dataesr/scientific-paragraphs-categorization huggingface dataset:
01 psu in regions of strong temperature gradients, such as the base of the mixed layer, but is negligible (< 0. 002 psu) elsewhere. The bias caused by conductivity cell sensor drift is the most significant error in Argo salinity. Some of this bias cannot be corrected, as severe sensor drift (and other CTD malfunctions) can cause data corruption that is beyond salvage. The remaining adjustable bias, ∂S, can be estimated by using the salinity adjustments that have been applied in delayed mode: ∂S = S raw -S adjusted , (1) where S raw values are the raw Argo measurements, and S adjusted values are the corresponding delayed-mode adjusted values. Here, we compute ∂S for each Argo vertical profile that has delayed-mode adjusted data, but we only use measurements deeper than 600 dbar to exclude the effects of the cell thermal inertia error.
Profiles with identifiable pressure error greater than 10 dbar (|P raw -P adjusted | > 10 dbar) are excluded to factor out the effects of pressure error on salinity. We consider the profiles with |∂S| < 0. 002 as good data that have not been affected significantly by sensor drift. Thus, the remaining ∂S represents the typical bias magnitude identified mostly from conductivity cell sensor drift. Here, a positive ∂S means the raw values are higher than true -or drifted towards saltier values (salty drift). Similarly, a negative ∂S means the raw values are lower than true -or drifted towards fresher values (fresh drift). Salty drift is the dominant mode of sensor drift in Argo salinity, with about 10 % of all Argo profiles having a positive adjustable bias (Fig. 1a , blue bars). Most of the physical causes of salty drift are unknown.
One known cause was determined to be due to the early deterioration of the encapsulant material in CTDs manufactured by Sea-Bird Scientific starting in 2015. Changes at the manufacturing level were introduced in 2018 to reduce such occurrences. The number of Argo profiles with adjustable salty drift increased steadily from 2000 and peaked in 2017-2018 at about 17 % of the annual profiles count. This 2017-2018 peak (Fig. 1a ), as well as the annual average of adjustable bias (Fig. 1b ), may shift slightly as more delayed-mode evaluated profiles become available in the future, but the present result is consistent with the timeline of the CTD encapsulant issue. On the other hand, fresh drift occurred more frequently in the early years of the Argo program (Fig. 1a , red bars), reaching a peak of about 28 % of annual profile count in 2001-2002.
The subsequent decline is broadly coincident with the introduction of the Iridium telecommunication system in 2005 for data communication. Fresh drifts are mostly caused by contamination of the CTD while the floats remain at the sea surface for communication with satellites. Earlier floats that used the ARGOS system, which was the predominant telecommunication system before Iridium, typically spent between 6 to 18 h at the sea surface for data telemetry. With Iridium, the time spent at the sea surface is reduced to about 30 min, thus reducing the risk of CTD contamination. The number of Argo profiles with adjustable fresh drift accounts for about 4 % of all Argo profiles. The magnitude of adjustable bias can be an indicator of sensor limitation. Amongst all the salinity profiles with adjustable sensor drift, salty or fresh, about 90 % have magnitude < 0.
03 (Fig. 2 ). Only 2 %-3 % of adjustable sensor drift have magnitude > 0. 05. Some of the larger-magnitude adjustments were concentrated in the Atlantic and the North Pacific in the early years of the Argo program before 2010 (Fig. 3 ), when delayed-mode efforts were focused in those areas that had more reference data and when delayed-mode operators had less experience evaluating larger-magnitude adjustments. Indeed, beyond the 0. 05 limit, salinity data with sensor drift usually show signs of unrecoverable damage, and applying such large adjustments to the exceptional cases should only be done with sound judgment. For the unrecoverable profiles, no adjustment is applied, and the data are flagged as bad in the Argo data files  (Wong et al. , 2022). These unadjustable salinity data (plus those corrupted by other CTD or float malfunctions) account for about 12 % of all Argo profiles.
As of time of analysis, about 54 % of Argo profiles were considered to be of good quality and with no identifiable bias, and about 20 % of Argo profiles remained in waiting for delayed-mode evaluation. The PARAM variables store the original raw measurements, while the PARAM_ADJUSTED variables store the corresponding evaluated/adjusted values. Both the raw data and the corresponding evaluated/adjusted data are available in the same Argo data files as a practice of good data stewardship. Since the evaluated/adjusted data are based on the original raw measurements, archiving of the original raw measurements is important to allow for checking of the data processing procedures. Therefore, the raw data are preserved as originally received to serve as a record if questions arise later. Argo data files that contain data evaluated/adjusted in delayed mode are denoted by DATA_MODE = "D".
