dataesr/scientific-paragraphs-categorization huggingface dataset:
Perhaps a different wording such as "giving researchers the freedom to choose their own denoising schemes" could still accommodate what the authors may have wished to state. I cannot find information about the subjects. Who were them? Where was the data collected? With what scanner and sequences? Who approved the protocol? Presumably this information is on reference #1 but it cannot hurt to have that information here. It would be good if a few more details on what exactly FMRIPREP does could be given, without having to rely completely on external links that may no longer be available in the future. Even more important considering that results did change with after minor version changes. Page 2, 1st column, 4th paragraph of the Methods: As written, a reader may think that the input images to FreeSurfer were those non-linearly aligned to the MNI space, which surely was not the case.
But if that was, then the FS analysis would have to re-done as the warps affect thickness and area measurements. Page 3, 2nd column: Regarding the masks, one would have thought that using the mask from FEAT/FLAME could have been a good shortcut instead of creating a new one for randomise. Why wasn't that done? Page 3, Validation section: The strategy using the mask contours to investigate between-subjects registration is surely not a good one for not showing how the overlap between structures. A hyperslab across subjects would have been more informative. Moreover, the contours shown in Figure  1  are a bit concerning for suggesting somewhat suboptimal registration. Still in the Validation section: what exactly is being validated here? It doesn't seem to show that the dataset would be valid or not valid in any particular aspect.
Consider investigating some specific validation parameters over different aspects (e. g. , registration, bias correction, surface reconstruction, the tasks eliciting expected response, etc), or remove this section altogether, as it can be misleading for suggesting that the dataset is "valid" somehow. Page 6: The description of the files is extremely helpful. I note that one of the files listed has extension. h5. Is this HDF5? If yes, please state so. I believe this format was used for the lack of another option, but in fact, this is a great format that probably should in the future be an option for most imaging data we use (both surface-based and volume-based). Of the FreeSurfer surfaces, the white is the most important one, not pial or midthickness. The pial is computed after the white already exists, and its exactness depends on the white.
The midthickness does not match any particular tissue border, and if one measures surface area from it, that area will depend on thickness, which would make it a poor phenotype. It would be great if the white surface files could be provided. Page 7: I find it concerning that information and resources about this dataset are scattered over the internet: There is the current paper (PDF) and its Supplementary Material on F1000, then there are results stored in NeuroVault, source code on Github and Zenodo, and finally, a Docker container on DockerHub. Could not a copy of all these pieces be on a single place that can be simply downloaded and maintained on the long term, e. g. , in DataDryad? How can the readers be sure that all these links will be alive in 10 or 20 years? Is the rationale for creating the dataset(s) clearly described? Yes.
While such additional descriptions will result in a longer paper, the information will be helpful in allowing readers to understand how their specific research questions may be addressed by a deeper exploration of these data. Lastly, the Introduction should also state where the data may be downloaded and a summary of the different unprocessed and processed files that have been shared. Methods: Demographic information on the participants should be provided, as well as a statement of IRB approval. A description of the MRI scanner should be included, along with the data acquisition parameters. Each of the fMRI tasks should be fully described -this will help clarify subsequent reference to different conditions (e. g. , "accept, explode, reject"). Much of the Methods is written for those who are already very familiar with the software packages that are utilized.
It would be helpful to improve accessibility by including a brief description of some of the newer, less ubiquitious software tools. In particular, given how the Methods is framed around use of FMRIPREP, a short intro should be included. particular, given how the Methods is framed around use of FMRIPREP, a short intro should be included. The total numbers of participants for each of the tasks (shown on page 3) doesn't agree with the numbers of task datasets discarded for being incomplete -please note why the additional participants were omitted from the final dataset. Overall, the flow of the Methods section could be improved by adding a workflow or pipeline figure that summarizes the different analysis steps and the versions of the data (e. g. , volume vs. surface approaches). I'm not convinced that "Dataset validation" is an appropriate heading.
