dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion and conclusion NeuroQuery makes it easy to perform meta-analyses of arbitrary questions on the human neuroscience literature: it uses a full-text description of the question and the studies and it provides an online query interface with a rich database of studies. For this, it departs from existing meta-analytic frameworks by treating meta-analysis as a prediction problem. It describes neuroscience concepts of interest by continuous combinations of terms rather than matching publications for exact terms. As it combines multiple terms and interpolates between available studies, it extends the scope of meta-analysis in neuroimaging. In particular, it can capture information for concepts studied much less frequently than those that are covered by current automated meta-analytic approaches.
Related work A variety of prior works have paved the way for NeuroQuery. Brainmap  [Laird et al. , 2005]  was the first systematic database of brain coordinates. NeuroSynth  [Yarkoni et al. , 2011]  pioneered automated metaanalysis using abstracts from the literature, broadening a lot the set of terms for which the consistency of reported locations can be tested. These works perform classic meta-analysis, which considers terms in isolation, unlike NeuroQuery. Topic models have also been used to find relationships across terms used in meta-analysis. Nielsen et al. [2004]  used a non-negative matrix factorization on the matrix of occurrences of terms for each brain location (voxel): their model outputs a set of seven spatial networks associated with cognitive topics, described as weighted combinations of terms.
If the selected term is not popular enough, the resulting map also risks being unusable for lack of statistical power. NeuroQuery provides statistical maps for arbitrary queries -from seldom-studied terms to free-text descriptions of experimental protocols. Thus, it enables applying fully-automated and quantitative meta-analysis in situations where only semi-manual and subjective solutions were available. It therefore brings an important advancement towards grounding neuroscience on quantitative knowledge representations. The dataset collected by NeuroSynth  [Yarkoni et al. , 2011]  is openly available 3. In July, 2019, Neu-roSynth contains 448 255 unique locations for 14 371 studies. It also contains the term frequencies for 3228 terms (1335 are actually used in the NeuroSynth online tool 4 ), based on the abstracts of the studies.
Moreover, as publications cannot always be redistributed for copyright reasons, NeuroSynth (and any dataset of this nature) can only provide term frequencies for a fixed vocabulary, and not the text they were extracted from. We therefore decided to collect a new corpus of neuroimaging studies, which contains the full text. We also created a new peak activation coordinate extraction system, which achieved a higher precision and recall than NeuroSynth's on a small sample of manually annotated studies.
A. 2 Journal articles in a uniform and validated format We downloaded around 149 000 full-text journal articles related to neuroimaging from the PubMed Central 5   [Sayers, 2009]  and Elsevier 6  APIs. We focus on these sources of data because they provide many articles in a structured format. It should be noted that this could result in a selection bias, as some scientific journals -mostly paid journals -are not available through these channels. The articles are selected by querying the ESearch Entrez utility  [Sayers, 2009]  either for specific neuroimaging journals or with query strings such as "fMRI". The resulting studies are mostly based on fMRI experiments, but the dataset also contains Positron Emission Tomography (PET) or structural Magnetic Resonance Imaging (MRI) studies. It contains studies about diverse types of populations: healthy adults, patients, elderly, children.
However, differences between the two coordinate systems are at most of the order of 1 cm, and much smaller in most of the brain. This is comparable to the size of the Gaussian kernel used to smooth images. Moreover, the alignment of brain images does not only depend on the used template but also on the registration method, and there is no perfect transformation from Talairach to MNI space  [Lancaster et al. , 2007]. Therefore, treating all coordinates uniformly is acceptable as a first approximation, but better handling of Talairach coordinates is a clear direction for improving the NeuroQuery dataset. Coordinate extraction evaluation. To evaluate the coordinate extraction process, we focused on articles that are present in both NeuroSynth's dataset and NeuroQuery's, and for which the two coordinate extraction systems disagree.
Out of 8 692 articles in the intersection of both corpora, the extracted coordinates differ (for at least one coordinate) in 1 961 (i. e. in 23% of articles). We selected the first 40 articles (sorted by PubMed ID) and manually evaluated the extracted coordinates. As shown in Table  2 , our method extracted false coordinates from fewer articles: 3 / 40 articles have at least one false location in our dataset, against 20 for NeuroSynth. While these numbers may seem high, note that errors are far less likely to occur in articles for which both methods extract exactly the same locations.
