dataesr/scientific-paragraphs-categorization huggingface dataset:
If it is sometimes very difficult to reproduce exactly the conditions of an experiment. Using open source tools can help to attain Computational Reproductibility  (Leonelli, 2018) , as when data are shared, anyone can reproduce the results. Arabesque, Kepler. gl and Magrit are interesting in this regard. The export function of these tools integrates both the data and the project parameters, making it possible to reproduce the map. All the tools are open source and their code is auditable. Their processing and data visualization algorithms can therefore be inspected.
Purpose Among the four tools tested: 1. Kepler and Flowmap. blue are intended for an audience wishing to explore data without the desire to perform statistical analysis and the intention to produce and export a thematic map. Author's manuscript. Proceedings of the 2021 edition of the FOSS4G 2021 -Academic Track, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS). ________________________________________________________________________________________________________________ 2. Magrit is intended for the realization and production of thematic maps and allows statistical processing to be carried out beforehand. 3. Arabesque is at the interface of these two typical uses: it offers powerful exploration functions, visual parameterization of figures, and statistical analysis possibilities.
However, the export and captioning functions needed to produce thematic maps in Arabesque are still limited. 4. Arabesque, like Flowmap. blue, is designed for flow maps. Kepler. gl and Magrit are more generalist and offer other representation modes for other datasets. 5. Arabesque and Magrit target the same audience of researchers and students and come from the same school of French cartography (in the line of Jacques Bertin). Magrit's more general approach allows it to attract a wider audience. 6. Flowmap. blue and Kepler aim more at producing something quickly communicable and are very web publishing oriented.
Conclusion This article highlights the specificities of Arabesque within the small ecosystem of web applications for mapping flows. Rather than highlighting the flaws and qualities of each tool tested, we observe that these tools meet distinct user needs. The style and type of map produced, the target audience, and the technical skills required to use these applications differ interestingly. Having tested these software on the same dataset, this complementarity is clearly visible. In the end, the business culture, the user's skills and the final use of the map influence the choice of the tool because it will influence the chosen approaches (exploration and/or analysis) and the expected purposes. Our contribution points out the strengths and limitations of each in order to make an informed choice.
Bahoken et al. , 2020;agrit,Flowmap. blue,Tableau,CARTO,Flourish, ArcGIS Online, and NETSCITY. Of course, this list of online flow mapping applications might not be exhaustive and is open to suggestions to enrich it. To this purpose, the fruits of the above-mentioned review of web applications for flow mapping are shared in the form of a collaborative dashboard open to any contribution. Possible contributions to this collaborative work of digital curation can be modifications or enrichment of metadata, addition of new applications and of course reuse in the context of other research (the paper:Bahoken et al. , 2020; the dashboard: Le Campion and Nguyen, 2019). Table 1. Software licensing of a selection of online flow mapping tools While it also allows users to map their own data, NETSCITY has the particularity of being more specifically designed for mapping science data (i.
e. scientific collaborations). Moreover, being still in beta version, the code sharing operation of this application is not yet finalized; thus justifying excluding it from the following comparison with Arabesque. Flow mapping web tools FOSS4G compliance open free Kepler. gl yes yes Magrit yes yes Flowmap. blue yes yes While many of the tools identified are based on open-source data-visualisation, mapping and geoprocessing libraries, they do not all have the same degree of accessibility, as shown by Table1. Among these 8 flow mapping web applications we identified for the study, only 4 are entirely free and open: Arabesque, Kepler. gl,Magrit, and Flowmap. blue. In the perspective of comparing Arabesque to tools in the same category, and in the logic of presenting in this article research results that are themselves as open and reproducible as possible, we have decided to operate our benchmark on those 4 applications (developed between 2017 and 2019).
