dataesr/scientific-paragraphs-categorization huggingface dataset:
Charting the data and synthesis of results We designed a data extraction form using Excel (online supplemental file 3). General study characteristics extracted covered author names, title, citation, type of publication (eg, journal article, meeting abstract), study population and sample size (if applicable), methodology/ study design and outcome measures (if applicable). Specific items associated with the topic of the scoping review included the study type (eg, case-control study, differential diagnosis study, prognostic study, reviewmethods, review-applications, review-validation); the article type (journal or conference article), the generic ML domain (eg, supervised/unsupervised); and the name of specific approaches for outcome prediction and for validation. Moreover, to capture key findings related to the review questions, relevant sentences were extracted from each reviewed article, and if needed, complemented by a brief explanatory remark, and by writing out abbreviations used in the original text.
The reviewers piloted the data extraction form using five records from the retrieved article collection. Two reviewers (EG, AR) working independently extracted the data from the included articles. In the case of disagreements, consensus was obtained by discussion. In the final full-text review stage, the preselected articles were grouped by topic, categorising articles into applied versus methodological studies, supervised versus unsupervised analyses and assigning algorithm type identifiers to each article (review articles and papers on validation methodologies were considered as separate categories without a specific algorithm type assignment). The fulltext review and categorisation of articles into different publication types was done through independent manual inspection by the two reviewers.
Study selection and general characteristics of reports We retrieved 1563 abstracts from the literature search. After the removal of duplicates, we screened the remaining 1475 abstracts for eligibility. A total of 619 records were excluded, while 856 abstracts were retained for the full-text assessment. Finally, we included 352 articles that passed all filtering criteria in the data extraction and analysis (see flow chart in figure  2  and online supplemental file 4, providing the reference for each selected article, as well as information on the study type and Open access methodology, the outcome measures, the validation type, and representative sentences from each article on the main study results and key findings; a spreadsheet version of this table has been made available on the online platform Zenodo  34  ).
63  While the first validated omics biomarker signatures were developed for breast cancer, similar diagnostic and prognostic tools have followed for other cancer types. One of these is the Decipher Prostate Cancer Test,  9 64-68  which differs from other omics-derived diagnostic tools by being provided together with a software platform and database, the Decipher Genomic Resource Information Database (GRID), that captures 1. 4 million expression markers per patient to facilitate personalised care. The test itself uses 22 preselected RNAs to predict clinical metastasis and cancer-specific mortality for patients who have undergone radical prostatectomy. An initial discovery study by the Mayo Clinic (Rochester, Minnesota, USA) investigated a cohort of 545 such patients, split into a training (n=359) and a validation cohort (n=186).
However, while direct FNA-based diagnosis is feasible in most cases, indeterminate results can occur. 72 3] [74] [75] [76] [77]  The original discovery study behind the GEC signature used mRNA expression analysis in 315 thyroid nodules, covering 178 retrospective surgical tissues and 137 prospectively collected FNA samples. Two ML classifiers were trained separately on surgical tissues and FNAs, assessing the test set performance on 48 independent, prospective FNA samples (50% of which had indeterminate cytopathology). Discriminative features were selected using a linear modelling approach implemented in the software Limma, and a linear support vector machine was applied for model building and performance estimation via 30-fold CV. The successful CV results were confirmed on multiple distinct cohorts.
Non-cancer approved omics-derived diagnostic tests (four studies) While most clinically approved omics-derived diagnostic tests have been developed in the field of oncology, one of the first LDTs that received FDA clearance for a noncancer disease was the AlloMap Heart test. 13 85-87 It uses a gene expression signature of 11 target genes and 9 control genes in peripheral blood from heart transplant recipients to estimate the risk for acute cellular cardiac allograft rejection. The development process involved statistical analyses of leucocyte microarray profiling data from 285 samples, and subsequent RT-PCR validation and bioinformatics postprocessing. 13 Prior knowledge from database and literature mining was included in the Open access analysis by mapping the data to known alloimmune pathways.