A. 6 Summary of collected data The data collection described in this section provides us with important resources: i) Over 149K fulltext journal articles related to neuroscience -13. 5K of which contain peak activation coordinates -all translated into the same structured format and validated. ii) Over 418K peak activation coordinates for more than 13. 5K articles. iii) A vocabulary of 7547 terms related to neuroscience, each occurring in at least 6 articles from which we extracted coordinates. This dataset is the largest of its kind. In what follows we focus on the set of 13. 5K articles from which we extracted peak locations. Some quantitative aspects of the NeuroQuery and NeuroSynth datasets are summarized in Table  3. Text. In terms of raw amount of text, this corpus is 20 times larger than NeuroSynth's.
Combined with our vocabulary, it yields over 5. 5M occurrences of a unique term in an article. This is over 5 times more than the word occurrence counts distributed by NeuroSynth 9. When considering only terms in NeuroSynth's vocabulary, the corpus still contains over 3M term-study associations, 4. 6 times more than NeuroSynth. Using this larger corpus results in denser representations, higher statistical power, and coverage of a wider vocabulary. There is an important overlap between the selected studies: 8 692 studies are present in both datasets -the Intersection Over Union is 0. 45.
B Methodological details B. 1 Notation We denote scalars, vectors and matrices with lower-case, bold lower-case, and bold-upper case letters respectively: x, x, X. We denote the elements of X by x i,j , its rows by x i , and its columns by x * ,i. We denote p the number of voxels in the brain, v the size of the vocabulary, and n the number of studies in the dataset. We use indices i, j, k to indicate indexing samples (studies), features (terms), and outputs (voxels) respectively. We use a hat to denote estimated values, e. g. B. x, y is the vector scalar product.
Figure2: Illustration: studying the contrast "Read pseudo words vs. consonant strings". Left: Group-level map from the IBC dataset for the contrast "Read pseudo-words vs. consonant strings" and contour of NeuroQuery map obtained from this query. The NeuroQuery map was obtained directly from the contrast description in the dataset's documentation, without needing to manually select studies for the meta-analysis nor convert this description to a string pattern usable by existing automatic meta-analysis tools. The map from which the contour is drawn, as well as a NeuroQuery map for the page-long description of the RSVP language task, are shown in Appendix C. 1, in Appendix C. 1c and Appendix C. 1d respectively. Right: ALE map for 29 studies that contain all terms from the IBC contrast description.
Building the NeuroQuery training data 18 A. 1 A new dataset. 18 A. 2 Journal articles in a uniform and validated format. 19 A. 3 Coordinate extraction. 19 A. 4 Density maps. 20 A. 5 Vocabulary and TFIDF features. 20 A. 6 Summary of collected data. 21 B Methodological details 22 B. 1 Notation. 22 B. 2 TFIDF feature extraction. 22 B. 3 Reweighted ridge matrix and feature (vocabulary) selection. 22 B. 4 Smoothing: regularization at test time. 25 C Additional details on validation experiments 27 C. 1 Example Meta-analysis results for the RSVP language task from the IBC dataset. 27 C. 2 NeuroQuery performance on unseen pairs of terms. 27 C. 3 NeuroQuery prediction performance without anatomical terms. 28 C. 4 Variable terminology. 29 C. 5 Comparison with the BrainPedia IBMA study. 30 C. 6 Comparison with Harvard-Oxford anatomical atlas.
Acknowledgements : JD acknowledges funding from  Digiteo  under project  Metacog  ( 2016-1270D ). TY acknowledges funding from  NIH  under grant number  R01MH096906. BT received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreement No. 785907  ( HBP SGA2 ) and No 826421 (VirtualbrainCloud). FS acknowledges funding from  ANR  via grant  ANR-16-CE23-0007-01  ("DICOS"). GV was partially funded by the  Canada First Research Excellence Fund , awarded to  McGill University for the Healthy Brains for Healthy Lives initiative. We also thank the reviewers, including  Tor D. Wager , for their suggestions that improved the manuscript.