Proceedings of the 2021 edition of the FOSS4G 2021 -Academic Track, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS). ________________________________________________________________________________________________________________. Only developed in JavaScript using flowmap. gl, deck. gl, mapbox, d3, blueprint and CARTOColors, the tool is designed to explore and create flow maps from Google Sheets or tabular data in a web browser. Additional tools are also available such as geocoding, matrix converter (wide / long formats). Flowmap. blue allows automatic clustering, provides global data filtering and automatically adapts the scales of Author's manuscript.
Methods We invited cancer GWAS to share summary association statistics with the FAMRC and subjected the collated data to a comprehensive QC pipeline. We identified meta data errors through comparison of study-specific statistics to external reference datasets (the NHGRI-EBI GWAS catalog and 1000 genome super populations) and other analytical issues through comparison of reported to expected genetic effect sizes. Comparisons were based on three sets of genetic variants: 1) GWAS hits for fatty acids, 2) GWAS hits for cancer and 3) a 1000 genomes reference set.
GWAS summary data can also be obtained from an increasingly diverse range of sources, including online platforms and study-specific websites, but it is not always clear whether such results have been through post-GWAS filtering steps (e. g. with low frequency or poorly imputed SNPs excluded), which increases the potential for unreliable genetic associations. The potential for meta and summary data errors is compounded in relatively complex MR study designs, such as in MR-PheWAS  [6] [7] [8]  (MR-phenome-wide association study), wide-angled MR  9, 10  and pan-disease MR  11  , in which summary datasets from many different studies, corresponding to many different exposures and/or outcomes, are collated and harmonised into a single analysis. Within the GWAS field, quality control (QC) procedures have been developed that can detect a wide range of analytical issues and meta data errors, either at the GWAS stage  12  or at the post-GWAS meta-analysis stage  13.
For example, it is common practice to exclude genetic variants of low genotype or imputation quality or with low minor allele counts, since inclusion of such variants can lead to unstable genetic effect estimates and increase the rate of type I errors  13. A widely used QC strategy for the identification of meta and summary data errors in GWAS meta-analyses is to compare study-specific statistics to external reference datasets or to results based on theoretical expectations  13. Some of these QC procedures can also be used in the MR context to identify potential issues with the summary data. For example, effect allele coding errors can be identified by comparing reported allele frequency to allele frequency in a reference population. However, MR studies are subject to a unique set of challenges that often hamper the application of some previously developed QC checks.
In the present paper we describe a pipeline for the QC of GWAS summary data developed for the Fatty Acids in Cancer Mendelian Randomization Collaboration (FAMRC), a pan-cancer MR study that seeks to evaluate the causal relevance of fatty acids for risk for most major cancers. The basic principle of our QC approach is to identify meta data errors through comparison of study-specific statistics to external reference datasets (e. g. the NHGRI-EBI GWAS catalog and 1000 genome super populations) and to identify potential analytical issues or summary data errors through comparison of reported to expected genetic effect sizes. Using the pipeline, we collated, harmonised and quality controlled 219,851 genetic associations with 87 cancer types in 160 datasets and 48 separate studies, consortia or biobanks.
Key Messages • Meta-data errors (e. g. incorrect specification of the effect allele column) and other analytical issues can introduce substantial bias into Mendelian randomization studies but have received relatively little attention in comparison to other sources of bias, such as violations of instrument variable assumptions. • We found that 13% of the studies in the Fatty Acids in Cancer Mendelian Randomization Collaboration (FAMRC) were subject to meta data errors or analytical issues with the potential to introduce substantial bias into MR analyses (e. g. inferences of causal effect in the wrong direction or bias to the null). • Previously developed guidelines for conducting MR studies provided insufficient safeguards against such errors. • We developed additional guidelines and the CheckSumStats R package (https://github.