Minor comments: -page 2: first report of DVARS is capitalized, but later mentions are not -page 2: "Temporal derivatives were added to all task regressors to compensate variability in the for haemodynamic response function" -page 3: typo -"unsuccesful" -ensure past tense used consistently throughout Methods (e. g. , "tasks data discarded", "no correct were answers registered" on page 3). were Is the rationale for creating the dataset(s) clearly described? Partly.
funded by the  Laura and John Arnold Foundation. JD has received funding from the  European Union 's Grant information:  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  706561. The acquisition of the original dataset was supported by the  Consortium for Neuropsychiatric Phenomics (NIH   Roadmap for Medical Research grants   UL1-DE019580 ,  RL1MH083268 ,  RL1MH083269 ,  RL1DA024853 ,  RL1MH083270 ,  RL1LM009833 ,  PL1MH083271 , and  PL1NS062410 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
ACKNOWLEDGMENTS We thank  Ms. Carleigh Jenne  and  Drs Claudia Gragnoli ,  Naila Karim  and  John Stiller  for helpful editorial assistance and comments on the manuscript. The following individuals have acknowledged financial support for their work on the data presented in the manuscript, and/or their writing and editing contribution to the article:  Lena Brundin ,  MH104622 ;  Todd Gould ,  MH091816 ;  Zoe Donaldson ,  MH102352  and  American Foundation for Suicide Prevention   Young Investigator Grant ; Carlos Zarate  NIMH-NCT02543983  and  15-M-0188 ;  Yogesh Dwivedi MH082802 ,  MH101890 ,  MH100616 , and  MH107183 ;  Christopher Lowry ,  Colorado Clinical & Translational Sciences Institute (CCTSI) Center for Neuroscience  ( CNSTT-15-145 ),  Alfred P. Sloan Foundation  ( G-2016-7077 ),  Rocky Mountain MIRECC  for Suicide Prevention, and Military and  Veteran Microbiome Consortium for Research and Education ;  Lisa Brenner , The  Rocky Mountain MIRECC for Suicide Prevention , and the  Military and Veteran Microbiome Consortium for Research and Education ;  Patricia Zunszain ,  National Institute for Health Research(NIHR) Biomedical Research Centre at South London , and  Maudsley NHS Foundation Trust and King's College London ;  Shami Kanekar ,  USTAR ,  Science Technology and Research Initiative , the  Rocky Mountain MIRECC for Suicide Prevention ;  Perry Renshaw ,  USTAR ,  Science Technology and Research Initiative , the  Rocky Mountain MIRECC for Suicide Prevention ;  Teodor Postolache ,  FDU001418-19 FDA/JIFSAN grant ,  MHBA-016-15S -01   VA CSR&D Merit Award ,  Rocky Mountain MIRECC  for  Suicide Prevention , and the  Military and Veteran Microbiome Consortium for Research and Education.
(2000). To do so, we followed the methodology presented in  Thiery et al. (2017)  and combined reports of dietary composition, including seasonal variation in food item consumption, with studies on the physical properties of primates food. Whenever a species was reported to consume stress-limited food on a regular basis, we classified it as a hard food eater. If stress-limited food consumption was only reported as marginal despite several reports on diet composition for a given species, we classified it as a soft food eater. Species for which data were too scarce or contradictory were classified as undefined. This does not necessarily mean no data on their diet could be found. For instance, the diet of chimpanzees (Pan troglodytes) is detailed in numerous studies, but it was classified as undefined because there are some discrepancies between reports involving forest chimpanzees  (Vogel et al.
ACKNOWLEDGMENTS This work was supported by the  Agence Nationale de la Recherche  ( ANR-09-BLAN-0238 ), the  Ministère de l'Education Nationale, de l'Enseignement Supérieur et de la Recherche , the région Poitou-Charentes (Conventions Région # 07/RPC-R-100  and # 12/RPC-013 ) as well as a research grant of the  International Primatological Society. Many thanks to  E. Gilissen  from the  Royal Museum of Central Africa (Tervuren, Belgium ),  J. Cuisin  from the  Muséum National d'Histoire Naturelle (Paris, France)  and  K. Krohmann  from the  Senckenberg Museum of Frankfurt (Frankfurt-am-Main, Germany)  for granting us access to the specimen used in this work. We are greatly indebted to  A. Mazurier  ( IC2MP ) as well as  A. Euriat  and  J. Surault  (iPHEP) for their help in data acquisition.