AF Building the NeuroQuery training data 18 A. 1 A new dataset. 18 A. 2 Journal articles in a uniform and validated format. 19 A. 3 Coordinate extraction. 19 A. 4 Density maps. 20 A. 5 Vocabulary and TFIDF features. 20 A. 6 Summary of collected data. 21 B Methodological details 22 B. 1 Notation. 22 B. 2 TFIDF feature extraction. 22 B. 3 Reweighted ridge matrix and feature (vocabulary) selection. 22 B. 4 Smoothing: regularization at test time. 25 C Additional details on validation experiments 27 C. 1 Example Meta-analysis results for the RSVP language task from the IBC dataset. 27 C. 2 NeuroQuery performance on unseen pairs of terms. 27 C. 3 NeuroQuery prediction performance without anatomical terms. 28 C. 4 Variable terminology. 29 C. 5 Comparison with the BrainPedia IBMA study. 30 C. 6 Comparison with Harvard-Oxford anatomical atlas.
Abstract. We have analyzed TROPOspheric Monitoring Instrument (TROPOMI) carbon monoxide (CO) data acquired between November 2017 and March 2019 with respect to other satellite (MOPITT, Measurement Of Pollution In The Troposphere) and airborne (ATom, Atmospheric Tomography mission) datasets to better understand TROPOMI's contribution to the global tropospheric  CO record (2000 to present). MOPITT and TROPOMI are two of only a few satellite instruments to ever derive CO from solar-reflected radiances. Therefore, it is particularly important to understand how these two datasets compare. Our results indicate that TROPOMI CO retrievals over land show excellent agreement with respect to MOPITT: relative biases and their SD (i. e. , accuracy and precision) are on average -3. 73 % ± 11. 51 %, -2. 24 % ± 12.
, 2011)  stations for selected dates between 9 November 2017 and 4 January 2018; good agreement between the two datasets was found, with the TROPOMI CO product well within the mission requirements  (Borsdorff et al. , 2018b). Here we analyze daily global TROPOMI retrievals acquired between 7 November 2017 and 10 March 2019 with respect to MOPITT and ATom. MOPITT is the only currently operating satellite instrument deriving CO from near-infrared (NIR), thermal-infrared (TIR), and multispectral (TIR + NIR) radiances; also, it has the longest global CO record to date  (2000-present). TROPOMI was, until recently, the only other operative satellite instrument retrieving CO from NIR measurements. ENVISAT SCIAMACHY (2002 -2012; Bovensmann et al. , 1999)  and  GOSAT-2 TANSO-FTS-2 (since 2019; NIES, 2019)  are two other instances.
Thus, understanding how MO-PITT and TROPOMI retrievals compare to each other is important. MOPITT results are systematically validated using airborne vertical profiles  (Deeter et al. , 2019, and references therein)  and ground measurements  (Buchholz et al. , 2017; Hedelius et al. , 2019) , as well as being compared to other satellite datasets  (Worden et al. , 2013a; Martínez-Alonso et al. , 2014; George et al. , 2015). Thus, its continuity and consistency are well-understood. Despite the low reflectivity of open water, TROPOMI CO retrievals over bodies of water are possible if clouds are present. In these cases partial CO columns under the cloud tops are approximated by scaled TROPOMI reference profiles  (Borsdorff et al. , 2018b). We quantify the error introduced by this approach by comparing TROPOMI CO retrievals over bodies of water to both airborne ATom-4 (fourth ATom campaign) and MOPITT TIR data.
15 and 3. 5 ppb, respectively  (Santoni et al. , 2014). The NOAA Picarro measures radiation in the 1. 57 µm region, where the second overtone of the CO stretch is located; the estimated total uncertainty of its measurements is 5. 0 ppb at 1 Hz, or 3. 4 ppb at 0. 1 Hz  (McKain and Sweeney, 2018). Here we use the merged QCLS-Picarro data product CO. X from the dataset version published 28 March 2018 and updated 25 November 2019. The quantity CO. X uses QCLS CO data with calibration gaps filled in by Picarro CO data, after subtracting the low-pass-filtered difference between the QCLS and the somewhat noisier Picarro measurement. Both instruments were calibrated to the NOAA X2014A CO scale. Measurements account for drift of CO in their field calibration tanks (ESRL, 2018) by having them measured at the central calibration laboratory before and after the campaign and applying a linear drift correction to the assigned values.