4, the highest-quality Argo salinity data are those stored in the variables PSAL_ADJUSTED, with PSAL_ADJUSTED_QC = "1" and DATA_MODE = "D" ("delayed-mode"). Here, we evaluate the uncertainty in these highest-quality Argo delayed-mode salinity data from 2000 to 2021 by comparing them to the shipboard CTD reference database (CTD_for_DMQC). The CTD_for_DMQC reference database contains data from the World Ocean Database and the Global Ocean Ship-based Hydrographic Investigations Program (GO-SHIP), which are considered the best estimates of the true ocean salinity field. This same database is also used as part of the Argo delayed-mode salinity evaluation and adjustments (with some evaluation aided by a second reference database, Argo_for_DMQC). However, while the Argo delayed-mode process considers data from each float separately, this analysis considers data from all floats collectively.
Moreover, the CTD_for_DMQC reference database is enriched over time, and it may contain more data today than when the delayed-mode evaluation was done. We do note that this analysis may not satisfy the standard of a rigorous regression validation, where a completely independent dataset is needed. Nonetheless it provides a means to examine the uncertainties in the global Argo salinity dataset. This analysis was focused on Argo profiles that extended to 2000 dbar. Additional visual inspection was done on the delayed-mode salinity profiles to remove gross outliers that remained. These were generally contaminated profiles that had not been adjusted or flagged properly and amounted to < 1 % of the delayed-mode dataset as of the time of this analysis. The remaining Argo delayed-mode profiles and reference CTD profiles were grouped into grid squares of 10 • latitude by 10 • longitude.
The selected isotherms for estimating S Argo-refCTD typically have potential density anomalies σ 0 > 27. 6 kg m -3 in the equatorial Pacific, > 27. 7 in the equatorial Atlantic, and > 27. 8 south of 60 • S. Hence, these are deep water masses that do not show much decadal change. We speculate that this minor fresh skewness is instrument noise that has remained in the Argo delayed-mode dataset. During delayed-mode evaluation, it is often easier to identify strong sensor drifts than mild instrument calibration offsets, as the latter requires verification from contemporaneous, co-located reference data, which are often lacking. It is therefore possible that many mild instrument offsets, fresh or salty, have not been adjusted. The residual fresh bias is more apparent in regions such as the equatorial Pacific and Atlantic, where the deep T /S relations allow for easier delayed-mode adjustment of sensor drifts, which then emphasize the unadjusted fresh offsets.
In all Argo data files, both the raw data and the delayedmode data are available as a practice of good data stewardship. The delayed-mode data represent an improvement over the raw data because of the reduced bias, the detailed quality control flags, and the provision of uncertainty estimates. Scientific applications that are sensitive to salinity errors should therefore use the delayedmode data provided by the Argo program. When accessing data from Argo data files, the highest-quality Argo delayed-mode salinity data are obtained by selecting values in PSAL_ADJUSTED, with PSAL_ADJUSTED_QC = "1" and DATA_MODE = "D" ("delayed-mode"). We analyzed these highest quality Argo salinity data (as of April 2022) to 2000 dbar against a shipboard CTD reference database to assess their uncertainty. The statistical distribution of S Argo-refCTD , computed on isotherms with small salinity variance, showed mean and median values close to zero, suggesting good agreement on average between the selected Argo delayed-mode data and nearby reference CTD data.
The distribution had a kurtosis of 12. 5 and a skewness of +0. 1. Hence, it is not exactly a normal distribution, which has a kurtosis of 3 and a skewness of 0. We note that such statistics are dependent on sample sizes, and this analysis only accounts for about 20 % of all Argo delayed-mode salinity data as of April 2022, being limited by the availability of nearby reference CTD data. Our analysis of S Argo-refCTD shows that there are significant regional variations in the uncertainty of the Argo delayed-mode salinity dataset. In addition, there may be some residual bias that remains, possibly due to the diffi-culty in verifying small instrument calibration offsets in the absence of contemporaneous and co-located reference CTD data. These findings highlight several important points: 1. Even after delayed-mode evaluation and adjustment, some residual uncertainty can still remain in Argo salinity data.