Fatty acid instrument selection strategy We searched for GWAS of fatty acids published up to December 2018 by searching the NHGRI-EBI GWAS catalog  14  (https://www. ebi. ac. uk/gwas/) and OpenGWAS  15  (https://gwas. mrcieu. ac. uk/), using fatty acid related search terms, including: fat, acid, fatty acid, DHA, omega, monounsaturated, mono-unsaturated, polyunsaturated, saturated, omega 3 and omega 6. Fifteen studies were identified by this strategy. When full summary association statistics were available, independent genetic associations with P<5e-8 were identified through linkage disequilibrium (LD) clumping (r 2 threshold set to 0. 01), with LD reference panels based on either the European or East Asian 1000 genome superpopulations (clumping was performed using the ieugwasr package  16  ).
We also selected all SNP associations reported in the GWAS catalog, with no specified P value threshold. We further identified SNP proxies, defined as SNPs having an r 2 greater than, or equal to, 0. 8 with any one of the fatty acid SNPs in European or East Asian 1000 genomes reference data. We also searched for alias rsids in dbSNP and 1000 genomes reference data, to make allowance for different rsids across different genome builds for the same SNP. We refer to the genetic associations for fatty acids, their r 2 proxies and alias rsids as the "fatty acid SNP set" (Figure  1 ). To identify meta data errors, summary data errors or other analytical issues, we developed and applied a QC pipeline to the fatty acid summary datasets (described below).
riken. jp/en/), FinnGen (data freeze 1 [January 14 2020]) (https://www. finngen. fi/fi) and UK Biobank  20, 21  , using the Open GWAS platform  15  and ieugwasr package  16. We prioritised studies of cancer incidence and excluded studies of cancer survival, mortality or progression-related phenotypes. For datasets obtained via correspondence, studies were invited to share summary data up until December 2019, after which data collection was closed. Example data sharing instructions can be found in the supplementary materials. For each SNP, we asked studies to provide a minimum of: effect estimates (log odds ratios and standard errors), the effect allele, non-effect allele and effect allele frequency. We also asked studies to provide metrics of SNP genotype quality, such as P values for Hardy-Weinberg equilibrium and metrics of imputation quality, such as info scores.
Cancer data preparation and harmonisation For each cancer with full summary data, we extracted the following 3 sets of SNPs: 1) The fatty acid SNP set 2) The 1000 genomes reference set 3) The GWAS catalog "top hits" for cancer When full summary data were not provided, QC analyses were restricted to the 'fatty acid SNP set'. We next formatted the cancer summary datasets to have similar tabular formats (e. g. where results were distributed across multiple files we merged these together) and to have consistently named data fields. SNPs without rsids were mapped to an rsid using the reported chromosome and base pair position. We excluded duplicate and triallelic SNPs as well as SNPs with missing effect sizes and standard errors or with a minor allele count less than 50 in either cases or controls.
Quality control pipeline to identify analytical issues or summary and meta data errors To identify meta data errors, summary data errors or other analytical issues, we developed a QC pipeline based on the R programming language and associated packages  16, [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33]. We used the pipeline to: 1) confirm the identity of the effect allele column; 2) to confirm the identity of the effect allele frequency column; and 3) to identify analytical issues or potential errors in the summary data (e. g. an unusual number of GWAS hits or unusual distributions in effect sizes). All the functions and tests of the QC pipeline are available to other researchers via the CheckSumStats package (https://github. com/MRCIEU/CheckSumStats).
Instrument-specific quality control To identify potential analytical issues or errors in the genetic instruments for fatty acids, we compared genetic association results identified through LD clumping (r 2 =0. 01 and kb=10,000) to associations in the GWAS catalog. We set the significance threshold for LD clumping to the threshold reported in the fatty acid GWAS: 5-e8 in CHARGE  34  , SCHS  35  and NHAPC/MESA 36 , 1e-8 in the Framingham study  37  , 2. 3e-9 in Kettunen et al  38  and 1. 03e-10 in the TwinsUK/KORA study  39  and used the 1000 genomes European superpopulation as an LD reference panel (except for the SCHS, for which the East Asian superpopulation was used). We searched the GWAS catalog for the lead SNP, identified by our clumping procedure, as well as SNPs within 200,000 base of the lead SNP (associations were retrieved from the GWAS catalog via the gwasrapidd package  33  ).