Overall, distinctive aspects of the development approach for the AlloMap signature include the knowledge-based gene discovery, a comprehensive RT-PCR validation of candidate genes, and the robust bootstrap and external validation analyses. The first clinically validated LDT for a cardiovascular indication derived from omics data was the Corus coronary artery disease (CAD) test, developed to identify CAD in stable non-diabetic patients. 11 88-91 In contrast to most other omics-based tests, Corus CAD is not a pure molecular signature test, but takes the clinical covariates gender and age into account. The initial discovery study used a retrospective microarray analysis of blood samples from 195 diabetic and non-diabetic patients from the Duke University CATHGEN registry. After ranking the studied genes by the statistical significance of group differences and prior biological knowledge on their disease relevance, 88 genes were selected for RT-PCR validation.
Strengths and limitations The majority of methodological recommendations derived from the study relate to the early planning and study design for biomarker discovery projects, involving considerations associated with the choice of the study Open access group, sampling and blocking design, the measurement technology, and the input and output variables. 16 17 These recommendations are therefore mainly applicable to prospective studies. For retrospective biomarker investigations of already collected data, the suggestions derived from the review are limited to guidance on improving analysis workflows, for example, for filtering and evaluation analyses, the integration of prior knowledge from multiomics data and public annotation databases, and the choice of robust and interpretable modelling approaches for the generation of biologically plausible and reproducible prediction models.
AcknowledgmentsThe authors thank  Vanna Pistotti  for her assistance with search strategy development and conduction. Collaborators PERMIT group: 1. Antonio L. Andreu 2. Florence Bietrix 3. Florie Brion Bouvier 4. Montserrat Carmona Rodriguez 5. Maria del Mar Polo-de Santos 6. Maddalena Fratelli 7. Rainer Girgenrath  8. Alexander Grundmann 9. Josep Maria Haro   10. Frank Hulstaert 11. Iñaki Imaz-Iglesia 12. Setefilla Luengo Matos 13. Emmet McCormack 14. Albert Sanchez Niubo 15. Emanuela Oldoni 16. Raphael Porcher 17. Vibeke Fosse 18. Luis M. Sánchez-Gómez   19. Lorena San Miguel 20. Cecilia Superchi 21. Teresa Torres 22. Anna Monistrol Mula Contributors Study conception and design: EG and AR. Methodology: CG and RB. Data collection and analysis: EG and AR. Original draft preparation: EG. Review and editing: AR, EG, PG, CG, JD and RB.
Project supervision: PG. Funding acquisition:  JD. Responsible  for the overall content as guarantor: EG. All authors have read and revised the manuscript and approved the final version. The members of the PERMIT group were involved in the preparation or revision of the joint protocol of the four scoping reviews of the PERMIT series, attended the joint workshop (consultation exercise) and are coauthors of the other scoping reviews of the PERMIT series. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 874 825.
Data availability statement The study protocol was published on the online platform Zenodo. 19 Copies of searches and data extraction sheets will be made publicly available on Zenodo as part of the database collection for all scoping reviews conducted in the PERMIT project. Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
Many studies suggested a use of uniform vertical distribution of particles in the planetary boundary layer  (Emili et al. , 2010; Koelemeijer et al. , 2006; Zhang and Li, 2015). Hence, a uniform vertical distribution is assumed in this study, and the detail information about the vertical correction can be found in the Appendix A. 1. The PBLH data is obtained from the fifth generation ECMWF (European Centre for Medium Range Weather Forecasts) atmospheric reanalysis of the global climate (ERA5) which can be found at https://www. ecmwf. int/en/forecasts/datasets/reana lysis-datasets/era5. f o (RH) denotes the optical hygroscopic growth factor, the ratio of the aerosol extinction coefficient under wet conditions to that under dry conditions  (McInnes et al. , 1998) , can always be represented in Eq.