g. ,  Nikodelis et al. , 2005; Sanders et al. , 2006; Elipot et al. , 2009; Naemi et al. , 2010; Mason and Formosa, 2011; Callaway, 2015)  to collect kinematic data (i. e. , prerequisite data for assessing behavior). First, two-dimensional analyses were "designed to identify where, why and how swimmers performed better than others"  (Mason and Formosa, 2011, p. 413). The temporal parameters of events (i. e. , duration of start, turn and finish segments) or stroke length (SL; i. e. , distance traveled by the body during a complete stroke), stroke rate (SR; i. e. , number of stroke cycles per minute), and mean stroke velocity are assessed by a digitization procedure using two-dimensional camera-based analysis. It should be noted, however, that simple manual digitization of anatomical landmarks is error-prone and the data processing is long  (Wilson et al.
In the first section, we review the IMU-based investigations of low-order behavioral parameters and their variability. The second and main section is devoted to the assessment of high-order behavioral parameters (i. e. , movement coordination) in swimming and inter-cycle variability. IMUs are particularly well suited to explore the functional role of variability (especially between cycles) through numerous and long time-series analyses performed in an ecological context of performance. As assessing these parameters in swimming depends greatly on sensor use in the constraining aquatic environment, the last section provides practical recommendations ranging from sensor positioning to data processing. Journal and conference articles referencing the assessment of coordination dynamics in swimming with accelerometers or IMUs were selected from the major scientific databases: PubMed, Science Direct, IEEE Xplore, Scopus and Google Scholar.
, 2015b). To compute it, the beginning and end of the swimming event are recorded. Generally, the start of a swimming event is characterized by high acceleration along the longitudinal axis  (Davey et al. , 2008; Bächlin and Tröster, 2011; Stamm et al. , 2011; Ohgi et al. , 2014) , decreasing to values close to zero at the end of the event. The average velocity can also be computed if the event duration and the distance covered by the swimmer are known  (Bächlin and Tröster, 2011; Stamm et al. , 2011; Beanland et al. , 2013). This first level of analysis, which is a simple examination of the raw data when the accelerometer-based information is known (e. g. , sampling frequency, reference axes), provides rapid and useful information on swimming performance. In a further step to characterize swimmers' accelerations for a given distance, scientists can focus on the signal within the starting and ending bounds of the swimming event.
, 2015) , as described in the practical implications section. Dadashi et al. (2012 Dadashi et al. ( , 2014) )  assumed that the average trend of instantaneous velocity peaks is quasi-constant due to the steady regime of front crawl swimming. They therefore extracted the cycle minimum and maximum peaks of instantaneous velocity and fit them with a shape preserving spline  (Fritsch and Carlson, 1980). Then, they assessed the instantaneous velocity by expressing the data into an external reference frame (e. g. , as a function of the gravity vector) to obtain a general overview of the swimmer's velocity profile. Thus, only the component collinear with the swimmer's displacement axis was needed to estimate swimming performance  (Barbosa et al. , 2011; Dadashi et al. , 2015). In contrast,  Stamm et al.
It also had to be personalized for the subject  (Callaway, 2015)  in terms of sensor fixation, location and orientation, the swimmer's technique, and signal processing (e. g. , low-pass filtering cutoff frequencies). Currently, coupling three-dimensional accelerometers and video systems is considered appropriate for investigating the motion phases of swimming training (as recently confirmed by Callaway, 2015, for front crawl phase determination). In other studies, a second data source (i. e. , three-dimensional gyroscope) was added to the accelerometer-video couple to obtain angular velocities, further improving phase detection accuracy and validity. IMU data alone may indeed be too imprecise since these sensors provide only estimations; systematic control with video may thus be essential.
