dataesr/scientific-paragraphs-categorization huggingface dataset:
Pre-Processing fNIRS Data in Channel-Space For the purposes of demonstrating the quality of image reconstruction below, we used data from a TechEn CW7 system (12 sources and 24 detectors) with wavelengths of 690 and 830 nm. Fiber optic cables were used to carry data from the machine to a customized probe geometry of 40 channels covering bilateral frontal and parietal cortices [see Fig. 4(a) ]. fNIRS data in the examples provided here were pre-processed using the EasyNIRS package in Homer2. In the pipeline documentation on github, we also provide guidance for users of Homer3 and the *. snirf file format. Raw data were pruned to include channels with signals between 80 dB and 130 dB using the enPruneChannels function which prunes channels based on the signal to noise ratio. Next, these data were converted to optical density units using the hmrIntensity2OD function.
Motion artifacts were corrected using the hmrMotionCorrectPCArecurse function which used targeted principal components analysis  25  (tMotion = 1. 0, tMask = 1. 0, StdevThresh = 50 and AmpThresh = 0. 5). The corrected data were examined for uncorrected motion artifacts using hmrMotionArtifactByChannel (tMotion = 1. 0, tMask = 1. 0, StdevThresh = 50 and AmpThresh = 0. 5). If these uncorrected artifacts fell within -1 to 18 s of a stimulus trigger, that trigger was removed from further data processing using the enStimRejection function. Finally, these data were bandpass filtered using the hmrBandpassFilt function with high-pass and low-pass cutoff frequencies of 0. 016 and 0. 5 Hz, respectively. Relevant information from these processed *. nirs files were then used in subsequent steps.
Image Reconstruction and Verification The next step in the processing pipeline is image reconstruction. This step integrates the 3D light model created using AtlasViewer with the channel-based fNIRS data processed using Homer2 or Homer3. The image reconstruction is conducted in Matlab using NeuroDOT tools  17  (https:// github. com/WUSTL-ORL/NeuroDOT_Beta). In the sections below, we provide an overview of the image reconstruction approach including details for streamlining processing steps between AtlasViewer, Homer2/Homer3, and NeuroDOT. We then show sample image-reconstructed data plotted relative to the channel-based fNIRS data.
Regularization parameters of λ 1 ¼ 0. 01 and λ 2 ¼ 0. 1 work well for a broad range of geometries and array designs. These steps are all carried out within Tikhonov_invert_Amat. m. We also recommend using at least a modest amount of direct spatial smoothing using a Gaussian smoothing kernel to control for speckly noise in the images. Recommended smoothing kernel sizes range from 3 to 10 mm at their full width at half max. The optical data are reconstructed into the voxelated space using the NeuroDOT function reconstruct_img. Relative changes in oxygenated (HbO 2 ), deoxygenated (HbR), and total hemoglobin (HbT) concentrations are obtained from the reconstructed absorption coefficients by spectral decomposition of the extinction coefficients of HbO 2 and HbR at the two wavelengths. 32, 33 Conversion from relative changes in absorption coefficients to HbO 2 and HbR is performed with the NeuroDOT function spectroscopy_img.
Individual-Level Analysis of Data (General Linear Model) Once the participant fNIRS data have been reconstructed, a typical approach is to model these time series data using GLM. For our test dataset, brain responses were analyzed using a GLM run with the NeuroDOT function GLM_181206. We used a hemodynamic response function (HRF) derived from DOT data as it has been shown to be a better fit for optical data than those derived from fMRI. 34 The same HRF was used for HbO 2 and HbR responses. Each event was modeled with a 10 s extent and variable inter-trial intervals with an exponentially distributed set of durations. We focused our analyses over the window from one second before each stimulus onset to 20 s post onset. To control for variability in the number of events per run and per participant, we computed a weighted average of the betas over runs, weighted by the number of events per condition in each run.
Demonstration of the Pipeline on an Example Dataset from NIRSSport To demonstrate the type of data the processing pipeline yields, we report sample results here from a second test dataset taken using a NIRSSport (NIRx) system. This has the added utility of showing that our pipeline generalizes across NIRS systems (as the prior examples in this report used a TechEn system). The data presented in this case study are part of a longitudinal study investigating the effects of schooling on neurocognitive function. 36.