Data Acquisition Following a study protocol approved by the Institutional Review Board, 24 healthy subjects (age = 35. 0 ± 10. 7 years; 18 females/6 males) participated in this study after written informed consent was obtained. All subjects were scanned on the C3T using a 32-channel brain coil (Nova Medical, Inc. , Wilmington, MA, USA). During the RS-fMRI scans, all subjects were instructed to remain still and fix their gaze on the center of a mirror mounted on the head coil. Independent measures of physiological variables (cardiac and respiration) were recorded. RS-fMRI experiments were conducted using a ME MB gradient-echo EPI sequence with blipped-controlled aliasing in parallel imaging technique  [10, 37]. The fMRI datasets were separately acquired for OE and ME (three echoes) acquisitions.
Data Preprocessing for fMRI Datasets The preprocessing procedures for RS-EPI datasets were all carried out using AFNI (Analysis of Functional NeuroImages, https://afni. nimh. nih. gov/, accessed on 26 March 2021)'s suite of programs  [41]. For preprocessing and artifact reduction of the RS-EPI data, the following steps were performed in sequence: truncation by initial ten volumes to yield steady-state magnetization, de-spiking ('3dDespike' in AFNI), physiological noise elimination including cardiac and respiratory artifacts ('RETROICOR' and 'RVT')  [42, 43] , slice acquisition timing correction ('3dTshift' in AFNI), time-series alignment, Legendre polynomial detrending and motion-and hardware-related linear regressions  [44] [45] [46] , in order. For the ME dataset, a T 2 * -weighted echo combination was performed between the time-series alignment and the detrending and the regressions  [4, 38, 47]  by using AFNI ('@compute_OC_weights').
All datasets were evaluated for artifacts due to abrupt head motion, passing the sudden motion detection of AFNI at the threshold level of 0. 2 mm for the Euclidean L2 norm of motion displacement during each TR interval  [45]. For functional connectivity (FC) estimation, the artifact-reduced residual time series were bandpass-filtered with a frequency range of 0. 009 to 0. 1 Hz and smoothed by a Gaussian kernel with a 4 mm full-width-at-half-maximum on volume dataset ('3dTproject' in AFNI). The preprocessed datasets were spatially normalized into the 2. 0-mm-isotropicresolution MNI template of 'MNI152_T1_2009c+tlrc. HEAD' by 'auto_tlrc' in AFNI. For FC investigation, the two ME datasets were concatenated after time-series z-score normalization as performed in the previous study  [48]  to render the final dataset with ~10-min length.
Explained Variance Evaluation To examine the change in signal variance using a model component, namely a regressor or echo combination, the marginal R 2 value was introduced to represent the proportion of the signal variance observed without individual preprocessing steps relative to the variance observed with all preprocessing steps, as performed in the previous study  [44]. The value of R 2 was evaluated from: R 2 step ≡ 1 - SS total SS step , (1) where SS total was the error sum of squares with all preprocessing steps, and SS step was the error sum of squares with all preprocessing steps except for a certain step. Without band-pass filtering and spatial smoothing, R 2 were evaluated for the conventional artifact-related regressors of the RETROICOR  [42] , ANATICOR  [44] , motion and global cerebrospinal fluid (CSF) signal, and for the T 2 * -weighted echo combination only in ME datasets.
For comparison between OE and ME acquisitions, ME datasets before the time-course scaling were used and the mean R 2 values were calculated in gray matter (GM) and white matter (WM) voxels across all subjects. To evaluate the dependence between artifact/noise removed by regressors and the T 2 * -weighted echo combination, the Pearson correlation coefficients were calculated. The numbers of regressors for RETROICOR  [42]  (up to 2nd order, including 5 regressors of RVT  [43] ), motion (six parameters for rigid-body motion and their first order derivative), and global-CSF artifacts (first 3 principal components of lateral ventricles) were thirteen, twelve and three for each dataset, respectively. The single time series for each voxel was collected as an ANATICOR regressor. The noise removed by the echo combination was estimated by subtraction between echo-combined and mid-echo-only time courses.
ROI-Based FC Analysis The effect of echo combination on signal intensity recovery was investigated for OE and ME datasets. A mask was created with the base EPI image of each dataset binarized by '3dAutomask' in AFNI and converted onto a 3D surface model of 'suma_TT_N27 provided in SUMA  [50]. The cortical coverage was visualized by overlapping each EPI mask with the gray matter on the corresponding anatomy image  [35]. The cortical coverage ratio was evaluated as the percentage of overlap between the averaged EPI masks across subjects and the gray matter on the surface model and  [35]. The effect of signal intensity enhancement of echo combination on FC was also investigated by ROI-based FC. 168 cortical parcellations for an individual anatomical image were defined as ROIs by FreeSurfer software  [51, 52].