INVESTIGATION OF HIGH-ORDER PARAMETERS TO CHARACTERIZE COORDINATION DYNAMICS AND BEHAVIORAL VARIABILITY IN SWIMMING: NEW PERSPECTIVES USING IMUS Combining spatial and temporal data (i. e. , mixing low-order parameters) is one way to investigate the interaction of the components of the swimmer-aquatic environment system at a behavioral level. Computing the so-called spatial-temporal coordination between two or more segments (or joints) starts with the determination of the angular times series of the segments (or joints) under consideration  (Wheat and Glazier, 2006). When these angles are determined using IMUs, the data processing should take into account drift, offset, sensor synchronization and three-dimensional position determination, all of which implies complex steps compared with the procedures for determining low-order parameters.
These investigations are made possible by considering the orientation of the sensors in the tridimensional domain, without any further help from video systems. Sensor orientation is determined by fusing the data from the acceleration integration and angular velocity (assessed by gyroscopes). As noted, this operation has to deal with the problem of drift, whose negative effects can be limited by using a magnetometer coupled to the accelerometer and gyroscope. The magnetometer compensates for the baseline drift in the other two devices  (Callaway et al. , 2009). The studies on this problem are very recent, but already they point to new ways to better characterize the spatial-temporal coordination of human swimming behavior and its variability over time (works on this topic are summarized in Table  3 ).
Static calibration takes advantage of gravity, being the signal common to all IMUs. Then, an additional dynamic calibration is performed, during which the subject rotates his/her limbs about the proximal joints (hip and shoulder) while maintaining "stiff " ankle, knee, wrist and elbow joints, imposing the same angular velocities for all IMUs. In this way, the relative orientation of the IMUs with respect to the limb can be identified, and the joint angles can then be estimated from the IMU signals. Another method for reducing integration errors caused by drift is to combine the data recorded by a three-dimensional accelerometer, a threedimensional gyroscope and a three-dimensional magnetometer with a Kalman filter  (Zhu and Zhou, 2004). The Kalman filter estimates the state parameters derived from the fusion of the three sensors by incorporating the stable drift-free performance of gravity acceleration and magnetic field (for more explanation on Kalman filtering techniques, please see  Brookner, 1998).
Two issues therefore require further research: (i) the generalizability of the results to larger populations and (ii) the automaticity in the data processing (regularly highlighted as an advantage of IMUs). It should be noted that using IMUs may even be inferior to video analysis if the technical recommendations in the last section of the review are not scrupulously followed. Of particular interest, IMUs can be used to determine joint angles as the first step in investigating the functional behavior of athletes in ecological conditions. The essential computational steps, however, cannot be ignored, from sensor placement to the correction of drift, both being critical steps in the procedure. By following the recommended procedures, researchers-as well as coaches-will be able to study swimmers' coordination patterns and their variability (e.
Material and Methods Stimuli In a first experiment that we label baseline condition, we presented the observer with single surfaces varying in albedo and gloss. In the second experiment which is the main focus of the present research, each stimulus consisted of a squared centered surface with a background surface that is taken from the first experiment. This latter stimulus is similar to the classical simultaneous contrast display. Each surface was a fronto-parallel plane that was slightly deformed along the surface normal. We created three different Perlin noise images (Figure  1 ) in Matlab R2014b (The MathWorks Inc. , Massachusetts, US) and used those procedural textures to generate three different random bumpy surfaces. The luminance value of each pixel in the texture image defined the height on the surface and deformed it accordingly.
The surface was then smoothed using the built-in Smooth operator (Factor: 0. 5, 50 repeats) and rendered using the Cycles Render Engine in Blender 2. 74. We illuminated the surfaces using the Uffizi environment map  (Debevec, 1998). Each surface was rendered with a mixed shader with 90% diffuse and 10% gloss. Our renderings were based on a Microfacet model using GGX to describe the microfacet distribution function D and Smith to describe the shadowing and masking function G (see  Walter et al. , 2007 for details). We manipulated the width parameter in the distribution function to obtain different gloss levels (alpha between 0. 5 and 0. 029). All gloss levels were chosen such that two consecutive levels were approximately perceptually equal by using a single maximum likelihood difference scaling scaling (MLDS;  Maloney & Yang, 2003)  dataset.
