dataesr/scientific-paragraphs-categorization huggingface dataset:
As software oriented toward surfacebased analysis, its visualization is 3D. The interface proposes a few preset views' layouts, which are easy to use but limited in customization. FreeView is not designed for scripting and extension programming, and TkSurfer can be scripted in Tcl language. Other software libraries and toolkits provide programming tools and functions to develop visualization: VTK and Mayavi can be cited. VTK 14 is a C++ language library dedicated to 3D visualization. The VTK project started approximately at the 14 https://vtk. org/ same time our group started the Anatomist project, and there are similarities between them. VTK has a well-designed API and offers powerful 3D rendering features and interactions. The C++ library has Python language bindings. It does not provide a user application; thus, it is intended for programmers, not directly for users.
VTK is a general visualization library, and is not primarily designed for neuroimaging; however, it has neuroimaging applications, and it supports a few neuroimaging image formats. But getting started with VTK, especially for neuroimaging applications, is more difficult than with Anatomist. Anatomist used to have a VTK plugin, which allowed to mix VTK and Anatomist objects in a "hybrid" rendering engine. As users were not using this plugin, it fell into obsolescence. Mayavi 15  is a scientific data visualization library in Python language. It proposes a user application and is not dedicated to neuroimaging. In fact, its real added value is in displaying many kinds of plots or time series in a nice Python API, where, for 3D rendering, it relies on VTK. 3D Slicer 16  is 3D visualization software developed in C++ language, and is dedicated to medical imaging (although not precisely for brain imaging).
Its focus is on image processing; it features segmentation or registration algorithms, but also provides powerful 3D rendering features. It is also based on the VTK library. It provides a C++ API for extensions, but (in our knowledge) no scripting language API; thus, customization is more laborious than in Python language. The Human Brain Project 17  develops a web-based viewer, 18  which features displaying several species (humans, rats, mouses), each containing multiple atlases (for humans, the MNI ICBM152 template, the MNI Colin27 template, and the BigBrain multiscale atlas) with sets of regions (cortical layers, cortical areas, fiber bundles). The viewer includes a very efficient multiscale view engine, which supports zooming fluently into very large microscopic datasets (used for the BigBrain histological data).
Our visualization software library and application, Anatomist, qualifies, in our opinion, as a convenient and flexible solution to address this question, with many possibilities of extensions and customizations to build use case-oriented applications. Its efficient and scalable design allows the user to display large amounts of data, both individual data or atlases, with several navigation options (region based, coordinate position transformations, etc. ). This possibility can help the neuroimaging community to go beyond the classical "normalization and averaging" paradigm and to grasp the complex variability of human and non-human brain structures.
FUNDING This project has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreement No. 945539  ( HBP SGA3 ), from the  FRM   DIC20161236445 , from the  ANR IFOPASUBA and FOLDDICO , and from the  Blaise Pascal Chair of Region Ile de France and Université Paris Saclay  to WH.
DATA AVAILABILITY STATEMENT The programs codes can be found here: GitHub (https://github. com/brainvisa/anatomist-free, https://github. com/brainvisa/ana tomist-gpl, and https://github. com/mmancip/TiledAnatomist). The standard templates deformation graph is available on https:// search. kg. ebrains. eu/instances/Dataset/7a9aa738-a5b2-4601-818 e-05db2627ba5c.
Data collection and tagging procedure We used Acousonde 3B (www. acousonde. com) attached to females or calves via four suction cups, a non-invasive attachment system, to record sounds. We deployed tags from a 6. 40 m rigid motorboat using a 5 m handheld carbon-fiber pole. The boarding team consisted of staff experienced in successfully approaching mother-calf pairs: one operator (boat pilot), photographer, note-taker, and tagger. While the boarding team may change between outings, the team composition was always respected and each team member held one role within a whole outing. The tags were placed on the back, near the dorsal fin of the animal. Calves were tagged using one of the two approaches described in  Stimpert et al. (2012)  and in  Huetz et al. (2022)  in order to minimize disturbance to the mother-calf pair.
