dataesr/scientific-paragraphs-categorization huggingface dataset:
Description of testing visits and outcome measures Table  2  presents the timing of the testing visits and outcome assessment measures. The baseline assessment (pre-test) visit (V1) will take place on the rst day of the 3-week in-patient hospital rehabilitation stay. A specialist trained therapist will run the clinical assessments (WMFT, FM-UE, Box and Block test-BBT, Bell's test, MMSE, Barthel Index-BI; more details in the Clinical data and outcome measures section). Patients UL kinematics and motor cortical region activation will then be evaluated by one of the investigators on three functional motor tasks: a seated reaching task (Proximal Arm Non-Use, PANU), a circular steering task, and the Ladybug game of the Armeo VRT platform used for rehabilitation. The next day (Day 2), patients will start the standard in-patient stroke rehabilitation program that will last for 13 complete days (excluding weekends) over 3 weeks.
Clinical data Clinical data will be collected by the medical doctor during the routine clinical visit, including date of birth, age, sex, hand laterality before and after the stroke, date of the stroke, delay from the stroke to the inclusion, characteristic of the brain lesion (type and location). Treatment, medication and health events will also be reported. The source documents are the original data, sheets and les, from which the data concerning the research are copied in the electronic case report form (eCRF) by the clinical investigation centre (CIC). Primary outcome measure.
Functional motor tasks The experimental setup for the Seated Reaching and Circular Steering tasks will allow synchronous measurements of motor cortical region activation (functional near infrared spectroscopy-fNIRS and electroencephalography-EEG) and UL kinematics (Kinect V2, Microsoft, United-States) using lab streaming layers (LSL, https://github. com/labstreaminglayer/App-LabRecorder). Seated arm reaching task (see Fig. 5a )-Patients will be comfortably seated on a chair with armrests. A target is located in front of them (centred in relation to the body) at a distance such that the base of the metacarpal of the index nger touches the target when the elbow is extended, the shoulder at ~ 30° of exion and the back resting on the chair. Patients will have to reach the target in two conditions: i) spontaneous condition, where patients are free to use trunk exion compensation as they spontaneously do and ii) maximal condition, where patients limit trunk movements with the help of a light tactile feedback from the therapist to minimize trunk movements.
A practice trial will be done to familiarise the patients on the timing of reaching (~ 5 reaches/20s based on pilot testing) by following a recorded voice of investigator to start and return phase of the reaching movement. After a 2min rest period, the patients will be required to repeat the reaching movements rst with the paretic UL then with the non-paretic UL over 3 trials (20s with 20s rest between trials). The spontaneous condition will be undertaken rst followed by the maximal condition. LSL-Kinect software (see Availability of data and materials for details) will be used to stream and analyse the UL kinematics to calculate PANU score and other kinematic variables according the methods outlined in our previous work  (Bakhti et al. , 2018). Circular steering task (see Fig. 5b )-A computerised version of a circular steering task (LSL-Mouse; see Availability of data and materials for details) based on the speed-accuracy trade-off  (Besson et al.
Collecting and data management The participant will only be identi ed by a unique identi cation number, the rst letter of the name, the rst letter of the rst name, his gender and the year of birth. An identi cation list of subjects will be kept in the investigator's le. The investigator will ensure that the anonymity of each person participating in the study is guaranteed. Information will be collected for each participant in a standardized observation booklet lled out by the investigator or the co-investigator. The source documents are the original documents, data and les, from which the data concerning the research participants are entered in the electronic case report form (eCRF) by dedicated employees at Montpellier university hospital. The investigator undertakes to authorize direct access to the source data of the study during control, audit or inspection visits.