Apparatus Each surface was displayed on a 24-in. calibrated LED monitor (Viewsonic V3D245) with a linearized gamma. The resolution was set to 1920 Â 1080. All stimuli were displayed using Matlab R2010a (The MathWorks Inc. , Massachusetts, US) and Psychtoolbox-3  (Brainard, 1997)  that ran on a MAC Pro Quadro-Core Intel Xeon with OSX 10. 5. 8.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: SHR and PM were supported by the  EU   Marie Curie Initial Training Network ''PRISM''  ( FP7 -PEOPLE-2012-ITN , Grant Agreement:  316746 ). The laboratory also benefits from grants  ANR-10-LABX-0087 IEC  and  ANR-10-IDEX-0001-02 PSL*.
Participants and Procedure Two 17-year-old men participated in the study. Having been admitted into the French National Rowing School (Pôle Espoir Aviron -Nantes), both were qualified as expert-level individual rowers. Each rower had more than 10 years of experience in single scull (two-oar) rowing. Rowers individually performed in the national competition and belonged to the French top 10. While both had rowed in crew boats during training sessions, neither had experienced dedicated crew training. Before engaging in the present study they had never rowed together in the same boat. Data were collected during two on-water rowing sessions as a coxless pair (i. e. , in an asymmetrically rigged sweep-oar boat where each rower operates one oar) that took place before and after a 6-week training program dedicated to crew rowing.
Data Collection and Analysis During the pre-CT and post-CT sessions, behavioral data were collected at 50 Hz using the PowerLine system (Peach Innovations, Cambridge, United Kingdom). For the present purposes, we retained the time series of horizontal oar angles (delivered by position sensors in the oarlocks) and boat velocity (delivered by an impeller fixed under the shell). According to  Coker (2010) , the PowerLine angle sensors provide an accuracy of 0. 5 •. No accuracy data are available with respect to boat velocity measurements. For each session, the first 350 recorded strokes were retained for analysis. Full time series of the 350 recorded strokes were first filtered using a low-pass Butterworth filter with a 7-Hz cut-off frequency, run through twice in order to negate the phase shift.
Oar angular velocity (OAV) time series were subsequently derived using the first central difference method. The first 10 cycles were then removed in order to focus on stabilized performance, leaving 340 full strokes for analysis purposes. Samples of five subsequent strokes from the pre-CT and post-CT sessions are presented in Figure  2. With overall crew performance quantified by boat velocity, data analysis focused on the collective level of between-rower coordination and the individual kinematic level of oar-movement patterns. At the individual rower level, different cycles of oar movement were identified by their catch and finish points, corresponding to the minimum and maximum oar angles (with zero defined as perpendicular to the boat, negative in the direction of the bow and positive in the direction of the stern) for each rower separately.
statistical analysis Statistical analysis was carried out with SPSS20 for windows. Chisquare (χ 2 ) tests compared categorical variables between patients with and without PD. Analysis of variance (ANOVA) adjusted for patients' age and behavioral addiction subtype compared the quantitative clinical variables. The effect size for the proportions and means comparisons was estimated through Cohen's-d coefficient, considering |d| > 0. 50 to be a moderate effect size and |d| > 0. 80 to be a high effective size. Bonferroni-Finner's correction was applied to control Type-I error due to multiple statistical comparison. resUlTs.
limitations The findings of this study should be considered with certain drawbacks in mind. First, our study did not examine PD severity. Relatedly, cognition impairment was not assessed, and it has been postulated that PD patients with behavioral addictions have more severe frontal lobe dysfunction than PD patients without behavioral addictions  (38, 39). Second, possibly due to higher awareness of this condition, the number of GD patients in our sample was vastly higher than the number of other behavioral addiction patients. Future research should include larger, more balanced samples so as to overcome these limitations. In this same vein, all data were collected from men who sought treatment and it significantly limits the generalizability of the present study results to women and other populations.