Vocal repertoire and acoustic analysis We downloaded the audio files from the tags as Ã. MT files, converted them to Ã. WAV format using GoldWave software (GoldWave Inc. , St. John's, Canada), and analyzed them using Avisoft SASLab Pro version 5. 207 (Avisoft Bioacoustics, Nordbahn, Germany). We produced spectrograms of the acoustic recordings using 1,024-point Fast Fourier Transform, 75% overlap, and Hamming window. Social calls that were clearly audible and distinguishable were aurally and visually identified, classified, and then compared with social call catalogues available in the literature  (Dunlop et al. , 2007; Dunlop, Cato & Noad, 2008; Zoidis et al. , 2008; Stimpert et al. , 2011; Rekdahl et al. , 2013 Rekdahl et al. , , 2017;; Fournet, Szabo & Mellinger, 2015; Epp, Fournet & Davoren, 2021; Indeck et al.
, 2021). Call types qualitatively similar to call types described in these catalogues (reference catalogue hereafter) were given the same name. New names were assigned for the remaining call types onomatopoeically, as the behavioral context or biological function of these new call types is still unknown and the main acoustic structure can be common to several call types (e. g. , several call types can be low-frequency upsweep calls). Distant song units could not be confused with social calls in our dataset as songs can be easily recognized by their well-organized temporal pattern. Additionally, we used 16 of the recorded male songs to determine if some social calls were similar to males' song units. For each identified social call, we measured nine temporal and spectral characteristics (Table  1 ).
Similar to  Dunlop et al. (2007) , we categorized social calls as either low-frequency sounds (LF), mid-frequency harmonic sounds (MF), high-frequency harmonic sounds (HF), amplitude modulated sounds (AM), noisy and complex sound (NC), or pulsed sounds (PS). LF corresponds to calls with peak frequency below 160 Hz. MF corresponds to calls with a peak frequency ranging from 170 to 550 Hz. HF corresponds to calls with a peak frequency above 700 Hz. AM corresponds to sounds consisting of a combination of long harmonic and amplitude modulated components with peak frequency ranging from 20 to 300 Hz. NC corresponds to broadband calls or harmonic calls with additional noise-like features. PS corresponded to low-frequency sounds repeated rhythmically. Representative spectrograms (Hamming window, FFT window size: 1,024 pts, 90% overlap) for the identified social calls were generated using the Seewave package  (Sueur, Aubin & Simonis, 2008)  in R software (R Core Team, 2021).
Statistical analysis To validate our aural-visual classification of calls by categories and by types, we performed random forest (RF) analyses  (Thiebault et al. , 2019; Indeck et al. , 2021)  using the randomForest  (Liaw & Wiener, 2002)  package in R. We calculated the global accuracy of the RF classification of calls by categories and by types (defined as accuracy = 1 -OOB, where OOB stands for out-of-bag error, the misclassification error rate) and computed the Gini index, which gives the importance of the variables used for the classification. We only included acoustic variables that were primarily measurable for most of the calls: Dur, Fmax, Q25, Q50, Q75, and PR. We included only call types for which we had at least six exemplars. The number of variables to be randomly selected at each split was set at 2 (as we had only six variables), and the number of trees grown was set at 500.
Tag deployments We performed 62 successful tag deployments (35 on calves and 27 on mothers) during the four years of data collection. Acoustic data were usable for 21 deployments (21 different mother-calf groups): seven deployments on mothers and 14 deployments on calves (Table  2 ). The other deployments were not analyzed due to the high background noise level or their short durations (less than 30 min). Background noise was mainly present when the tag was placed in a higher position close to the dorsal fin and thus often out of the water, especially on calves for which surfacing activities occurred very often. Of the 21 studied groups, we identified 12 as MC and nine as MCE. Three groups had a C1 calf, three had a C2 calf, and 15 had a C3 calf. We detected social calls in all of the studied groups.
"Whoop" (Fig. 2F ) was a long upsweep call starting with a flat Most of the LF sounds were harmonic-structure sounds, produced alone, except for boom and snort that can be produced in sequences. Drum sounds were always produced in series. Spectrogram parameters: Hamming window, FFT window size: 1,024 pts, 90% overlap. Generated using the Seewave package in R. Full-size  DOI: 10. 7717/peerj. 13785/fig-  1  part and fast ascending frequency. "Downsweep" (Fig. 2B ) was a long call showing a descending frequency slope with well-spaced harmonics. "Woohoo" (Fig. 2C ) was a long-duration call (i. e. , several seconds) showing a variable frequency-modulated pattern. "Trumpet" (Fig. 2D ) was a call produced alone or associated with "gru" or "slight snort". "Downsweep", "woohoo", and "trumpet" were found in humpback whale songs recorded around the study site.