In situ validation: TROPOMI vs. ATom-4 In situ profile data acquired from aircraft are well-suited for validating satellite CO retrievals. In the following we use the ATom-4 in situ dataset, which mainly includes over-ocean observations. We derived both true and retrieval-simulated (i. e. , unsmoothed and smoothed) total CO column values from the ATom-4 profiles; smoothed values account for the vertical sensitivity of the TROPOMI measurements as expressed by their AK. Prior to obtaining unsmoothed and smoothed ATom-4 total CO columns, complete (e. g. , from the surface to the top of the atmosphere) ATom-4 CO profiles were generated following the standard method for MOPITT validation with airborne data. Profiles that did not cover the 400 to 800 hPa range were rejected. The remaining profiles (between 271 ± 48 hPa and 983 ± 32 hPa) were interpolated to match the MOPITT a priori 35-level vertical grid, which preserves high vertical resolution in the troposphere.
Land retrieval comparisons Over land, MOPITT and TROPOMI total column retrievals were compared in clear-sky scenes only. In such scenes, TROPOMI smoothing error is typically negligible since a TROP is close to 1 at all altitudes. For these comparisons, we selected six ROIs (regions of interest; Fig. 2   Colocated and non-colocated retrievals from the two instruments were analyzed separately; results from the former are presented in Sect. 4. 1, whereas supporting results from the latter are presented in the Supplement. We apply the term "colocated" to pairs of retrievals from two different datasets acquired on the same day and within a ≤ 50 km horizontal distance. In contrast, we apply the term "non-colocated" to retrievals from two different datasets acquired on the same day and inside the same ROI.
TROPOMI vs. MOPITT TIR Daily results from the analysis of colocated TROPOMI and MOPITT TIR data (Fig. 3 ) show that during the ∼ 1. 5 years analyzed, TROPOMI and MOPITT TIR total CO column retrievals were close to each other in both magnitude and temporal variation. Both datasets agree in displaying strong differences between clean ROIs (Sahara and Australia; 10-20 × 10 17 molec cm -2 ) and highly polluted ROIs (India and China; 15-40×10 17 molec cm -2 ). They also show the expected differences between the two hemispheres: retrievals are overall lower in the S Hemisphere ROI (10-20 × 10 17 molec cm -2 vs. 15-22 × 10 17 molec cm -2 ) due to less land area, lower population, and less industrial activity. Both TROPOMI and MOPITT TIR show similar seasonal variability. ROIs located in the Northern Hemisphere present an absolute maximum during boreal winter and a secondary maximum in late boreal summer.
TROPOMI retrievals over water Next we present results from the comparison of colocated TROPOMI and ATom-4 retrievals between 24 April and 21 May 2018 over the Atlantic and Pacific regions. Similarly, we describe results obtained from colocated TROPOMI (both untransformed and null-space-adjusted) and MOPITT TIR over-water retrievals acquired between 7 November 2017 and 10 March 2019 over the two hemispheric ROIs. The ATom-4 data offer the opportunity to compare TROPOMI retrievals to in situ measurements; the MOPITT dataset has the advantage of a substantially larger number of samples distributed over a longer period of time and a wider geographical area.
TROPOMI vs. ATom-4 Results from the TROPOMI and ATom-4 comparison over bodies of water are summarized in Fig. 6  and Table  2. As described in Sect. 3. 2, comparisons were performed in terms of both true (unsmoothed) and retrieval-simulated (smoothed) ATom-4 total column values; the latter account for the vertical sensitivity of the TROPOMI retrievals. Figure  6a  shows that unsmoothed ATom-4 total CO columns and TROPOMI are strongly correlated (R = 0. 93, slope of linear fit = 0. 96) and exhibit a negative relative bias (-4. 76 %) indicative of low TROPOMI values with respect to the true ATom-4. In contrast, Fig. 6b  shows results for smoothed ATom-4 vs. TROPOMI. The relative bias is better in this case (3. 25 %), and the fit between the two datasets has a slightly larger R (0. 94) indicative of an improved correlation.
Discussion TROPOMI and MOPITT are consistent with each other in terms of the main spatial and seasonal CO features they capture, as shown by mean seasonal maps (Fig. 11 ). Both datasets display relatively high values in the Northern Hemisphere during boreal winter (panels a and b) and spring (c and d), similarly high values during all seasons in Africa and Asia, and relatively high values due to Amazon fires in austral summer and fall (a and b, g and h). We note differences between TROPOMI and MOPITT that we interpret as due to their contrasting daytime passing times (13:30 and 10:30, respectively): TROPOMI shows higher CO over Africa than MOPITT, consistent with higher CO emissions from afternoon fires than from morning fires. (Fires are commonly more active in the afternoon than in the morning, as observed in fire counts from same-day morning Terra MODIS vs.