(A3)  (Chen et al. , 2017; Im et al. , 2001; Kotchenruther et al. , 1999; Li et al. , 2005; Tsai et al. , 2011; Wang et al. , 2010). Due to the differences in the hygroscopic properties of different components, it is difficult to measure f o (RH) exactly except in the lab. The fitting coefficients (a and b) depend on aerosol chemical compositions. Many studies assumed that both a and b equal to 1. 0 and obtain good correction effects  (Chen et al. , 2017; Li et al. , 2005; Tsai et al. , 2011). Hence, the humidity correction scheme in the Appendix A. 2 is used in this study. The RH data is also adopted from the ERA5 reanalysis dataset.
Statistical study based on ground-based observations The VE 10 model is studied with consideration of different aerosol types using AERONET  (Holben et al. , 1998)  observation around the world. In this study, datasets collected over approximately 10 years at seven AERONET sites are used. The Lev. 1. 5 AOD at 500 nm, FMF derived from spectral AOD using spectral decomposition approach  (O'Neil et al. , 2001 (O'Neil et al. , , 2003)) , and PVSD (Particle Volume Size Distribution) data inverted from sky-radiance by  Dubovik and King (2000)  are used in this study. The seven sites include Beijing (urban/industrial aerosol), Goddard Space Flight Center (GSFC; urban/industrial aerosol), Cuiaba Miranda (biomass burning aerosol), Mongu (biomass burning aerosol), Solar Village (desert dust), Ascension Island (oceanic aerosol), and Lanai (oceanic aerosol).
The influences of vertical model's uncertainties We designed sensitivity test to discuss "how sensitive is this approach when distinct elevated layers, like severe dust events or forest fires, with large AOD values are present above the PBL". Liu et al. , (2019)  constructed monthly aerosol extinction profiles dataset based on CALIPSO data during 2007-2015 over China. Although aerosol vertical profiles varied with location and season, the seasonal mean results over Northeastern China are very similar to the Gaussian curve (Fig. 12 (a) ). The Gaussian model can be expressed as followed: Ext(z) = A•exp ( - (z -z h ) 2 δ 2 h ) (18) where σ(z) represented the aerosol extinction with altitude of z, A represented peak concentration, z h represented peak height, and σ h represented half-width. Thus, the Gaussian curve is used to simulate actual atmospheric aerosol extinction profile.
In addition, our semi-empirical physical approach is independent of in-situ PM 10 measurements. The generated dataset by our method can be used for regulatory purposes and long-term trends of PM 10. Furthermore, the contributions of human and natural factors  (Zhang et al. , 2020)  to PM 10 are expected to be estimated in the future work, based on the developed semiempirical physical formula. In the future, the accuracy improvement of remote sensed PM 10 results is depending on the development of the sensors and the inversion techniques. To reduce the remote sensing PM 10 uncertainty, more accurate input data and more appropriate parameterization schemes are needed. In addition, high-resolution remote sensing results have large spatial coverage and spatial representation, which can provide scientific basis for site selection on the ground.
Conclusions The improved remote sensing method of near-surface PM 2. 5 by using semi-empirical physical models has been proposed and demonstrated. The columnar volume-to-extinction ratio, which can realize the conversion of optical extinction to volume concentration, was found to be well described by a quadratic polynomial function of FMF. In addition, the particle effective density can be obtained from the real part of the complex refractive index. Both semi-empirical physical models of columnar volume-to-extinction ratio and particle effective density proposed in our study performed reliably for different aerosol types. By using the aerosol retrievals from POLDER/GRASP, and BLH and RH data from ERA5, PM 10 mass concentration was derived based on the semi-empirical physical approach over China from January to October in 2013.
Acknowledgements Thanks to the AERONET team for providing the data for this study. Thanks to  Brent Holben , the  AERONET  Project Scientist, Physical Scientist at  NASA , the PI of Solar Village site, Ascension Island site, Lanai site, GSFC site and Mongu site. Thanks to  Paulo Artaxo , the professor of the  University of São Paulo , the PI of Cuiaba Miranda site. We also acknowledge the use of ECMWF data and POLDER/GRASP data.