Data from from the actimetry, from the Armeo and from the functional motor tasks (kinematics, performance, EEG/fNIRS) will be extracted from the local password-protected computers and stored on the EuroMov centre private server that can be accessed only by the registered investigators (KB, CM, DM, MM, SP, PJ, GD) with reinforced authentication during the duration of the project. All data will be checked monthly by the PI to ensure that all protocols and ethical guidelines for data collection and analysis are followed. Study data are entered through the e-CRF developed using ENNOV CLINICAL software, which allows realtime data quality control. In order to meet regulatory requirements, this software complies with the recommendations concerning Computerized Systems for the management of Clinical Trials and electronic signature and standards.
Connection is made by a unique password and identi er speci c to each user, which will only give him access to his patients' data. An audit function is integrated into the software, allowing traceability of the data collected as well as the modi cations made. The encrypted data will then be transmitted to the department responsible for data management via a secure internet connection. The eCRF must consist of all the information required by the protocol. The individual data necessary for the analysis of the study will be: -entered into e-CRFs as they are obtained, whether they are clinical or para-clinical data -anonymized by the investigator (the participant will be identi ed only by a unique identi cation number, the rst letter of the name, the rst letter of the rst name and the year of birth; an identi cation list of subjects will be kept in the investigator's le) -authenticated by an electronic signature of the investigator -all entered; therefore, the missing data must be justi ed.
This information is reported in the study database. Once the study is complete, namely the data required for the protocol and any additional data entered (self-questionnaires), the data monitored and validated, the study database is frozen. The maintenance of the database is the responsibility of the Clinical and Epidemiology Research Unit of the Innovation and Research department of the study sponsor. The data is kept in an ASCII type format. The data encoding is integrated into eCRFs. The computer les and the follow-up of any modi cation are saved and made available by the Innovation and Research department of Montpellier university hospital. The database freezing is carried out according to the Innovation and Research department procedures. The freeze is documented by a database freeze certi cate.
It takes place once all the data is veri ed and the corrections requested from the investigators are obtained. The closure of the trial including the closure of the centres will be carried out in accordance with Good Clinical Practice and International Conference on Harmonization. The medical, administrative and observation records (all study-related documents) will be kept for the duration of the study, then archived in locked ling cabinets at the PRM department of Montpellier university hospital, for a minimum period of 15 years after the end of the project.
Monitoring and Quality control The project will be monitored by the CRA. Appropriate monitoring will be implemented based on a risk grid related to the project. In accordance with this grid, the CRA will carry out regular follow-ups of the project's investigation centres (establishment visit, follow-up according to the rhythm of inclusions and a closing visit). All checks will be the subject of a monitoring report by written report (traceability of visits). The investigators agreed to comply with the requirements of the sponsor and the Competent Authority with regard to an audit or inspection of the research. The audit might be applied to all stages of research, from the development of the protocol to the publication of results and the classi cation of data used or produced in the context of the research.
Descriptive analysis Analysis will be done on the intent-to-treat sample population. All patients' characteristics at pre-test (V1), post-test (V2) and retention test (V3) will be described according to the randomization group. Qualitative variables will be described using frequency and percentages of each class. Quantitative variables will be described using median and quartiles. Primary outcome measure analysis aim of the primary outcome analysis will be to compare the functional motor capacity of the UL (WMFT) in the Intervention group comparatively to the Placebo group at V3. The normality of the data from the WMFT will be analysed by the Shapiro-Wilk test. The variance homogeneity will be assessed using the Bartlett test. If the normality is respected, a parametric Student t-test will be performed for mean comparisons.
Funding The ReArm project is funded by the Direction générale de l'offre de soins (DGOS) in the French Ministère des Affaires Sociales et de la Santé (Programme hospitalier de recherche in rmière et paramédicale-PHRIP-2018-0731). CM and GF are supported by PhD scholarships from the Doctoral School of Human Movement Science at the University of Montpellier. The requested budget was calculated according to the design of the study (number of subjects, number of centres and visits planned / patient and number of speci c examinations carried out), the applicable regulations and additional and speci c examinations planned in the framework of this study. As a reminder, this study is based, in 2 centres, on the inclusion of 58 patients in total. The design, management, analysis and reporting of the study are entirely independent of the funding received for the ReArm project.
