dataesr/scientific-paragraphs-categorization huggingface dataset:
We opted to present local and global models as graphical networks to better communicate key quality measurements such as precision, complexity, and other statistics. The edge colors in the graph provide an overview of the measurements and allow for quick identification of salient sub-graphs (very good or very bad). We have seen in the use-case described above that the back and forth between local and global levels, user interactions and computations, allowed the efficient testing and questioning of various plausible hypotheses emerging from the examination of the available dataset. It has been convenient for instance to let variables of the cellular and population-bioprocess scales depend on variables at the genomic scale, and equally easy to prohibit their dependence on temperature and growth phase.
In the use-case (Production and Freeze-drying process of Lactic Acid Bacteria), the proposed interactive process has been proved useful for: • Retrieving known facts, which allows the user to build trust in the system. Lactococcus lactis is a well-known system, so that the final graph was considered as representing a biological reality under the experimental conditions of the data. This model was also expected to help quantify some known facts on the available dataset: for the current experiment, the user estimated the shape of the equations and their parameters, which was interesting to quantify the relative importance of the variables inside an equation, but the absolute values were not considered as directly interpretable. However, developing additional visualizations to explore the behaviour of an equation with respect to the data and its uncertainties was considered interesting.
Conclusions The aim of the use-case outlined in this paper was to assess an interactive modelling scheme in real-world conditions, making use of both a real-world dataset and expert input. The use-case that was considered is related to the multi-scale engineering process of freeze-drying of Lactic Acid Bacteria, which presents several challenges including: (i) the paucity and variability of the experimental dataset, (ii) the complexity and multi-scale nature of biological phenomena, and (iii) the wide knowledge about the biological mechanisms involved in this process that are not captured in the data (experts knowledge). In such a case it is obvious that classical machine learning approaches fail. We have proposed a way to exploit the power of machine learning techniques while letting domain experts conveniently control the modelling process and inject their insights using adapted visualizations and user interactions.
The experiment proved that it was possible for an expert to quickly identify a relevant group of genes, then elaborate a series of coherent local models, to finally building a global model that reasonably fits the dataset. A domain expert was also able to test the overall consistency of various hypotheses and compare them to the available data. The resulting multi-scale model features the experts' current understanding of the process and reveals interesting hypotheses which can then be explored in future experiments. To the best of our knowledge, we produced the first multi-scale model that represents the Lactic Acid Bacteria freeze-drying process. This interactive modelling scheme, based on the iterative use of GIC and IMSIE, can be transferred to other strains of Lactic Acid Bacteria with other experimental settings.
It can also be applied to other multi-scale biological systems. This will require the integration of other types of models like Bayesian network  [95] , which are may be more suitable to characterize uncertain relationships between variables. Systems of differential equations may also be considered to deal with time series data. Visualization may also be improved, for instance by displaying various types of statistical information, say to identify outliers or to uncertainty sources (measurement error, model parameter sensitivity, etc). User interactions may be extended to let domain experts edit equations and models or create new variables. The user study described in this paper demonstrates the viability of the proposed approach: the experiment was conducted using a well-known bacteria and a challenging dataset that is too sparse and ill-conditioned for classical machine learning.
• Integrating the different interpretations into a global model (new knowledge) and confront it with the dataset. The graph enables correlation of different scales that provide insight into the global response of the microorganism under cell preparation conditions. It shows the effect of the process on bacterial physiology at the cellular level (membranes) and how it is regulated at the molecular level through the clusters highlighted by GIC. • Summarizing and simplifying the current knowledge on the process: from an initial model with 2,741 genes, 33 variables and 165 equations to a synthetic version with 11 gene clusters, 16 variables associated to only 16 equations.
This problem is combinatorial, it is tractable with a manual or exhaustive search for small instances. However, biosystem datasets typically feature a considerable number of variables, so even if an expert could in theory manually select an equation/local model for each, it is intractable in practice. IMSIE computes an optimal global model, in the sense that it is able to fit the values of experimental data. For that purpose, we use an evolutionary algorithm [71] , a class of stochastic optimization techniques loosely inspired by the neo-Darwinian paradigm of natural selection, able to return solutions of reasonable quality in a limited amount of time. Once a global model has been computed, the user has access to all equations through an interface and can modify the constraints on the graph, enforce some equations or links, and prohibit some others.
Exposure and outcome traits can vary from relatively simple molecular traits, such as expression or protein traits, to highly complex traits, such as human behaviours and disease outcomes. cancers. The basic principle of our QC approach is to identify metadata errors through comparison of studyspecific statistics to external reference data sets [e. g. the National Human Genome Research Institute-European Bioinformatics Institute (NHGRI-EBI) GWAS catalogue 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 created a data set of 219 842 genetic associations with 90 cancer types, generated in analyses of 566 665 cancer cases and 1 622 374 controls in 51 studies.
