dataesr/scientific-paragraphs-categorization huggingface dataset:
These unique long-term monthly time series of the CRB's SWS provide the broad characteristic of the variability of the surface water storage anomaly at the basin and sub-basin levels over 24 years in the CRB. They open new perspectives to move towards answering several crucial scientific questions regarding the role of SWS dynamics in the hydrological and biogeochemical cycles of the CRB. For instance, SWS estimates are a relevant source of information to make progress in the understanding of the hydrodynamic processes that drive the exchanges between rivers and floodplains, both in terms of freshwater and dissolved and particulate materials. Such datasets also enable us to explore the link between regional climate variability and water resources, especially during extreme events, and can now be used to improve our understanding of hydroclimate processes in the Congo region  (Frappart et al.
Finally, since GIEMS-2 and the DEMs used in this study are available globally, our results thus also present a new first step towards the development of such SWS databases at the global scale. Furthermore, the proliferation of new DEMs that are proper DTMs and the increasing availability of high-accuracy bare Earth DEMs  (O'Loughlin et al. , 2016; Yamazaki et al. , 2017; Hawker et al. , 2022)  have opened new opportunities to better investigate SWS dynamics at the global scale. As highlighted in  Papa and Frappart (2021) , global SWS estimates and variations are crucial for understanding the role of continental water in the global water cycle, and global estimates will offer new opportunities for hydrological and multi-disciplinary sciences, including data assimilation, land-ocean exchanges, and water management.
Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/essd-15-2957-2023-supplement. Author contributions. BK, FP, AP, RMT, and SC conceived the research design. BK processed the data and created the SWS dataset from the hypsometric curve approach. FF created the SWS dataset from the multi-satellite approach. OE created the SWS of lakes. RAJO provided precipitation data and Fig. 11. BK, FP, and AP analysed and interpreted the results and wrote the draft. FP and RMT were responsible for the data curation. All the authors discussed the results and contributed to the final version of the paper. Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements. Benjamin Kitambo has been supported by a doctoral grant from the French Space Agency (Centre National d'Etudes Spatiales -CNES), Agence Française du Développement (AFD), and Institut de Recherche pour le Développement (IRD). This research has been supported by the CNES TOSCA project DYnamique hydrologique du BAssin du CoNGO (DYBANGO)  (2020) (2021) (2022) (2023). We thank Catherine Prigent from Sorbonne Université, Observatoire de Paris, Université PSL Paris, France, for sharing the GIEMS-2 database. Omid Elmi is supported by the DFG (Deutsche Forschungsgemeinschaft) (project no. 324641997) within the framework of the Research Unit 2630, GlobalCDA: understanding the global freshwater system by combining geodetic and remote sensing information with modeling using a calibration/data assimilation approach (https://globalcda.
The two approaches are complementary: the hypsometric curve approach allows us to generate the SWS changes over the entire basin with limitation over lakes and in high-altitude topography, while the multi-satellite one can generate SWS variations over lakes but with a spatial constraint on the availability of VSs. SWS variations from FABDEM, which is the only DTM among all the DEMs used (i. e. both biases associated with trees and buildings have been removed), is then used to illustrate the capability of the new dataset.
We design algorithms for Robust Principal Component Analysis (RPCA) which consists in decomposing a matrix into the sum of a low rank matrix and a sparse matrix. We propose a deep unrolled algorithm based on an accelerated alternating projection algorithm which aims to solve RPCA in its nonconvex form. The proposed procedure combines benefits of deep neural networks and the interpretability of the original algorithm and it automatically learns hyperparameters. We demonstrate the unrolled algorithm's effectiveness on synthetic datasets and also on a face modeling problem, where it leads to both better numerical and visual performances.
To overcome these limitations, a technique known as deep unrolling (also known as deep unfolding) has been extensively explored  [11]  and has emerged as a promising approach in various signal processing problems. While the model parameters are fixed in the classical algorithms, the unrolled network replaces them with learnable parameters that can be optimised through end-to-end training using backpropagation. Therefore, a trained unrolled network can be viewed as a parameter-optimised algorithm, sharing both the benefits of conventional DNNs and interpretability of the original algorithm. Furthermore, as classical algorithms often have significantly fewer parameters than DNNs, unrolled networks can potentially mitigate the overfitting problem when there is insufficient data or when the training dataset is of low quality.
The proposed procedure also incorporates the Minimax Concave Penalty (MCP), an alternative to hard thresholding owning numerous interesting properties and more suitable than the ℓ 1 -norm relaxation  [18]. The overall proposed procedure performs excellently on benchmark synthetic datasets and real-world (face) datasets, exceeding the performances on the state-of-the-art (unrolled) approaches. Outline: The paper is organised as follows. Preliminaries on RPCA algorithms and deep unrolling are presented in Section 2. The proposed method is then described in Section 3 and numerical experiments are conducted in Section 4. Finally, concluding remarks are presented in Section 5.