Spectrogram parameters: Hamming window, FFT window size: 1,024 pts, 90% overlap. Generated using the Seewave package in R. Full-size  DOI: 10. 7717/peerj. 13785/fig- 4  defined main categories (OOB error rate = 7%, Table  3 ). The acoustic variables showing the highest importance for the classification were Q25, Fmax, and PR (Gini index: 10. 24, 10. 18, and 6. 93 respectively, Table  3 ). Most call categories showed low individual classification error rates. For classifying the calls by types, the RF showed a global accuracy of prediction of 77% (OOB error rate = 23%, Table  4 ). The acoustic variables showing the highest importance for classification were Fmax, Q25, and Dur (Gini index: 32. 10, 31. 78, and 26. 94 respectively, Table  4 ). Of the 17 calls included in the RF analysis, only four call types showed exceptionally high error rates (≥50%): gru, snort, thowp, and wop.
Figure1Spectrograms of low-frequency sounds (LF). (A) 100 Hz, (B) bass, (C) boom, (D) gru, (E) snort, (F) burp, (G) guttural sound, (H) thwop, (I) wop, (J) bark, (K) drum. Most of the LF sounds were harmonic-structure sounds, produced alone, except for boom and snort that can be produced in sequences. Drum sounds were always produced in series. Spectrogram parameters: Hamming window, FFT window size: 1,024 pts, 90% overlap. Generated using the Seewave package in R. Full-size  DOI: 10. 7717/peerj. 13785/fig-1.
Figure3Spectrograms of high-frequency sounds (HF). (A) Squeak, (B) ascending shriek. Squeaks were very short calls with frequencies above 1 kHz and ascending shrieks were among the longest social call types with the highest frequencies among all call types. Spectrogram parameters: Hamming window, FFT window size: 1,024 pts, 90% overlap. Generated using the Seewave package in R. Full-size  DOI: 10. 7717/peerj. 13785/fig-3.
Figure4Spectrograms of amplitude-modulated sounds (AM). (A) Door, (B) whine, (C) trill, (D) bug sound, (E) AM grunt. Except for AM grunts, AM sounds are long calls ranging from 1 to 5 s with a peak frequency between 100 and 400 Hz, produced in bouts of random durations. Whine, trill, and bug sounds were also found in humpback whale songs recorded around the study site. Spectrogram parameters: Hamming window, FFT window size: 1,024 pts, 90% overlap. Generated using the Seewave package in R. Full-size  DOI: 10. 7717/peerj. 13785/fig-4.
Level 3 CO/CO2 Data EMUS Level 3 data files contain ΣCO/CO 2 derived from EMUS observations. The algorithm is primarily intended for the U-OS1 observations, though it may be applied to U-OS2 observations as well, or any EMUS observation that views the disk of Mars. We note that the spatial resolution for other EMUS observation types is typically too coarse to derive useful data products. U-OS1 is comprised of two scans, each scan imaging a little more than half the Martian disk visible from EMUS' vantage point. U-OS2 observations consist of three scans, with one scan covering approximately two thirds of the Martian disk, while the first and last scans cover the edges of the disk. ΣCO/CO 2 and viewing geometry (e. g. , SZA, emission angle, etc. ) are contained in the Level 3 FITS files. Before ingestion by the ΣCO/CO 2 algorithm, Level 2 data may be spatially binned to increase signal to noise.
Validation The Mars Climate Database (MCD;  Forget et al. , 1999; González-Galindo et al. , 2015; Millour et al. , 2018)    10. 1029/2022GL099615 8 of 10 latitude). In both cases, MCD model values are calculated for all bins in the 2 dimensional grid, including those for which no EMUS data is available. For the L s -latitude image, a fixed longitude of 0° and local time of 10:00 hr was used for input to the MCD model, while for the L s -local time image the input longitude and latitude are set to 0°. The mean absolute magnitude of EMUS ΣCO/CO 2 is systematically larger than MCD by ∼40%. This discrepancy is at least partially due to a CO surface abundance of 800 ppmv assumed by MCD. This value is now considered to be low by 30-40% based on recent observations by the Nadir Occultation for MArs Discovery instrument suite (NOMAD;  Vandaele et al.