Exerimental Task VWM performance was measured as children took part in a color change detection task. 37 The experimenter explained the task to the children using 3 × 3 inch flash cards. Once the experimenter was satisfied that the children understood the rules, they moved to the experiment. The experimental task was run in E-prime V. 3 software on an HP laptop with a 14-inch screen. In each trial, a fixation appeared on either side of the screen to orient the child to the screen. Then, a memory array of colored squares was presented for 2 s, followed by a blank screen for 1 s (delay period). Then, a test array of colored squares was presented and stayed on the screen until a response was made. After the presentation of the test array, the child was asked whether the colored squares were same or different between the memory and test arrays.
fNIRS Data Acquisition An NIRX system with eight sources and eight detectors was used to collect neuroimaging data from children as they engaged in the VWM task. Data were collected at 7. 81 Hz and the wavelengths of the sources was 760 and 850 nm. Probe geometry with fourteen channels overlaying the bilateral frontal and parietal areas [see Fig. 7(a) ] was constructed based on regions of interest from previous fMRI VWM literature. 38 The head circumference of the child was measured and a suitable cap was chosen. Four cap sizes were used across all children and source-detector distances were scaled according to cap size (50 cm: 2. 5 cm, 52 cm: 2. 6 cm, 54 cm: 2. 7 cm and 56 cm: 2. 8 cm). The distances from the NZ to inion and between the peri-auricular points were measured to ensure that the cap was centrally placed on the child's head.
fNIRS Data Processing The probe geometry was digitized to obtain the coordinates of sources and detectors for each of the four capsizes. A 4. 5-year-old MRI atlas was obtained from the Neurodevelopmental MRI database  [39] [40] [41] [42] [43]  and segmented into four tissue types (scalp, CSF, gray matter, and white matter). Monte Carlo simulations were run with 1 million photons to generate sensitivity profiles for channels for each capsize (step 2, see Fig. 1 ). The head volume and sensitivity profiles were converted into NIFTI format. Next, channel-based fNIRS raw data were pre-processed, corrected for motion artifacts, filtered, and converted to optical density units. Data were also corrected for superficial scalp artifacts using global signal regression (step 3). These channel-based fNIRS optical density data were integrated with the volumetric sensitivity profiles using image reconstruction as described previously (step 4).
Next, these individual-level beta maps were registered to the MNI space (step 6). For Group analyses, the beta maps were entered into a linear mixed effects model (using 3dLME function in AFNI) with within-subjects factors of load, trial type, and chromophore (HbO and HbR). Note that only correct trials were used for our analyses. For the current case-study, we focused on the interaction between load and chromophore to examine whether HbO activation in significant clusters show the canonical trend of increasing activation with increasing VWM load. Only those voxels that contained data from 60% of the subjects were included for further analyses. To control the family-wise error in our data, we used the 3dFWHMx function in AFNI to estimate the empirical ACF in our fNIRS data and fit a mixed ACF model to this function.
In the present paper, we described a flexible pipeline with accompanying code to push through these complexities along with initial verification data acquired from different fNIRS systems. In the present report, we demonstrated that with fNIRS data and both individual anatomical MRI images and templates, fNIRS data can be reconstructed in a manner that correlates with the original channel-based data and is analyzable in voxel space. The accompanying publicly available code can be adapted by users for their own data requirements. The code also contains additional functionality not discussed in the current paper, such as functions to cross-check GLM results against the time-course data from a given region. We also demonstrated with datasets from child fNIRS studies that the analyses and reconstruction methods are reliable and correspond with the measurements taken in channel space.
We stress that users should verify their data as they move through the pipeline to understand what effect these decisions have on the data and to make sure, for instance, that the sensitivity profile created after photon migration simulations matches the locations of optodes. Finally, we note that the photon migration method we used works best with a high-performance computing cluster. To reduce this overhead, we recommend the use of MCX. 9  The pipeline presented here contains several important contributions to the field. In the digitizeR package for the R language, we present a novel method of creating digitization templates and preparing them for use with other software. In addition, we provide code, suggestions, and guidelines for the cleaning and segmenting of individual MRIs to turn individual anatomical data into a brain model suitable for use in the creation of a light model.