Learning β and γ automatically allows our model to be customisable to use cases where datasets share similar properties for the underlying low-rank and sparse components. One challenge when developing an unrolled version of Ac-cAltProj is that we are unable to directly use hard-thresholding for the non-linear activation. This is because it is not subdifferentiable, a property needed to deploy gradient-based optimizers to learn the parameters β and γ  [19]. LRPCA  [18]  tackled this problem by replacing the hardthresholding operator with the soft-thresholding operator in their unrolled model. However, the soft-thresholding operator is the proximal mapping of the ℓ 1 norm while the hard-thresholding operator is the proximal mapping of the ℓ 0 pseudo-norm. As such, vanilla soft-thresholding is not suitable for our objective in  (2).
Face Dataset Problem setup: We now test the proposed unrolled model on the Yale Face Database  [23]  for the application of face model- Cases ǫ supp Fig. 3. Means and standard deviations of errors. The "Unrolled (st)" curve stands for unrolled algorithm with soft-thresholding. ing. The Yale database consists of 11 grayscale facial images each for a total of 15 subjects. The 11 images, each having a dimension of 243×320, show the same individual with different facial expressions, lighting conditions or accessories such as spectacles on the face. The task is of face modeling is to recover the occlusion-free image for facial recognition  [3]. We vectorise the images of each subject, which are then stacked to form a 77760 × 11 matrix M ⋆. The static occlusion-free image of the subject forms the low-rank component of the matrix while the varied facial expressions, shadows, and objects covering the face form the low-rank component.
Since all 11 images share one common underlying occlusion-free facial image, we assume that rank(M ⋆ ) = 1. Subjects 1 to 7 and Subjects 8 to 15 are used for training and testing respectively. Similar to the experiment on synthetic datasets, we use IRCUR to obtain initial estimates and train the unrolled network for 8 epochs. Results: Visual results are displayed in Figs. 4  and 5  for the low rank and sparse parts respectively. The methods enable the separation of the original images into expressionless faces and expression details. While the images learned by IRCUR are poor, the proposed unrolled strategy adaptively learned hyperparameters (γ, β) that result in sharper edges. Tests were performed on an Intel(R) Core(TM) i7-1185G7 @3. 00GHz, with 32Go RAM. The total (7 subjects) training time is 300s.
CONCLUSION We proposed an unrolled algorithm to solve the RPCA problem in its nonconvex form. This results in an unrolled version of the AccAltProj algorithm but incorporates the Minimax Concave Penalty. The underlying learning strategy, which has the advantage of learning hyperparameters γ and β automatically, allows us to improve the state-of-the-art performances on benchmark synthetic datasets used in existing works as well as on real-world face datasets. In future work, we plan to improve on the training criterion in Section 3. 3 as well as the automatic learning of more parameters such as the ones that parametrize the MCP, i. e. , ζ and υ in (4).
Methods We conducted a Systematic Review, searching online databases and search engines in English. Studies were screened in a two-stage process (title/abstract review, followed by full-text review) to evaluate whether they met a list of eligibility criteria. Data coding, extraction, and study validity assessment was completed by a team of trained reviewers and each entry was validated by at least one secondary reviewer. We used logistic regression models to assess whether the direction of shift supported common range-shift expectations (i. e. , shifts to higher latitudes and elevations, and deeper depths). We also estimated the magnitude of shifts for the subset of available range-shift data expressed in distance per time (i. e. , km/decade). We accounted for methodological attributes at the study level as potential sources of variation.
Online search engines We then conducted a follow-up search in the online search engine Google Scholar (Fig. 1 ), and reviewed 1000 articles (the maximum retrievable; [36]); only articles not previously identified were included. We used the advanced search options and entered our search terms in the "with all of the words" field. Similar to the Web of Science and Scopus searches, our Google Scholar searches were not constrained by date, year, or author. All searches were conducted in English, since this is the primary language of the reviewers. We acknowledge a clear bias by not incorporating the non-English-language science that could impact the spatial representativeness of species redistribution (see Fig. 3b ;  [37] [38] [39] ), and that the conclusions of our review should be interpreted in light of this gap.
Other considerations We did not incorporate data from review articles or previous meta-analyses (e. g. ,  [6, 9, 41] ) directly into our review; instead, we used review articles and meta-analyses to identify original articles that may have contained relevant data. We flagged review articles and meta-analyses as "review" to be inspected after the preliminary title and abstract review. Each review article and its citations were read to identify the relevant primary source; we then evaluated the primary source and included it as appropriate based on a title/abstract review. Our methodology is challenged with regards to reproducibility, given that our exact literature search in Google Scholar may not produce identical results in the future. Search results from these sources vary as a function of date and location.