Summary EMUS' vantage point from high orbit provides large scale, high cadence, synoptic views of Mars. In this paper we have presented an overview of the EMUS ΣCO/CO 2 data product. Though the basic algorithm has a rich heritage of terrestrial applications in the form of ΣO/N 2 , some adjustments are required for application to the EMUS data set observed at Mars. We find that significant variability in CO brightness and relative column abundance is regularly observed in EMUS disk images. While the magnitude of EMUS ΣCO/CO 2 agrees with MCD 5. 3 (corrected for a systematically low estimate of surface CO abundance), EMUS observed variability of CO relative column abundance in local time, latitude, and solar longitude is generally inconsistent with MCD predictions.
is a climatological database derived from the Laboratoire de Météorologie Dynamique du CNRS General Circulation Model (LMD-GCM). EMUS ΣCO/CO 2 values are compared with those derived from MCD version 5. 3 as two dimensional maps in Figure 4. MCD column densities are calculated by generating density profiles using a solar minimum climatology. The top two panels show column density ratios as functions of L s and local time of observation points, while the bottom two panels show column density ratios as functions of L s and latitude (longitude and local time are held fixed because MCD variability with respect to these variables is relatively weak). For the L s -local time images, the average values of EMUS data grouped into bins 5° of L s and 1 hr local time are shown. For L s -latitude images, mean values from 5° L s to 10° of latitude bins are shown (between -90° and +90°.
Data Availability Statement The EMUS Level 2A data used in this study are available at the Emirate Mars Mission Science Data Center  (Amiri et al. , 2022; Holsclaw et al. , 2021)  at https://sdc. emiratesmarsmission. ae/data/emus. Users of EMUS disk data are reminded to carefully read documentation and release notes for detailed information regarding DQI flag values, limitations of the data, etc. The MAVEN IUVS processed (Level 1C) and derived (Level 2) data are publicly available in FITS format on the NASA Planetary Data System (PDS) at https://doi. org/10. 17189/1518964 (Schnieder, 2021b) and https://doi. org/10. 17189/1518956 (Schnieder, 2021a), respectively. MAVEN EUVM Level 2 data are available at the LASP MAVEN Science Data Center server  (Eparvier, 2022) , https://lasp. colorado. edu/maven/sdc/public/pages/search/search.
The ICA-based approach presented in this study thus provides an alternative or complementary approach to the state-of-the-art techniques that correct such artifacts during the orthorectification procedure  (Leprince, 2008; Leprince et al. , 2007; Shean et al. , 2016). We show in this study that the proposed approach outperforms the state-ofthe-art method, and allows the joint co-registration of images from different platforms and produces geometrically consistent data. Hereafter, we first briefly describe how 2-D and 3-D displacements can be measured from the correlation of orthorectified images and DEM differencing, which is an approach that is commonly adopted. We next present our alternative approach and compare the two approaches based on an application to the large dataset covering the epicentral area of the M w 7.
Proposed methodology Here, we propose an alternative end-to-end workflow to extract highquality surface deformation in 3D using multi-date and multi-platform high resolution (HR) optical imagery (Fig. 2 ). Part of the workflow uses the Co-registration of Optically Sensed Images and Correlation (COSI-Corr) methodology  (Leprince et al. , 2007) , which was incorporated and augmented in a custom python/C++ freely accessible package referred to as COSI-Corr + (geoCosiCorr3D). The software package allows: data preprocessing, physical geometry refinement, orthorectification, sub-pixel image correlation, 3D-displacement computation, and Geospatial Independent Component Analysis (geoICA). The COSI-Corr + code repository accompanying this manuscript contains processing libraries and additional documentation of this workflow.
In addition, we use the Ames Stereo Pipeline (ASP, version 2. 7. 0) for WorldView (WV) CCD correction  (Beyer et al. , 2018)  and MicMac for tie point detection  (Rupnik et al. , 2017). The set of input data required for our workflow includes the following: -A set of four images spanning the event, which are referred to as "3Dset", one base image (pre-event), and three target images (pre-event, post-event1, and post-event2). The current package version supports raw images with the accompanying physical model metadata. So far, we have tested this approach only with off-nadir angle images >8 •. However, it should be applicable too to data with smaller off-nadir angles, such as PlanetScope Doves images. -A reference DEM referred to as "rDEM". -Optional input: geo-referenced external image (e.