We then used a combination of existing software to demonstrate how to create a light model, perform a GLM, and move data into group space for analysis. While some individual aspects of this pipeline, such as the creation of a light model, feature in a number of software packages, these have not previously been combined in a way that allows users to go from raw data to final group analyses as reported here. The pipeline reported here, therefore, provides a roadmap of the functionality that users require to take their data all the way through analysis. As such, we hope this paper will be of use to software developers and others interested in creating standardization of analysis approaches within a rapidly developing field. https://github. com/samhforbes/digitizeR. Data used in this paper can be made available upon request to the authors.
Acknowledgments This work supported by Grant No. OPP1164153  from the  Bill & Melinda Gates Foundation  under Grant No. R01HD083287  from the  National Institutes of Health. Both grants were awarded to J. P. Spencer. The authors are grateful for the contributions of  Aaron Buss  and  Jessica Defenderfer  for their comments during testing of the pipeline. We also thank  Meryem Yücel  and  Stephen Tucker  for their help making the pipeline compatible with Homer3 and the *. snirf format.
Data synthesis The paleoclimate data synthesis used in this study was extracted from the publicly available PACMEDY Coral&Bivalve database_v1 which gathers fossil coral and bivalve high resolution geochemical records of the Holocene and late glacial period  (Carr e et al. , 2021). The synthesis includes Sr/Ca and isotopic (d 18 O) records from fossil corals and bivalves from the tropical Pacific that meet the following criteria: (1) the fossil record is dated to some interval in the past 10,000 years, (2) the temporal resolution of the fossil record is at least 3 months, (3) there is a modern record at least 15 years long obtained from the same species in the same locality. 73 coral and bivalve records from the tropical Pacific were retrieved from public repositories or provided by the authors (Table  1 ).
We do not include microatoll records, which are thought to reflect conditions of the lagoon instead of the open ocean. Sr/Ca records were included. Annually resolved records (or lower resolution) were not included because of the focus on seasonality in this study and potential seasonal biases in low resolution records  (Carr e and Cheddadi, 2017). Sr/Ca and d 18 O records obtained from the same coral are considered as different records. The Eastern tropical Pacific records are groups of short-lived bivalve shells from the same deposit  (Carr e et al. , 2014). All datasets have been previously published (Table  1 ). A few of the datasets were digitised from published figures when the original data files were unavailable. All datasets were interpolated to a constant time resolution (1 month or 3 months depending on the original resolution).
The distribution of data in time and space is heterogeneous (Fig. 1b ). Two thirds of the data are from the CP with 1312 years recorded. Both numbers of records and length of time covered are lower in other regions: EP (214 years), WP (245 years), and SWP (443 years). The CP box partially overlaps with the Niño3. 4 box. Located in the northern hemisphere, these records may be sensitive to the northward extent of La Niña and El Niño anomalies. No data are available from the Niño3 or the Niño1þ2 box. The data are most abundant for the last millennium and the mid-to late Holocene (7e6 and 5e3 ka). The abundance in the mid-and late Holocene reflects the accessibility of emerged fossil reefs in the CP and SWP regions as a result of the mid-Holocene sea-level highstand  (Grossman et al. , 1998; Lambeck and Nakada, 1990; Pirazzoli and Montaggioni, 1988; Woodroffe and Horton, 2005).
Description of the AWI-ESM2 simulation AWI-ESM2 is an extension of the AWI climate model version 2 (AWI-CM2)  (Sidorenko et al. , 2019)  for earth system modeling (https://fesom. de/models/awi-esm/). The atmospheric module is represented by the general circulation model ECHAM6  (Stevens et al. , 2013)  (here in T63 horizontal resolution, ~180 km) including a land surface model (JSBACH) which is based on a tiling of the land surface and includes dynamic vegetation with 12 plant functional types and two types of bare surface  (Reick et al. , 2013). The ocean model is based on FESOM2  (Danilov et al. , 2017). This model has been used in a number of applications (e. g. ,  Lohmann et al. , 2020; Vorrath et al. , 2020; Kageyama et al. , 2021). The current version of FESOM2 is available from the public GitHub repository at https://github.