The  ReArm  project was approved by The  French Research Ethics Committee, (Comité de Protection des Personnes-CPP SUD-EST II, N°ID-RCB :  2019-A00506-51 , http://www. cppsudest2. fr/). The ReArm project was registered on ClinicalTrials. gov(NCT04291573, 2nd March 2020, https://clinicaltrials. gov/ct2/show/NCT04291573.
Availability of data and materials The nal datasets used and/or analysed during the ReArm project will be available from the corresponding author on reasonable request. The software applications developed for this project, the most recent version of the software are available from the corresponding author's Github repository (LSL-Mouse: https://github. com/KarimaBak/LSL-Mouse ; and LSL-KinectV2: https://github. com/KarimaBak/LSL-KinectV2) and an archived version on the Zenodo repository (LSL-Mouse : DOI 10. 5281/zenodo. 4297675; LSL-KinectV2: DOI 10. 5281/zenodo. 4300182). Other materials used in the project (e. g. , consent forms) will be available from the corresponding author on reasonable request.
This method is based on the multiscale model-to-model cloud comparison (M3C2) algorithm and was applied to a multitemporal airborne lidar dataset of the Kaikōura region, New Zealand, following the M w 7. 8 earthquake of 14 November 2016. In a 5 km 2 area, the 3D point cloud differencing method detects 1118 potential sources. Manual labeling of 739 potential sources shows the prevalence of false detections in forest-free areas (24. 4 %), due to spatially correlated elevation errors, and in forested areas (80 %), related to ground classification errors in the pre-earthquake (pre-EQ) dataset. Combining the distance to the closest deposit and signal-to-noise ratio metrics, the filtering step of our workflow reduces the prevalence of false source detections to below 1 % in terms of total area and volume of the labeled inventory.
Dynam. , 9, 1013-1044, 2021 https://doi. org/10. 5194/esurf-9-1013-2021 significantly changing such as bedrock landsliding on barerock hillslopes. Addressing these three sources of uncertainty -volumearea scaling uncertainty, landslide amalgamation and the underdetection of landslides -is necessary. In the last decade, the increasing availability of multitemporal high-resolution 3D point cloud data and digital elevation models (DEMs), based on aerial or satellite photogrammetry and light detection and ranging (lidar), has opened the possibility to better quantify surface change and displacements (e. g. ,  Bull et al. , 2010; Mouyen et al. , 2020; Okyay et al. , 2019; Passalacqua et al. , 2015; Ventura et al. , 2011). The most commonly used technique is the difference of DEM (DoD) which computes the vertical elevation differences between two DEMs from different times  (Corsini et al.
Applicable to any type of 3D data to measure the orthogonal distance between two point clouds, this approach has generally been used for terrestrial lidar and UAV (unoccupied aerial vehicle) photogrammetry over sub-kilometer scales. In the context of landsliding, it has been used to infer the displacement and volume of individual landslides, using point clouds obtained by UAV photogrammetry (e. g. ,  Esposito et al. , 2017; Stumpf et al. , 2015) , as well as for rockfall studies  (Benjamin et al. , 2020; Williams et al. , 2018)  and sediment tracking in post-wildfire conditions (DiBiase and Lamb, 2020). To our knowledge, systematic detection and segmentation of hundreds of landslides from 3D point clouds have not yet been attempted. Here, we produce an inventory map of landslide topographic changes using a semiautomatic 3D point cloud differencing (3D-PcD) method based on M3C2 and applied to multitemporal airborne lidar data.
An objective of this work is to provide a first evaluation of the type of landslides produced during an earthquake that 3D point cloud differencing can detect. Our workflow was designed to be as automated as possible in order to be applied to very large multitemporal 3D datasets in the future. As any topographic data will contain elevation errors  (Anderson, 2019; Joerg et al. , 2012; Passalacqua et al. , 2015)  that may result in false detections of sources and deposits, our workflow combines two steps to filter them out: we first isolate patches of significant topographic change using the statistical model accounting for point cloud roughness, density and registration error defined in the M3C2 algorithm  (Lague et al. , 2013) , and we then use patch-based metrics to detect the remaining false detections.