Confirming the effect allele column To identify incorrect specification of the effect allele column, we compared summary association statistics in the test dataset (either a fatty acids or cancer dataset) to summary association statistics in the NHGRI-EBI GWAS catalog  14. The latter is a manually curated database of 251,401 genetic associations from 4961 publications (as of April 2021) and includes information on effect alleles, effect sizes and EFOs. The genetic associations in the manually curated database typically correspond to the statistically significant findings ("top hits") from published studies (often defined as P<5e-8). In recent years, the GWAS catalog has started to host full GWAS summary statistics. However, for this QC step, we are referring exclusively to the manually curated database of published "top hits".
In the first step, we searched the GWAS catalog for SNPs associated with the EFO or reported trait of the test dataset. Second, for each SNP associated with the EFO term, we extracted from the GWAS catalog the effect size, standard error, effect allele, effect allele frequency and study ancestry (genetic associations were retrieved via the gwasrapidd package  33  ). SNPs missing any of this information, or that were palindromic, were removed. Third, genetic associations for these SNPs were then extracted from the test dataset. Fourth, the effect sizes and effect allele frequencies from the GWAS catalog and test dataset were harmonised to reflect the same effect allele and compared in scatter plots (constructed using the ggplot package  23  ). Comparisons were restricted to populations of European or East Asian ancestry.
We then inspected the scatter plots for conflicting directions of association. For example, we declared a conflicting direction of association if the effect allele was associated with higher cancer risk in the GWAS catalog but was associated with lower risk in the test dataset. The level of conflict was further labelled as 'high' if the P value for the association was <0. 0001 in both the GWAS catalog and the test dataset, and as 'moderate' if not, so as to make allowance for chance deviations in effect direction in small studies. If the test dataset and the data in the GWAS catalog corresponded to the same publication, the conflict level was labelled as 'high' regardless of the P value strength. For comparisons of allele frequency, we declared a conflict if effect allele frequency was not greater (or less) than 0.
5 in both datasets. The level of conflict was further labelled as high if minor allele frequency was ≤0. 4 in both datasets, and as moderate if not. The latter step makes allowance for chance deviations in allele frequencies for SNPs with minor allele frequencies close to 0. 5. Conflicts were also labelled as high if allele frequency differed by more than 10 points between the test and reference datasets. When interpreting the scatter plots, it is important to take into account the total number of SNPs in the comparison as well as the ancestry of the test and reference datasets. Conflicting associations are more likely to reflect true effect allele coding issues when the conflict is systematic across a large number of SNPs and when the ancestry of the datasets being compared is the same. When a substantial proportion of SNPs displayed effect or allele frequency conflicts, we flagged the test dataset as containing a potential effect allele meta data error.
Confirming the effect allele frequency column To confirm the effect allele frequency column, we compare the test datasets to two types of reference datasets: the 1000 genomes project  40  and the exposure study. In the case of the present analysis, we used the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE) and the Singapore Chinese Health Study (SCHS) as representative of exposure (i. e. fatty acid) studies in Europeans and East Asians, respectively. For comparisons with the 1000 genomes project, we created a reference dataset of 2297 SNPs that have the same minor allele across the African, European, East Asian, American, South Asian and Global super populations and that also have a minor allele frequency between 0. 1 and 0. 35 (this dataset is available to other researchers in the CheckSumStats R package).
We refer to the 2297 SNPs as the "1000 genomes reference set". For comparisons with CHARGE and the SCHS, we created a reference dataset corresponding to the fatty acid SNP set described above (see fatty acid instrument selection strategy). We then compare minor allele frequencies between the test dataset and the reference dataset. The comparison involves the following steps. First, we merge the test and reference dataset. Second, we recode the reported effect allele and reported effect allele frequency in the test dataset to reflect the minor allele in the reference dataset. Third, we compare minor allele frequencies between the datasets in scatter plots  23  and inspect the plots for conflicting patterns. A conflict is declared for individual SNPs if their allele frequency is greater than 0.