The inter-ROI FC was evaluated as the Pearson correlation coefficient between the time courses of the two ROIs. Fisher-transformed FCs composed an FC matrix of 168 × 168. Due to regional signal dropout in images, which depended on the subject's anatomical shape and position against the B0 field, a paired Student's t-test for group comparison was applied to the FC matrices obtained from OE and ME datasets from 18 subjects. Inter-ROI FCs were thresholder with a statistically significant difference (uncorrected p < 0. 01) and a meaningful FC value (Fisher(r)) > 0. 3) in either OE or ME FCs, and the numbers of the inter-ROI FCs survived by the two thresholds were counted.
Seed-Based FC Map To examine seed-based FC extents, 10 ROIs adopted from AAL3v1  [53]  were selected as the seeds for the spatially normalized preprocessed datasets, including anterior cingulate cortex (ACC), insula (Ins), hippocampus (Hipp), posterior cingulate cortex (PCC), and precentral gyrus (PCG) on both hemispheres  [54, 55]. To evaluate an individual FC map, the Pearson correlation coefficients were calculated between the time courses of each voxel and the seed regions, which were converted into a z-score by Fisher transformation. Average FC maps for both OE and ME datasets were calculated by unpaired two-sample Student's t-tests, including one-sample tests by '3dttest++' in AFNI with a covariate of global correlation (GCOR)  [56]  and corrected by the FDR algorithm ('3dFDR' in AFNI).
ICA-Based FC Detection Independent component analysis (ICA)-based functional connectivity was investigated by using concat-ICA and dual regression methods. Concat-ICA was used to identify a set of independent component (IC) maps from the whole group of OE and ME datasets, which was performed by using the 'melodic' command in FSL with an option of 20 dimensions  [57] [58] [59]. Among 20 IC maps, 8 IC maps were identified as brain networks, including default mode network (DMN), lateral visual areas (LOcc), medial visual areas (MVis), right and left frontal-parietal networks (RFPN and LFPN), auditory network (Aud), executive control network (ECN), and sensorimotor network (Sens)  [57]. The whole-group IC maps were used as the inputs for dual regression and as the group truth with a statistical threshold to assess OE/ME group results derived by dual regression.
Dual regression was conducted by 'dual_regression' provided in FSL with the following two steps  [58] [59] [60]. The first step was to identify a subject-specific time course using spatial regression of the group IC maps derived by concat-ICA. The second step was to identify a subject-specific spatial map using temporal regression of the subject-specific time course. To investigate the scan time dependence, i. e. , the effect of scan time length on the sensitivity and specificity of ICA-derived brain network, each subject-specific dataset was truncated to time lengths ranging from 2 to 9 min at increments of 1 min. The same dual regression was applied to each dataset with different time lengths as the group IC maps. For a statistical comparison between the ME and the OE datasets, the OE/ME groups of individual spatial IC maps were tested by using 'randomise' command of the FSL's randomise permutation-testing tool (N = 5000).
In this study, whole-group IC maps were assumed to be the ground truth for brain networks  [57, 60]. OE-/ME-group IC maps derived from OE/ME datasets were assessed in terms of sensitivity, specificity, and accuracy. For the quantitative assessment, the OE-/ME-group IC maps were binarized by a threshold of p < 0. 001 to create the corresponding masks. The numbers of voxels were counted as true positives (TP), true negative (TN), false positive (FP), and false negative (FN) based on the OE-/ME-group IC map mask on the corresponding whole-group IC map mask. Sensitivity, 1-specificity (as false positive rate, FPR), and accuracy for each IC map were evaluated as followings, respectively. Sensitivity = TP TP + FN (2) False positive rate = 1 -TN TN + FP Accuracy = TP + TN TP + TN + FP + FN (3).
Data Selection for Analysis Among the total of 24 volunteers, two subjects had 70 and 48 time frames censored by the criterion in concatenated ME data and 22 and 32 in OE data, respectively, which were quite larger than the number of censored time frames of other subjects. The datasets from the subjects were considered to be contaminated with high motion  [61]  and excluded for subsequent analysis. Four volunteers were scanned only with OE fMRI, without ME fMRI. Thus, 22 sets of 11-min for OE datasets and 36 sets of 5-min for ME datasets (2 sets from 18 individual subjects) were used for further analysis. The average translational motion per TR across datasets were 0. 028 ± 0. 010 mm and 0. 027 ± 0. 010 mm for OE and ME acquisitions, respectively, which corresponded to 0. 033 ± 0. 012 mm and 0.