We illustrate the benefits of working directly on 3D data to generate landslide source and deposit inventories, and we discuss the methodological advantages of operating directly on point clouds with M3C2 compared with DoD, in terms of detection accuracy and error for total landslide volume. The paper is organized as follows: first, the lidar dataset is presented, followed by a detailed description of the 3D-PcD method; second, results of the geomorphic change detection and identification of individual landslides in the studied area are presented. The remaining part of the paper focuses only on landslide sources. First, we evaluate the prevalence of false detections and define optimal filtering parameters to be used to limit their occurrence. Second, the comparison with conventional 2D landslide mapping is presented.
Data description In this study, we compare two 3D point clouds obtained from airborne lidar data collected before and after the 14 November 2016 Kaikōura earthquake (Table  1 ). Both airborne lidar surveys were carried out during summer. Pre-earthquake (pre-EQ) lidar data were collated over six flights performed from 13 to 20 March 2014 for a resulting ground point density of 3. 8 ± 2. 1 points m -2. The vertical accuracy of this dataset has been estimated at 0. 068-0. 165 m as the standard deviation of the difference between the elevation of GPS points located on highways and the nearest-neighbor lidar shot elevation  (Dolan, 2014). However, these control points were not in the survey area. Thus, we have weak independent constraints on the vertical accuracy of the pre-EQ lidar in the survey area.
The post-earthquake (post-EQ) lidar survey took place soon after the earthquake, from 3 December 2016 to 6 January 2017, for an average ground point density of 11. 5 ± 6. 8 points m -2. The vertical accuracy of this dataset has been estimated following the same protocol as the preearthquake lidar data with a mean of 0. 00 m and a standard deviation of 0. 04 m (Aerial Surveys, 2017). The difference in acquisition dates represents a period of 2 years and 8 months. For both lidar point clouds, only ground points defined by the data providers are selected. For the pre-EQ lidar, they were classified automatically using the TerraScan software  (Dolan, 2014) , whereas the post-EQ data were classified using an unspecified algorithm followed by manual validation by the data provider  (Aerial Surveys, 2017).
Manual quality control shows that ground classification is excellent for the post-EQ data but that some points corresponding to vegetation remain in the pre-EQ data. As the incorrectly classified points are located a few meters above the ground, they can lead to false landslide source detection because they translate into a negative topographic change (i. e. , apparent spatially correlated erosion). Thus, we reprocess this dataset to remove as many incorrectly classified points as possible using a method similar to surface-based filtering that removes points or patches of points significantly higher than the locally interpolated ground (e. g. ,  Kraus and Pfeifer, 1998)  (details in Sect. S1 in the Supplement). This operation removes 0. 3 % of the pre-EQ original point cloud, but our results show that classification errors still remain.
The 3D point cloud differencing with M3C2 and the distance uncertainty model The method developed here to detect landslides consists of 3D point cloud differencing between two epochs using the M3C2 algorithm  (Lague et al. , 2013)  available in the Cloud-Compare software  (EDF R&D, 2011). This algorithm estimates orthogonal distances along the surface normal directly on 3D point clouds without the need for surface interpolation or gridding. While M3C2 can be applied on all points, the algorithm can use an accessory point cloud, called core points. In our case, core points constitute a regular grid with constant horizontal spacing generated by the rasterization of one of the two clouds. In the following, all the M3C2 calculations are done in 3D using the raw point clouds, but the results are "stored" on the core points.
, 2012; Passalacqua et al. , 2015). These error sources may create apparent lowamplitude and variable-wavelength topographic change that could be mistakenly considered as a significant change resembling a landslide source or deposit if reg is not high enough. Predicting the spatial pattern of registration error can be done in two ways. The first method involves using a spatially explicit direct error propagation model that accounts for all elevation errors in the lidar survey (e. g. ,  Joerg et al. , 2012). This approach is complex and requires detailed information on the survey, including the trajectory file with the position and attitude uncertainties. This file is rarely available in data repositories and was not available for the pre-EQ or post-EQ datasets. The second approach involves studying patterns of topographic change on flat, stable and near-horizontal surfaces (e.