13 ). Possible causes for the contrasting relative biases obtained from the MOPITT NIR product over the Sahara include aerosol and/or surface albedo effects. Further work is needed to diagnose these effects for different wavelengths and to account for differences between MOPITT and TROPOMI measurement and retrieval methods. Determining the most accurate retrievals would require in situ CO column measurements (e. g. , airborne profiles) that are not currently available for that region. There appears to be a seasonal component in MOPITT-TROPOMI bias values in the two hemispheric ROIs and Australia. Polluted ROIs (USA, Europe, India, China) and the We have also analyzed daytime, colocated TROPOMI and ATom-4 data over the Atlantic and Pacific regions for the period between 24 April and 21 May 2018 to quantify the error introduced in TROPOMI retrievals over bodies of water (possible only under cloudy conditions) by approximating below-cloud-top partial columns with their colocated, scaled reference profiles.
, 2012; Landgraf et al. , 2016). The precision values calculated for some of the ROIs analyzed surpass the target value by a few percent. We have analyzed cloud-free, land-only TROPOMI and MOPITT retrievals from 7 November 2017 to 10 March 2019 over ROIs representative of clean, polluted, and hemispheric regions in order to compare total CO column values from the two instruments. ATom being restricted mostly to oceanic regions precludes the use of this in situ dataset for fully validating TROPOMI retrievals over land. To that end, in situ data from other airborne measurement programs are required. Ground-based measurements (e. g. , NDACC, TCCON) could also be used; this would allow for the validation of seasonal variability at fixed locations. Quantitative comparisons between TROPOMI and MOPITT retrievals over land are nevertheless relevant.
The MOPITT dataset represents the longest global  CO record available (2000-present) ; because of extensive validation efforts with respect to in situ measurements and comparisons with other satellite datasets, it is well-characterized. Additionally, MOPITT products have served as the reference for many other satellite retrieval products for CO, including AIRS  (Worden et al. , 2013b) , TES  (Worden et al. , 2013b) , and IASI  (George et al. , 2009 (George et al. , , 2015)). Furthermore, TROPOMI and MOPITT were, until TANSO-FTS-2 became operational in 2019, the only working satellite instruments retrieving CO from NIR solar-reflected radiances. Thus, it is important to understand their relative behavior, particularly because we are interested in continuing the MOPITT multispectral record (which has enhanced sensitivity to near-surface CO for some land observations;  Worden et al.
, 2010)  using radiances from TROPOMI (NIR) and SNPP-CrIS (TIR), two instruments on satellites flying in loose formation  (Fu et al. , 2016). While our TROPOMI-MOPITT comparisons do not account for the contrasting vertical sensitivities of these two instruments, their results show that there is excellent agreement between the two datasets. To analyze TROPOMI retrievals over bodies of water, only possible in cloudy conditions, we have used both ATom-4 in situ data (24 April to 21 May 2018) and MOPITT TIR retrievals (7 November 2017 to 10 March 2019). The ATom comparison allowed for full validation using the TROPOMI AK. This is the ideal situation, since retrieval-simulated ATom-4 column values (i. e. , ATom-4 values smoothed using the TROPOMI AK) explicitly account for the TROPOMI retrieval vertical sensitivity (unlike TROPOMI-MOPITT comparisons).
record of tropospheric CO is important for climate studies as well as for air quality monitoring and prediction. To better understand TROPOMI in the context of the current CO satellite record and thus facilitate the record's extension, we have compared TROPOMI data to other satellite (MOPITT) and airborne (ATom) datasets. Our results https://doi. org/10. 5194/amt-13-4841-2020 Atmos. Meas. Tech. , 13, 4841-4864, 2020.
Acknowledgements. We thank  Clive Rodgers  for his insightful comments regarding retrieval methods. NCAR internal reviews provided by  Louisa Emmons  and  John Gille  are greatly appreciated. This paper benefited from helpful comments from three anonymous reviewers. Sentinel-5 Precursor is part of the  EU Copernicus program , and Copernicus (modified) Sentinel data for 2017-2019 have been used. Financial support. This material is based upon work supported by the  National Center for Atmospheric Research (NCAR) , which is a major facility sponsored by the  National Science Foundation  (grant no. 1852977 ). The  NCAR   MOPITT  project is supported by the  National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) Program. This research is supported by  NASA-ROSES  (grant no.