This standard error primarily depends on the length of the record, and the amplitude of the variability  (Carr e et al. , 2012a). Uncertainties for modern reference records were calculated using instrumental SSTs from the NOAA ERSST. v5 monthly dataset  (Huang et al. , 2017). For each record, the 1900e2017 instrumental SST time series was calculated as the average of the nine grid points around the site location. The instrumental record is too short to estimate the uncertainties for fossil records, which sometimes exceed 100 years in length. We therefore used monthly SSTs from the last 1000 years of a transient simulation averaged over the region in which the record was located. We arbitrarily used the IPSL-CM5 simulation for this, but since the amplitude of multidecadal to centennial modulation of ENSO is similar across models (see result section 4.
Interannual variability Interannual variability is reduced in the WP, CP and EP during of the whole Holocene compared to the post-1900 CE interval, indicating that ENSO activity is anomalously high in the 20th century (Fig. 3 ). The trends in interannual variability in EP and CP are coherent to first order. A statistically significant minimum variability occurs between 3 to 6ka; interannual variability before 6ka is not significantly different from that in the late Holocene. The timing of the HEM is poorly constrained because of the sparsity of data but may have lasted 2000e3000 years. All the records point to stronger ENSO activity after 3ka compared to 4-6ka, but the transition is only recorded in the CP, where it appears gradual. In the WP, the interannual variability is similar to modern by 2ka, but strongly reduced before that.
Therefore, the var(EP)/var(CP) ratio and the var(E)/var(C) ratio, while well correlated in the instrumental record (Fig. 2 ), seem to represent slightly differently the spatial changes of ENSO in the last 6000 years in the models, possibly due to the representation of the easternmost tropical Pacific. In observations, the variability of the EP region is likely influenced by coastal Niño events and by the dynamic of the coastal upwelling, while the CP region may not reflect well enough the variability further west. Changes in ENSO flavours since the mid-Holocene are not robust across models, and thus provide no evidence of forced changes. While imperfect, the var(EP)/var(CP) ratio estimated from marine records seems a promising approach to examine changes in the spatial expression of ENSO, provided the uncertainty can be reduced, in particular by increasing the dataset in the eastern Pacific.
0Global Change 2. 0: Unlocking the past for a clearer future, grant number 694481and from the JPI-Belmont project PACMEDY (via NERC). The distribution and analyses of the transient Holocene simulations with the IPSL model benefit from the Institut Pierre Simon Laplace Climate Modeling Centre Infrastructure supported by ANR "Investissements d'avenir" program ANR-11-IDEX-0004-17-EURE-0006.
Acknowledgements We acknowledge the modelling groups of  Institut Pierre Simon Laplace, Alfred Wegener Institute and Max Planck Institute  for producing and sharing the climate simulations. We are thankful to all the authors who produced the paleoclimate records gathered in this paper for making their data public or sharing them with us. This research was funded by the  JPI-Belmont Forum  project entitled  Palaeo-climate Constraints on Monsoon Evolution and Dynamics  (PACMEDY). We thank two anonymous reviewers for their constructive comments.
Funding This research has been supported by the  JPI-Belmont  project  PACMEDY (via  grant  ANR-15-JCLI-0003-01  for MC, PB, ME, OM, TC, and BT). MC, JC, DO, RSG, AP, and PR acknowledge funding from  Concejo Nacional de Ciencia y Tecnologia de Peru  (grant n  007-2017-FONDECYT , grant n  034-2019-FONDECYT-BM ). SPH.
Data and code availability The complete PACMEDY_Coral&Bivalve_database is available at https://doi. org/10. 6084/m9. figshare. 14256434. The Pacific synthesis extracted from this database, the interannual variability and seasonality values calculated from proxy records and simulations, as well as the Matlab scripts for analyses and figures are available in a public repository at https://doi. org/10. 6084/m9. figshare. 14273381.