Funding This work was supported by the  National Outstanding Youth Foundation of China  [Grant number  41925019 ]; the  National Key R&D Program of China  [Grant number  2016YFE0201400 ]; the  National Natural Science Foundation of China  [Grant number  41671367 ];  O. Dubovik  and  C. Chen  recognize support from the  CaPPA Project (Chemical and Physical Properties  of the Atmosphere) that is funded by the  French National Research Agency  under contract " ANR-11-LABX-0005-01funded  by the  French National Research Agency (ANR)  through the  PIA (Programme d'Investissement d'Avenir)  under contract " ANR-11-LABX-0005-01 ". C. Chen  also recognizes support by  Open Fund of State Key Laboratory of Remote Sensing Science  [Grant number  OFSLRSS202008 ].
Data availability The ERA5 data sets are publicly available from ECMWF website at https://www. ecmwf. int/en/forecasts/datasets/reanalysis-datasets/era 5 (last access: 20 May 2019). The authors would like to acknowledge the use of POLDER data "POLDER/PARASOL Level-1 data originally provided by CNES (http://www. icare. univ-lille1. fr/) and AERIS/ICARE Data and Services Center. The POLDER/GRASP results are generated by Laboratoire d'Optique Atmosphérique and Cloudflight Austria GmbH using GRASP software (https://www. grasp-open. com) developed by  Dubovik et al. (2011 Dubovik et al. ( , 2014)). The AERONET data sets are publicly available from NASA website at https://aeronet. gsfc. nasa. gov.
Interpretations of fossil pollen data are often limited to broad, qualitative assessments of past climatic and environmental conditions (e. g. colder vs. warmer, wetter vs. drier, open vs. closed landscape). These assessments can be particularly imprecise in regions such as southern Africa, where botanical biodiversity is high, and there exists an associated uncertainty regarding the climatic/environmental sensitivities of the plants contributing to a given pollen type. This atlas addresses this limitation by characterising the climate sensitivities of the 140 pollen morphotypes most often recorded in Late Quaternary palaeoecology studies in southern Africa, relying on their parent plant distributions as one of the basic factors that determine their presence. The atlas is designed as a suite of graphical diagnostic tools and photographs together with analyses of the modern geographical distribution of more than 22,000 plant species to identify their primary climatic sensitivities across southern Africa.
Further, as pollen morphotypes (hereafter referred to as pollen types) commonly comprise more than one single species, defining the climatic affinity of a pollen type quickly becomes exceedingly complicated. These issues are amplified in regions with high botanical diversity, such as southern Africa, where tens or even hundreds of species, sometimes with distinct climate requirements, are subsumed within undifferentiated pollen types that can cover wide ranges of ecological and climatic conditions. In practice, robust statistical estimates of the specific climate affinities (both in terms of their tolerances and associated optima) are missing for the vast majority of the commonly observed pollen types, which often limits interpretations of fossil pollen data to relatively coarse-scale qualitative -and often subjective -climatological and/or environmental considerations.
The development of the CREST (Climate REconstruction SofTware) method  (Chevalier et al. 2014)  has enabled the production of the first quantitative climate reconstructions using fossil pollen records in southern Africa. CREST is based on the definition of independent, taxon-specific probability density functions (pdfs) to model a probabilistic relationship between pollen and climate from an ensemble of modern plant geolocalised occurrence data (hereafter 'plant distributions'). While more commonly used reconstruction techniques -such as modern analogues  (Overpeck 1985 ; e. g. Imbrie and Kipp 1971) , or regression techniques  (ter Braak and Looman 1986; e. g. ter Braak and Juggins 1993 ) -rely on large collections of modern pollen samples to estimate pollen-climate relationships, such samples cannot be obtained for every environment or vegetation type of southern Africa.