Using the Colandr relevance ranking function, highly relevant articles appeared earlier in the search. We reviewed abstracts 100 at a time, recording the number of included vs. excluded articles to create an accumulation curve (articles passing title/abstract review per 100 titles); once we reached the asymptote of the accumulation curve (defined by no new successful articles out of 300 titles reviewed), we stopped searching the database results. To validate our stopping criteria, we selected a random subset of 300 unreviewed articles for title and abstract review, and evaluated whether their inclusion substantially altered the findings of our meta-analysis (similar to the displacement method described in  [35] ). During this validation step in our review, we found only two additional articles that met our study inclusion criteria.
Study validity assessment All eligible studies were critically appraised for risk of bias and coded based on their methodology and data quality  [8, 9]. Rather than excluding studies using certain methodologies, we included methodological metrics as covariates in our analyses. This enabled us to account for differences in methodology among studies and estimate the size and direction of bias associated with different study methods  [42]. We categorized studies according to five methodological factors that may influence or bias study outcomes  [9, 42]. These factor categories included: • Number of taxa for which range-shift observations were recorded in the study (referred to as ntax), indicating potential positive publication bias  [6, 7] : although some previous meta-analyses of range shifts have omitted single-species studies, we determined that accounting for this as a methodological approach could effectively allow for the inclusion of these data  [9].
For our analyses, we categorized ntax into a binary factor variable of one or more than one species; • Monitoring frequency (referred to as sample): regular (i. e. , authors sampled species ranges continuously throughout the study period at specified time intervals) vs. irregular (i. e. , authors sampled species ranges periodically throughout the study period at irregular time intervals); • Underlying nature of the data, indicating whether abundance or occurrence data were collected (referred to as obsvt); • Sampling design (referred to as resurvey), indicating whether the data were drawn from a balanced resurvey (i. e. , from strictly paired designs such as permanent plots or calculated from a resampling procedure designed to reach a balanced dataset) or if data were collected opportunistically (i.
Data coding and extraction strategy Data from each article that passed title/abstract and full text review were extracted and entered into a spreadsheet that was initially shared among co-authors in Google Drive. This table was converted to an excel file (Additional File 3). Range-shift data were extracted for each species described in an included study; therefore, if a study contained results for more than one species, that study had more than one row in the table. In addition, for a given taxon in a given study, we reported range-shift values on distinct dimensions, time periods (e. g. , shifts were reported separately between a baseline survey and resurvey, and between the first resurvey and the second), and range-shift parameters on separate rows, if the authors reported these shifts as disaggregated data.
Database review & logical checks We ensured reproducibility and consistency in data coding and extraction by writing a detailed manual of instructions and guidelines on the process of entering data into our spreadsheet (Additional File 4). This was detailed enough for any of our co-authors to follow. It included complementary information clarifying our eligibility criteria and it answered common questions encountered during the review process. In addition, we trained reviewers to ensure consistency in how data were entered. We implemented a dual-review system so that all articles and associated data entry were reviewed by a second co-author. To resolve any questions or problems, the authors convened and reviewed questionable entries. After our initial data entry, we performed additional data validation and cleaning steps in R version 4.
0. 4 [43]. We performed a series of logical checks to confirm that range dimensions, categorical changes, hypothesis support, and sign of numeric shifts aligned for all entries; for example, an observation categorized as "latitudinal increase" should have a positive numeric entry (positive numbers indicating movement towards the poles) and be categorized as "supports". We ensured that species names used in the database were correct and up to date using the taxonomizr package  [44]. We verified species names and pulled the full taxonomic classification for each species from the National Center for Biotechnology Information (NCBI [45]), Global Biodiversity Information Facility (GBIF  [46] ), Integrated Taxonomic Information System (ITIS  [47] ), Arctos  [48] , and iNaturalist databases  [49].
Data synthesis & presentation First, we calculated basic summary statistics on the database (e. g. , total number of range-shift records, total Fig. 2  Typology of range shifts used in this review. Leading edge or center of range increases and trailing edge decreases were considered to support our temperature-related range shift hypotheses. Lack of shifts in any parameter; decreases on the leading edge or in the mean/optimum; and increases on the trailing edge were all considered "fail to support" number of species registered, geographic regions covered). We then assessed the direction and magnitude of range shifts. We determined that studies which measured range shifts over fewer than 10 years should be excluded due to the potential for confounding effects from shortterm and decadal climatic variability (e.
