (grey boxes) for each study case, shows the diversity of type and intensity of changes found in the dataset: seasonal changes (all study cases present changes in amplitude, and study cases 1 to 3 show  On Fig. 6  (subpart b of each subplot), the multiple breakpoints detected by BFASTmonitor, BFASTm-L2 and Edyn are represented with vertical lines. The selected breakpoints are the ones having the highest magnitude, and are highlighted with a red dot at the extremity.
Running times of the algorithms To assess the algorithm speed on real time series, the four algorithms and the L2 distance alone were applied on varying-size areas: from 10x10 to 200x200 pixels. Results are presented in Fig. 8. From Fig. 8 , BFASTm-L2 appears to be very fast (16. 4 min for a 200x200 pixels area), just after BFASTmonitor (2. 0 min for a 200x200 pixels area, with a median of numbers of breakpoints of 37). The "forcebrute" method (the continuous computation of L2 at a 3-month step) took 21. 1 min for the same area size, followed by BFAST Lite (86. 5 min), and finally Edyn (439. 8 min). Worth is to note that the R implementation of Edyn was called from python using the rpy2 interface, which may slower the entire process. Faster python implementation of Edyn may be further tested (as the vey recent pyEWMACD available in github https://github.
BFASTm-L2, an efficient method to detect seasonal changes The results obtained with the single-change simulated data set showed that BFASTm-L2 was the approach with the overall best performance in accurately detecting seasonal changes, thereby demonstrating the efficiency of combining BFASTmonitor and L2 distance for timely breakpoint selection (Fig. 4 ). The algorithm was particularly good at detecting, in a benchmark dataset with single changes, LOS changes (84. 8%) and NOS changes (98%), but also changes in amplitude (76. 8%) and break/trend changes (83. 4%), which were however better detected by BFAST Lite (99%). When tested over simulated time series with a unique change, all the algorithms showed increased sensitivity (i. e. higher breakpoint magnitude) to abrupt changes (Fig. 5 ). However, when considering the seasonal distributions alone in the violin plots of Fig.
Acknowledgments The authors wish to thank the  ISRA-BAME  and the  Land Matrix Initiative  for data gathering and support. This work was supported by the  French Space Agency (CNES)  through the funding of the  TOSCA-VISAGE  project. Y. Ngadi  received a fellowship from  Montpellier University. This work was also supported by the  French National Research Agency  under the Investments for the  Future Program #DigitAg referred  as  ANR-16-CONV-0004.
Background: Electronic health records (EHRs) contain valuable information for clinical research; however, the sensitive nature of healthcare data presents security and confidentiality challenges. Deidentification is therefore essential to protect personal data in EHRs and comply with government regulations. Named entity recognition (NER) methods have been proposed to remove personal identifiers, with deep learning-based models achieving better performance. However, manual annotation of training data is time-consuming and expensive. The aim of this study was to develop an automatic deidentification pipeline for all kinds of clinical documents based on a distant supervised method to significantly reduce the cost of manual annotations and to facilitate the transfer of the deidentification pipeline to other clinical centers.
Methods: We proposed an automated annotation process for French clinical deidentification, exploiting data from the eHOP clinical data warehouse (CDW) of the CHU de Rennes and national knowledge bases, as well as other features. In addition, this paper proposes an assisted data annotation solution using the Prodigy annotation tool. This approach aims to reduce the cost required to create a reference corpus for the evaluation of state-of-the-art NER models. Finally, we evaluated and compared the effectiveness of different NER methods. Results: A French deidentification dataset was developed in this work, based on EHRs provided by the eHOP CDW at Rennes University Hospital, France. The dataset was rich in terms of personal information, and the distribution of entities was quite similar in the training and test datasets.
Several recent studies have shown that machine learning-based models trained on annotated datasets achieve better performance than traditional rule-based methods on clinical NER tasks for PII extraction (  [10] ). Dernoncourt et al. ([11] ) proposed a deep learning-based approach based on artificial neural networks (ANNs) for the deidentification of EHRs and presented promising results in two publicly available datasets in English: i2b2 (  [12] ) and MIMIC (  [13] ). In France, Paris et al. (  [9] ) developed a machine learning model based on a recurrent neural network Bi-directional long shortterm memory (Bi-LSTM) associated with a conditional random field (CRF) for the deidentification of hospital reports recorded in an Observational Medical Outcome Partnership (OMOP) 3 database. Their neural network-based model was trained on a manually annotated set of 2,589 hospital text documents from the Assistance Publique des Hôpitaux de Paris (APHP) and obtained an F1 score of 95.
7%. This score was then improved by hybridization with rule-based and knowledge-based methods, achieving an F1 score of 96. 7%. In such approaches, manual annotation is a crucial step in the process of training NER models, but it also comes with a set of limitations. One of the main limitations is that the process is time consuming and expensive (  [14] ), particularly when dealing with large amounts of data and when domain expertise is needed. Furthermore, the limited availability of labeled datasets (  [15] ) and the privacy concerns of manual annotation (  [16] ) have severely limited the applicability of these supervised techniques in other languages such as French (  [4] ). Moreover, the model trained on a limited amount of data or in a monocentric fashion may not generalize well to unseen data, which could lead to lower performance on new clinical reports or in other hospitals (  [17] ).
Additionally, since the annotation is based on a limited dataset, it may not cover all the entities or variations of the entities that the model will encounter in a real-world scenario. Several studies have accepted the expensive cost of manual annotation and have hired teams to label training data (  [6] ). However, an increasing number of researchers are turning to less expensive techniques to generate automatic labels. One of these techniques is distant supervision, where data are matched with entities in knowledge bases to produce noisy labels (  [18] ). Other approaches include rules for labeling data (  [9] ). Although these techniques are less expensive, they produce noisy labels, which can negatively impact the performance of the resulting model (  [19] ). Approaches such as Snorkel  (Ratner et al.
Jiang et al. ([31] ) compared two machine-learning approaches, CRF and SVM, for the extraction of clinical entities using a training dataset with 349 annotated notes and a test dataset with 477 annotated notes. In their first experiments on the training set (using a fivefold cross-validation), CRF outperformed SVM with equivalent features. Additional features and kernel optimization for the SVM may improve its performance. However, it also indicates the complexity of SVM parameter optimization. Based on this comparison, they proposed a novel hybrid clinical NLP system using both ML methods and rule-based components. In the 2010 i2b2/VA NLP challenge, their approach achieved an F1 score of 83,91% for concept extraction and 93,13% for assertion classification.
They compared the performance of their system with that of CRF-based systems on two deidentification datasets: i2b2 [8] and MIMIC (  [13] ). It achieved an F1 score of 97. 85% on the 2014 i2b2 deidentification challenge dataset and an F1 score of 99. 23% on the MIMIC deidentification dataset. These RNN-based architectures have been further improved. In their paper, Huang et al. (  [36] ) performed a comparative study of several LSTM-based models for sequence tagging. These models include LSTM networks, Bi-LSTM networks (Bi-LSTM), LSTM with a conditional random field layer (LSTM-CRF), and bidirectional LSTM with a CRF layer (Bi-LSTM -CRF). The results demonstrate that the hybrid Bi-LSTM -CRF model outperforms the other models. It achieved an F1 score of 94. 40% on the CoNLL2000 dataset and an F1 score of 84.