5 in the test dataset. If the frequency is also greater than, or equal to, 0. 58, the conflict level is upgraded to 'high' (to make allowance for chance deviations). Conflicts are also labelled as high if allele frequency differs by more than 10 points between the test and reference datasets. If an inverse correlation is observed across the vast majority of SNPs this indicates that the conflict is systematic and that the reported effect allele frequency actually corresponds to the non-effect allele. When there is a conflict for approximately half the SNPs, this implies that the reported effect allele frequency column actually corresponds to the minor allele and that the minor allele is not consistently the effect allele. In the latter situation, the scatter plot will show two separate groups of SNPs, one with a positive correlation, and the other with an inverse correlation, in allele frequency between the datasets.
Fatty acid datasets We identified 15 GWAS analyses of 53 fatty acid characteristics. Nine of the 15 GWAS analyses were conducted by, or overlapped with, the CHARGE consortium. The median sample size per analysis was 7,811 (min=284; max=17,267), including analyses of 10 monounsaturated fatty acids (MUFAs), 15 saturated fatty acids, 6 omega 3 PUFAs, 11 omega six PUFAs and 11 other characteristics (Supplementary table  1 ). The 15 GWAS analyses corresponded to seven independent studies or consortia and 15 separate publications  [35] [36] [37] [38] [39] [43] [44] [45] [46] [47] [48] [49] [50] [51] [52]. An int raction study was the only fatty acid GWAS excluded  53. We subsequently invited the identified studies to share full summary data with the FAMRC (except for Shin et al  39  and Kettunen et al  54  , which were already available via Open GWAS).
5, or as SNPs that were present in only one study  34  ) had not been performed on the dataset posted to the CHARGE website. After excluding these SNPs, following recommendations of the data provider  34  , we observed a strong linear relationship between the reported and expected effect sizes and a slope of 1. 02 (Supplementary figure  6 ). The 109 independent GWAS hits also decreased to seven in the cleaned dataset, all of which mapped to the FADS genomic region on chromosome 11 or PDXDC1 on chromosome 16, established GWAS hits for fatty acids (and therefore unlikely to be false positives). In the SCHS, the relationship between the expected and reported effect sizes was skewed by a single outlier SNP (Supplementary figure  4 ). Further investigation revealed that the outlier was due to incorrect specification of the sample size for this SNP.
Cancer datasets As of January 2020, we had collated 166 summary genetic datasets from 51 cancer studies  17,. Two da asets did not report information on the effect allele and effect allele frequency and were therefore excluded  97, 98. The 164 retained datasets included 58 obtained via correspondence with study authors, 6 from the GWAS catalog and 100 from the Open GWAS project  104. Of the 100 Open GWAS datasets, 12 were from Biobank Japan, 29 were from FinnGen, 30 were from UK Biobank, 26 were from GWAS meta-analysis consortia and 3 were from other studies. Further details of the cancer studies can be found in Table  1  and Supplementary table 5. Effect allele frequency was available in 155/164 datasets, metrics of imputation quality (r 2 or info scores) were available in 53/164 datasets and P values for deviations from Hardy Weinberg Equilibrium were available in 6/164 datasets.
Of 70 datasets derived from GWAS meta-analyses of multiple studies, P values for between-study heterogeneity were available in 18 and number of studies per SNP was available in 16. We extracted three sets of genetic associations from each dataset for which full GWAS results were available (130 datasets in total): 1) the fatty acid SNP set, 2) the 1000 genomes reference set and 3) known cancer hits in the GWAS catalog. For 34 datasets, only a subset of GWAS results, corresponding to the fatty acid SNP set, was available. We excluded duplicate and triallelic SNPs, SNPs with missing effect sizes or standard errors, SNPs that could not be mapped to an rsid and SNPs with a minor allele count in cases less than 50. After these exclusions, there were 401,026 genetic associations with cancer across 163 datasets in 49 studies.