Next, a refined RSM-file that includes this correction is generated for each input image. This approach is different from the one described in  (Leprince et al. , 2007)  and currently implemented in the COSI-Corr software, where instead of optimizing the viewing parameters of a given set of GCPs, a new set of GCPs is estimated by correcting the ground coordinates of each GCP using the ground offset found between a reference and target orthorectified patches at each GCP location. One key advantage of our approach is that it makes bundle adjustment possible, therefore allowing to refine multiple images at the same time. In-situ measurement of a clearly identifiable feature on the image, a common practice to define control points, is expensive and may not be applicable depending on the accessibility of the area.
If no external data is available, a shaded DEM relief map is constructed with lighting corresponding to the sun azimuth and elevation angles at the time of the acquisition of each input image. The sun elevation and azimuth are extracted from the generated RSM-file, and the shaded DEM provides both horizontal and vertical coordinates. In this case, the location of the selected GCPs is very inaccurate, thus optimizing the GCPs with the original approach of COSI-Corr is recommended  (Leprince et al. , 2007).
Datasets We used a total of 90 HR satellite optical images out of which 37 were acquired before the earthquake and 53 after. The pre-earthquake images were collected between the 23rd of July 2016 and the 1st of September 2018, and the post-earthquakes images between the 6th of July 2019 and the 21st of January 2020. They were acquired by the Spot-6, WorldView-1 (WV1), WorldView-2 (WV2) and WorldView-3 (WV3) satellites with nominal pixel resolutions of 1. 5 m, 0. 46 m, 0. 46 m and 0. 31 m, respectively. The images used in this study and their characteristics are listed in Table  S. 1. For our analyses only panchromatic band were used. We used three different DEMs to assess the sensitivity to the  Furthermore, 113 orthorectified aerial photos with a resolution of 60-cm were used as reference images to extract GCPs and to assess the horizontal geolocation accuracy of the orthorectified satellite images.
Artifacts To identify the different sources of errors and the bias that could affect the displacement measurements, we orthorectified a 3D-set composed of four Digital Globe images (WV3 acquired on September 8, 2016, WV1 acquired on June 18, 2018, WV2 acquired on July 14, 2019 and WV1 acquired on June 21, 2020) at 60-cm ground sampling distance using the high-resolution DEMs (1-m GSD) produced from photogrammetry using data either before or after the event. Then, three horizontal surface displacement fields with 2. 6-m GSD were derived by correlating the base image (WV3) to the three target images (pre1-WV1, post1-WV2 and post2-WV1). The acquisition parameters of the images in this 3D-set and the correlation results are shown in Fig. 7. The measurements show strong linear stripe artifacts caused by misalignment of the CCD arrays, a common problem in push-broom imaging systems that generally use a sensor formed of staggered line arrays of CCD.
Comparison of displacements measured from 3-D correlation and state-of-the-art method The 3D surface displacement due to the Ridgecrest earthquake measured in previous studies by combining the standard 2-D workflow procedure with DEM differencing informally known as the "2. 5 D method", as outlined in section 2  (Antoine et al. , 2021; Barnhart et al. , 2020). In order to compare our approach to this more standard method, we have reproduced the displacement results presented in  Barnhart et al. (2020)  using two sets of Digital Globe stereo-pairs acquisitions. The first stereo-pair of WV-1 acquired on June 16, 2018, before the Ridgecrest earthquakes, and the second stereo-pair of WV-2 acquired after the earthquakes on July 14, 2019 (Table  S1 ). The two sets of stereo-data were processed separately to produce pre-and post-DSM at 2-m GSD using the OrthoEngine module of PCI Geomatics software (details on DEM extraction are provided in the supporting information).
Then, the misregistration between the two DEMs was reduced by extracting a sub-region located far from the surface ruptures. The horizontal surface displacement was retrieved using sub-pixel image correlation in the frequency domain between pre-and post-orthorectified images. For each date, the image with the smallest off-nadir angle (28. 6 • and 27. 6 • ) is orthorectified with the high-resolution preand post-DEM, respectively, with the purpose of limiting as much as possible the stereoscopic effects. The NS and EW horizontal displacements with 2. 4-m GSD are shown in (Fig. 10-a,b ). Ames Stereo Pipeline software is used to correct for Digital Globe images CCD misalignment, while the orthorectification and correlation processes are performed using COSI-Corr +. The vertical displacement is computed by simply differencing the two DEMs without accounting for the horizontal displacement.