033 ± 0. 012 mm average motion per second, which was much smaller than the censoring criterion of 0. 2 mm. Time frames with motion exceeding 0. 2 mm were censored in each dataset. 1. 8 (±2. 7) and 1. 4 (±2. 4) of time frames were censored on average (±standard deviation) for OE and ME acquisitions, respectively. Thus, the average number of samples used was 949 ± 3 and 309 ± 2 for OE and ME session, respectively. After concatenation of ME datasets, all preprocessed datasets ended up with various data length ranging from 10 min 58 s and 9 min 26 s and they were all truncated to 9-min length for comparison purpose, where 772 and 575 time frames were used for further FC analysis of OE and ME acquisitions, respectively.
For echo combination, R 2 in WM tended to be slightly larger than in GM. The average translational motion per TR across datasets were 0. 028 ± 0. 010 mm and 0. 027 ± 0. 010 mm for OE and ME acquisitions, respectively, which corresponded to 0. 033 ± 0. 012 mm and 0. 033 ± 0. 012 mm average motion per second, which was much smaller than the censoring criterion of 0. 2 mm. Time frames with motion exceeding 0. 2 mm were censored in each dataset. 1. 8 (± 2. 7) and 1. 4 (± 2. 4) of time frames were censored on average (± standard deviation) for OE and ME acquisitions, respectively. Thus, the average number of samples used was 949 ± 3 and 309 ± 2 for OE and ME session, respectively. After concatenation of ME datasets, all preprocessed datasets ended up with various data length ranging from 10 min 58 s and 9 min 26 s and they were all truncated to 9-min length for comparison purpose, where 772 and 575 time frames were used for further FC analysis of OE and ME acquisitions, respectively.
For echo combination, R 2 in WM tended to be slightly larger than in GM. The reduced signal variance led to improve SNR and TSNR of ME acquisitions in Figure  1C. Note that TSNR0 and TSNR were evaluated without and with artifact regression processing, respectively. In terms of TSNR, the artifact regression was more effective with ME datasets than OE datasets. TSNR gains in ME verse OE acquisitions were approximately 1. 8 and 2. 0 times on GM and WM, respectively. The OE acquisition has the number of time frames 34% more than the ME fMRI (700 vs. 940 ms). Considering TSNRs with the numbers of time frames like TSNR• # o f time f rames  [13] , its gains in ME verse OE acquisitions were approximately 1. 56 and 1. 74 times on GM and WM in the same scan time, respectively. In terms of TSNR• # o f time f rames on GM, approximately 2.
ROI-Based FC Difference In Figure  2 , raw EPI images with multiple echoes were plotted with the corresponding anatomical images of a single subject. The T 2 * -weighted echo combination enhanced signal intensity effectively in brain regions with high-susceptibility as indicated. For group comparison, the cortical coverage ratio was visualized in anterior and inferior views on the standard surface model for OE and ME datasets in Figure  3A ,B, respectively. The overall cortical coverage ratio was improved with ME datasets. Specifically, medial orbital sulci (olfactory sulci) and anterior transverse collateral sulci on both hemispheres as indicated in Figure  3B  showed signal intensity improvement across almost all subjects, which were medial orbital sulcus and anterior transverse collateral sulcus defined as 'S_orbital_medolfact' and 'S_collat_transv_ant' in the reference  [52] , respectively.
Through the statistical comparison of 168-ROI-based FC matrices of OE and ME datasets, FC group difference of each ROI was visualized on the surface model as shown in Figure  3C. Four ROI regions, indicated by arrows in Figure  3C , had significant differences in functional connectivity, as well as in signal intensity. The locations of the four ROIs were consistent with the regions indicated in Figure  3B , where cortical coverage on the surface model was dramatically improved by echo combination. For visualization of the meaningful FCs from the four ROIs, i. e. , |Fisher(r)| > 0. 3, the BrainNet Viewer  [62]  was used to plot edges for inter-ROI functional connections derived from the four ROIs. In Figure  3D ,E, OE datasets showed a much smaller number of FCs from the red nodes than ME datasets.