A critical aspect of the workflow is to choose the lowest reg possible that does not result in too many false detections. A first estimate of reg can be evaluated from the vertical accuracies provided with the lidar datasets, assuming that no systematic bias remains after the registration process (Table  1 ). If we assume that the two vertical elevation errors are uncorrelated, reg is the square root of the sum of the square accuracies and varies between 0. 08 and 0. 17 m. If we assume that the two accuracies are perfectly correlated, which is a worst-case scenario, reg is simply the sum of the two accuracies and would vary between 0. 12 and 0. 21 m. In both cases, it is largely set by the accuracy of the pre-EQ survey (Table  1 ). As no GCPs (ground control points) used to evaluate the lidar survey accuracy are applied in our study area (which will generally be the case in steep mountain areas where landslide inventory creation will be meaningful), we propose not relying on the stated lidar accuracies.
The same surface different sampling test Following the approach proposed in  Lague et al. (2013) , we employ a test based on using different sampling of the same natural surface to tune parameters of the workflow. To this end, we create two randomly subsampled versions of the post-EQ lidar data (which has the largest point density) with an average point density equal to the pre-EQ data. The resulting point clouds correspond exactly to the same surface (i. e. , reg = 0), with roughness characteristics typical of the studied area, but with different point sampling. We subsequently refer to this type of approach as a "same surface different sampling" (SSDS) test.
As roughness is a scale-and point-density-dependent measure, we explore a range for D from 2 m to 15 m for the pre-EQ dataset, which has the lowest point density (Fig. 2a ). We found that D ∼ 10 m represents a threshold scale below which the number of core points verifying this condition significantly drops. The projection scale d should be chosen such that it is large enough to compute robust statistics using enough points but small enough to avoid spatial smoothing of the distance measurement. Following  Lague et al. (2013) , M3C2 computes Eq. (2) only if five points are included in the cylinder of radius d/2 for each cloud. In our case, the pre-EQ data with the lowest point density will set the value of d. We use an SSDS test, applying M3C2 with D = 10 m and d varying from 1 to 40 m. The results (Fig.
2b ) show the following: (i) when it can be computed, the LoD 95 % actually predicts no significant change for at least 95 % of the time, indicating that the statistical model behind the uncorrelated random error component of Eq. (2)  (Lague et al. , 2013)  is correct for this dataset; (ii) the fraction of core points for which the LoD 95 % can be calculated rapidly increases between d = 1 and 8 m, at which point it reaches 100 %. We chose d = 5 m, as it represents a good balance between the ability to compute LoD 95 % on most core points (here, ∼ 97 %) and the smallest projection scale possible. To be able to generate M3C2 confidence intervals for as many points as possible, in particular on steep slopes below vegetation, we employ a second pass of M3C2 with d = 10 m and using the core points for which no confidence interval was calculated at d = 5 m.
We note that d could theoretically be set as a function of the lowest mean point density of the two lidar datasets, res, by d ∼ 25/πres. In our case, the pre-EQ dataset has res = 3. 8 points m -2 and would predict d = 1. 3 m. However, the presence of vegetation significantly reduces the ground point density in some parts, and the overlapping of flight lines creates localized high point density. Therefore, examining the mean ground point density of the entire dataset gives an incomplete picture of the strong spatial variations in point density. These changes in point density, critical to the correct evaluation of the LoD 95 % (Eq. 2), are generally lost when working on a raster of elevation (e. g. , DEM). The spacing of the core point grid should be smaller than half the projection scale d to ensure that all potential points are covered by at least one M3C2 measurement and should be larger than the typical point cloud spacing of the lowestresolution dataset.