Fatty acid instrument selection strategy We searched for GWAS of fatty acids published up to December 2018 by searching the NHGRI-EBI GWAS catalogue  13  (https://www. ebi. ac. uk/gwas/) and Open GWAS  14  (https://gwas. mrcieu. ac. uk/), using fatty acid-related search terms, including: fat, acid, fatty acid, DHA, omega, monounsaturated, monounsaturated, 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  15  ).
We also selected all SNP associations reported in the GWAS catalogue, with no specified P-value threshold. We further identified SNP proxies, defined as SNPs having an r 2 of !0. 8 with any one of the fatty acid SNPs in European or East Asian 1000 genomes reference data. We also searched for alias reference SNP identifiers (rsids) in the Single Nucleotide Polymorphism Database (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 metadata errors, summary-data errors or other analytical issues, we developed and applied a QC pipeline to the fatty acid summary-data sets (described below).
Cancer outcome selection strategy We searched for studies of cancer in the GWAS catalogue  13  up to 1 November 2018. Search terms included: cancer, carcinoma, neoplasm, neoplastic, tumor, tumour, adenocarcinoma, glioblastoma, leukemia, lymphoma, melanoma, meningioma, mesothelioma, myeloma, neuroblastoma and sarcoma. When multiple studies of the same cancer outcome were identified, we prioritized the larger study. When not already available via Open GWAS  14  (https://gwas. mrcieu. ac. uk/) or the GWAS catalogue, we invited the identified studies to share summary data for all SNPs in their GWAS analysis (defined as 'full GWAS summary data'). If studies were unable to share full summary data, they were invited to share genetic association results for the 'fatty acid SNP set'. We further downloaded summary association statistics for cancers from Biobank Japan  [16] [17] [18]  (http://jenger.
riken. jp/en/), FinnGen [data freeze 1 (14 January 2020)] (https://www. finngen. fi/fi) and UK Biobank  19, 20  using the Open GWAS platform  14  and ieugwasr package. 15  We prioritized studies of cancer incidence and excluded studies of cancer survival, mortality or progression-related phenotypes. For data sets 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 (available as Supplementary data at IJE online). 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 (HWE) and metrics of imputation quality, such as info scores.
Cancer data preparation and harmonization For each cancer with full summary data, we extracted the following three sets of SNPs: i. the fatty acid SNP set; ii. the 1000 genomes reference set; iii. the GWAS catalogue 'top hits' for cancer. 'Top hits' refers to the strongest or statistically significant genetic associations for the cancer of interest from published studies (often defined as P-values <5Â10 -8 ). When full summary data were not provided, QC analyses were restricted to the 'fatty acid SNP set'. We next formatted the cancer summary-data sets 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.
QC pipeline to identify analytical issues or summary and metadata errors To identify metadata errors, summary-data errors or other analytical issues, we developed a QC pipeline based on the R programming language and associated packages. 15, [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32]  We used the pipeline to: (i) confirm the identity of the effect allele column (Figure  2 ); (ii) confirm the identity of the effect allele frequency column (Figure  2 ); and (iii) 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).
38  We used the 1000 genomes European superpopulation as an LD reference panel for CHARGE, the FHS, Kettunen et al. and TwinsUK/KORA, and the East Asian superpopulation as an LD reference panel for the SCHS and NHAPC/MESA. We searched the GWAS catalogue for the lead SNP, identified by the latter clumping procedure, as well as SNPs within 200 000 base pairs of the lead SNP (associations were retrieved from the GWAS catalogue via the gwasrapidd package  32  ). Data sets were flagged for further investigation if any lead SNPs were absent from the GWAS catalogue. We additionally searched for metadata and summary-data errors in the fatty acid GWAS results through comparisons of effect alleles and allele frequency with external reference data sets and by comparing reported to expected effect sizes (described below).
Confirming the effect allele column To identify incorrect specification of the effect allele column, we compared summary association statistics in the test data set (either a fatty acids or cancer data set) to summary association statistics in the NHGRI-EBI GWAS catalogue  13  (Figure  2 ). 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 catalogue 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 catalogue for SNPs associated with the EFO or reported trait of the test data set. Second, for each SNP associated with the EFO term, we extracted from the GWAS catalogue the effect size, standard error, effect allele, effect allele frequency and study ancestry (genetic associations were retrieved via the gwasrapidd package  32  ). SNPs missing any of this information, or that were palindromic, were removed. Third, genetic associations for these SNPs were then extracted from the test data set. Fourth, the effect sizes and effect allele frequencies from the GWAS catalogue and test data set were harmonized to reflect the same effect allele and compared in scatter plots (constructed using the ggplot package  22  ). Comparisons were restricted to populations of European or East Asian ancestry.