Botanical Data To estimate the climate responses of the different taxa, we used botanical data from the Global Biodiversity Information Facility (GBIF) database  (GBIF. org 2020a (GBIF. org , 2020b (GBIF. org , 2020c (GBIF. org , 2020d, 2020e, 2020f, 2020g, 2020h, 2020f, 2020e, 2020f, 2020g, 2020h, 2020f ; 'GBIF: Global Biodiversity Information Facility,' 2018) using the curated dataset of  Chevalier (2019 Chevalier ( , 2018)). These data are available as 'presence-only' observations (i. e. the absences and abundances are not documented) and their spatial resolution are homogenised at 0. 25 • × 0. 25 • grid square (c. 900 km 2 at the latitudes of southern Africa). To build the atlas, we have used a total of 475,712 occurrence records, which correspond to 22,496 species grouped in 140 pollen types.
The number of observed species in each grid cell varies between 1 and 2815, with a median of 17 species and a mean of 81 species. The distribution of these data is spatially heterogenous (Figure  1A ), with >75% of the records being concentrated in South Africa and its biodiversity hotspot  (Rutherford et al. 2012; the Greater Cape Floristic Region; Myers et al. 2000). This sampling bias is also visible along the South African border. The other countries composing our study area are less well represented, especially eastern Angola and western Zambia. However, the modern climate and vegetation gradients are less steep in those regions than they are in the southern part of the study area, where orography increases spatial variability. As such, it may be considered that the lower sampling density is sufficient to capture the full range of climate response of the studied taxa in these regions.
Pollen Photographs Most of the pollen images are taken from the reference collection of African pollen types that was created by E. M. van Zinderen Bakker and co-workers at the University of the Free State, for a series entitled South African Pollen Grains and Spores, between 1953 and 1970 after extensive collection from several herbaria in Africa and Europe  (Coetzee 1955; van Zinderen Bakker and Coetzee 1959; van Zinderen Bakker et al. 1970; van Zinderen Bakker 1953 , 1956). Other photos were taken from the University of Cape Town reference collections (created by Jean Sugden and Sue De Villiers) and the African Pollen Database.
Identification of climatically 'uninformative' taxa Some pollen types are not useful for quantitative climatic reconstructions because of the diversity of species producing pollen of indistinguishable morphologies. Best illustrated by Poaceae (1266 species grouped into 242 genera in our southern African botanical database; Figure  4 ), the diversity issue highlights one limitation of many statistical tools as there is no simple, unique link between the presence records and the associated climate values. Presumably present (almost) everywhere in southern Africa (Poaceae species have been observed in about 60% of all Aridity classes, see Figure  4 ), only their spatially-varying relative abundance in the modern environment (unavailable data) may contain reliable climatic information. In fact, when the number of species becomes too large, and the niches too diverse, the pdfs of such taxa are not flat -as with the pdfs of uninformative taxa -but rather exhibit a peak that reflects the distribution of the whole climate space rather than specific sensitivities (see Poaceae on pp.
By construction, the shape of monospecific pollen types is constrained and will appear smooth. In such situations, looking at the distribution of Artemisia in the southern African climate space, and more specifically where it grows and where it does not grow, provides additional valuable information to discriminate between constraining and non-constraining variables. Real absence data are not available here, but it has been suggested that when the modern presence record dataset is sufficiently extensive, pseudo-absence records can be inferred  (Elith and Leathwick 2009; Elith et al. 2006; e. g. Birks et al. 2010). Superimposing plant distributions on climate layers thus sheds light on whether a variable is important or not. Maps in Figure  6  show that most records of Artemisia (c. 70% of the distribution) occur between TWetQ values of 17.
The same approach of superposing the distributions with climate gradients can be used to highlight some specific sensitivities. For example, the distribution of Podocarpus is strongly correlated with high humidity values  (Chevalier et al. 2021b, pp. 236-237) , while Zygophyllum's distribution is concentrated on the other side of the aridity gradient in the most arid grid cells  [pp. 312-313]. lives in the hottest and most humid sections of our climate space, while Burkea [pp. 68-69] has a similar thermal niche but a preference for more mesic climates. These assessments are consistent with previous interpretations, but the data presented here clarifies and validates these interpretations, and perhaps more importantly, contributes to a common understanding of a broader range of plant-climate relationships and environmental sensitivities in southern Africa.