We fitted separate models for each explanatory variable of interest (i. e. , range dimension, parameter, taxonomic group, and ecosystem type), because some of the variables are related (e. g. , the taxonomic group fish could only be in freshwater or marine ecosystem types) and fitting separate models allowed us to more clearly explore these relationships. Each of these variables of interest, as well as the five methodological variables were used as fixed effects (see Additional File 5: Table  S2  for full description of the models)  [8, 42]. After fitting the logistic regressions, we used the emmeans function in emmeans package  [51]  to compute the estimated marginal mean (EMM) probability and 95% confidence interval of support for general range (i. e. , the predicted probability of support/fails to support after averaging across the methodological variables weighted proportionally to their occurrence in the original dataset).
Magnitude We used regression analysis to assess the magnitude of range shifts on the quantitative observations in our database (i. e. , observations expressed as unit space per unit time). We did not use random-effects meta-analysis, because it is difficult to rigorously define study sample size in this context (e. g. , number of sites in the study, number of years used to estimate range shifts, number of individual surveys, or number of species). In most cases, these data are not reported, and given the various ways that studies measured range shifts, sample sizes in this sense would not be directly comparable. Thus, we have limited confidence in the comparability and consistency of sample size measurements across studies to use this as a weight for observations in a regression model. Instead, we converted all quantitative measures of range shifts to a standardized unit (kilometers per decade) for ease of comparison across dimensions, and with previous metaanalyses (raw and converted shift values are included in Additional File 3).
We calculated the average velocity of range shift by dimension (e. g. , overall latitudinal, elevational, and depth shifts), as well as for particular subsets of observations (e. g. , by range parameter and taxonomic group). To account for methodological factors, we first fit a multiple linear regression with methodological variables as predictors of km/dec shifts to assess the significance of methodological variables on quantitative estimates of range shifts, and to gain a general understanding of the direction and magnitude of the relationship between methodological factors and the magnitude of range shifts (see Additional File 5: Table  S2  for full description of statistical models). Having established that all methodological variables contributed significant variation to quantitative range-shift estimates (Additional File 5: Table  S3 ), we calculated estimated marginal means (EMM; as above) to estimate range-shift coefficients for specific taxonomic groups using the emmeans package in R  [8, 9, 42].
We used post-hoc Tukey adjustments to assess the significance of the marginal means over the methodological variables and used proportional weights when accounting for observations within each methodological category. In addition, we created a suite of linear mixed-effects model (package lmer) to assess the coefficient size and significance of ecological factors (i. e. , dimension, parameter, taxonomic group, and ecosystem type) while controlling for methodological factors. The five methodological factors (see study validity assessment above) were set as random effects (i. e. , random intercepts; we tested for the significance of random slopes but this did not improve model fit). We used the lmerTest package to estimate p-values for the fixed effects, and the MuMIn package to calculate conditional and marginal R2.
We selected our final models by comparing AIC scores. We first determined which methodological predictors to include in the initial modeling suite based on significance in a multiple linear regression with km/dec as the dependent variable and all methodological variables as independent variables. All five methodological variables were significant, and were therefore included in the linear mixed effects model as random effects. In our final model, we included dimension, parameter, taxonomic grouping, and ecosystem type as fixed effects (all were significant based on estimated p-values), and included the suite of methodological variables described above as random effects. Finally, we ran a sensitivity analysis to evaluate the role of statistical outliers in our dataset (Additional File 5: Table  S4 ).
Review results Our literature search workflow yielded a total of 94,672 candidate articles from Web of Science, Google Scholar, and Scopus. We reviewed 11,581 of these candidate articles before reaching the peak of the accumulation curve during the abstract and title review phase. Another 519 articles were identified through our additional search methods, updating our total articles reviewed to 12,100. Of these, 842 candidate articles passed the initial title/ abstract review for in-depth review. Full-text review resulted in a final list of 315 articles fitting criteria for inclusion in the database (Fig. 1 ; see Additional File 2 for list of all reviewed/included articles and full study validity assessment). These numbers do not include duplicate articles, since Colandr removes duplicates before abstract screening.
Of the 315 articles included in the final database, 124 came directly from online database searches, while the remaining 191 articles came from review paper search, snowball methodology, and opportunistic articles. This is likely due to the order of our search procedure, and the prominence of previously published high-profile review papers. We initially reviewed only 2000 abstracts from our database search before beginning the snowball methodology. Included within this original 2000 abstracts were several large, high-profile review papers which cited many relevant primarysource articles. These articles are therefore coded in our database as coming from "snowball methodology", even though they also turned up later inour extended database search as well. We completed the article searches in: Web of Science on April 16, 2019; Google Scholar on June 14, 2019; and Scopus on December 17, 2019.