ANIMAL DATA SHARING Some large data repositories (e. g. , for brain studies, The MJ Fox Parkinson's database or the Human Connectome project) and specific architectures (e. g. , COINS, LONI) are now available for human population imaging. The animal imaging community has also growing requirements for multicenter studies, for example to allow the comparison of academic results as in brain connectivity studies  (Grandjean et al. , 2020)  or to characterize the effects of drugs  (Bruns et al. , 2015). To share preclinical imaging data and data analysis pipelines, only few tools are available that take into account the specificities of animal studies  (Liu et al. , 2020; Messinger et al. , 2020) , and few studies aim at standardization of acquisition and post-processing techniques. Kain et al.
describe a solution, Small Animal Shanoir (SAS), for the management of imaging data and metadata. SAS is a preclinical extension of a cloud-based solution dedicated to the management of human brain imaging repositories, Shanoir  (Barillot et al. , 2016). The main feature of this working solution is to rely on a core ontology, OntoNeurolog, which allows for the federation of different local databases via the mapping of their corresponding data models to the ontology, and facilitates its extension, for instance for managing preclinical studies. Additionally, to reinforce its extensibility capacity, SAS is designed as a set of independent micro-services. Then, a specific micro-service, Dicomifier, is dedicated to the transformation to Nifti format, widely used by several neuroimaging pipelines, of raw files in Bruker format, Bruker being a manufacturer of preclinical MR scanners, or of Dicom files.
Associated data acquisition parameters are kept under a json file associated to each Nifti file stored in SAS. The web-oriented architecture allows for querying and retrieving stored images and processing pipelines. A data transfer module can interface the data management system to computing platforms for pipelines execution and the storage of image processing results. Specific authentication mechanisms allow for the fine control of data access, from an access restricted to a specific user's community to a publicly access for promoting open science. SAS was used for the project described by  Deruelle et al. in this Research Topic. Mandino et al. review  the efforts done by the animal MRI community toward the standardization of data acquisition and analysis procedures in the context of whole brain functional MRI; a key aspect for animal population imaging via multicenter studies.
Based on their 868 research papers analysis, they showed that animal studies (mainly on rats and Sprague Dawley strain, in general carried out on 10 subjects at 7T and 9. 4T) were underpowered and the false-positive rate incorrectly controlled, similarly to human studies  (Button et al. , 2013; Eklund et al. , 2016). Several sources of variations among studies, from animal preparation and anesthesia to the use of ad-hoc pipelines or/and ad-hoc templates, hamper the comparison of published results. The authors propose guidelines to improve data sharing and reproducibility. They emphasize the importance of raw datasets sharing for data re-analysis with other processing pipelines allowing results comparison between studies, the adoption of standard templates for the reporting of results (e. g.
PIPELINES COMPOSITION AND PIPELINE SHARING As shown by Mandino et al. in their of the small animal literature, a source of difficulty for comparing results in animal studies is the absence of a core of validated solutions for data processing and analysis, similarly to what is available for human neuroimaging. Four papers address this point. To facilitate the sharing of raw MR brain imaging data, Ioanas et al. present a tool to transform files in a proprietary format (Bruker files) to Bids format that has been proposed for human neuroimaging studies  (Gorgolewski et al. , 2016). The workflow is implemented as a function, bru2bids, written in Python. In the same vein, Celestine et al. propose the Python package Samba-MRI to preprocess, register to templates, perform functional analysis, and perfusion measures from raw MR brain imaging datasets.
It reuses several neuroimaging python libraries (e. g. , Nipype, Nibabel or Nilearn) and incorporates additional features for group-wise registration or inter-modality registration. The code is available via the open GitHub platform. One can mention here the recent work of  Brossard et al. (2020)  who introduce a package to design pipelines and obtain multiparametric MRI maps that was extensively evaluated at the preclinical level. The papers from Groeneboom et al. and Yates et al. propose software for analyzing histological rodent brain images. Indeed, the recent in vitro imaging systems provide large collections of highresolution images that raise specific computational problems for memory management and time execution. The former, Nutil, allows to automatize image processing and analysis of 2D brain histological sections.