The latter are subject to relatively rigorous reporting standards, whereas there is little consensus on the format that GWAS results should take when posted to study-specific websites. Online platforms and databases that aggregate full summary association statistics from different studies may also be susceptible to this kind of error. It is important to consider the impact of sample size when interpreting the presence of GWAS hits in the test data set that are absent from the GWAS catalogue. For example, if the GWAS being investigated is unpublished and is larger than any previously published study, we can reasonably expect a number of genetic associations to be identified that are absent from the GWAS catalogue but are nevertheless true novel hits. When the GWAS being investigated is smaller than any previously published study, one should be more sceptical of any GWAS hits that are previously unreported.
Two-sample population assumption One of the key assumptions made in two-sample MR is that the studies used to define the exposure and the outcome come from the same population. The comparison of allele frequencies between test data sets and reference populations can in principle be used to evaluate this assumption. For example, in our own analyses, allele frequencies in the European origin cancer studies and 1000 genomes European super population were consistently strongly correlated (the same applied to the East Asian origin studies and the 1000 genomes East Asian super population), indicating that the reported study ancestries were broadly accurate. However, our QC procedure was not designed to specifically test for ancestral origins and was restricted to SNPs with a narrow allele frequency range.
Conclusion We have developed a QC pipeline that can be used to flag metadata and summary-data errors and a range of analytical issues in GWAS results, which in turn can be used to enhance the integrity of downstream two-sample MR analyses. We applied the pipeline to the FAMRC, identifying errors with potential to introduce substantial bias in seven studies. After resolving analytical issues and excluding problematic studies, 160 data sets from 51 studies were retained, representing 90 unique cancer types generated in analyses of 566 665 cancer cases and 1 622 374 controls. The methods developed here are available to other researchers via the CheckSumStats R package (https:// github. com/MRCIEU/CheckSumStats). contributed to the interpretation of the data and revision of the article for critically important content.
Acknowledgements We gratefully acknowledge the participants and investigators of all studies that shared genetic summary data (further details of the studies can be found in Table 1 and Supplementary Table S5): the  23andMe Non-Melanoma Skin Cancer Study, Asian Colorectal Cancer Consortium, B Cell Childhood Acute Lymphoblastic Leukemia Study, B Cell Non-Hodgkin Lymphoma Study ,  Biobank Japan ,  CHARGE Consortium Fatty Acid Working Group, Childhood Acute Lymphoblastic Leukemia Study, China Hepatocellular Carcinoma Study, Endometrial Cancer Association Consortium, EPITHYR, Esophageal Adenocarcinoma Study, FHS, FinnGen, Genetics and Epidemiology of Colorectal Cancer Consortium, Colorectal Transdisciplinary study, Colon Cancer Family Registry, Glioma International Case-Control Study, MD Anderson Cancer Center, GliomaScan ,  Harvard Non-Melanoma Skin Cancer Study, Hodgkin Lymphoma Study , InterLymph,  International Head and Neck Cancer Epidemiology Consortium, KidRISK, Korean Chronic Myeloid Leukemia Study, Korean Hereditary Breast Cancer study, Malaysia Nasopharyngeal Carcinoma Study, Malignant Pleural Mesothelioma Study, Melanoma Meta-Analysis Consortium , Meta-analysis of  Cervical Cancer Studies, Multiple Myeloma Study, NanjingþBeijing Upper Gastrointestinal Cancers Study ,  NCI Upper Gastrointestinal Cancer Study, Neuroblastoma Study, Nijmegen Bladder Cancer Study, Pancreatic Cancer Case-Control Consortium, Pancreatic Cancer Cohort Consortium, SCHS ,  St.
Data availability Full GWAS summary data for the included cancer studies can be downloaded from Open GWAS (https://gwas. mrcieu. ac. uk/) or the GWAS catalogue FTP site (https://www. ebi. ac. uk/gwas/home), or obtained by direct correspondence with the relevant study. Supplementary Table  S5  contains further details on the underlying source for each cancer GWAS summary-data set, such as Open GWAS identifiers and columns indicating whether the data were downloaded from the GWAS catalogue or obtained by correspondence. Full GWAS summary data for cancers generated in UK Biobank, under application number 15825, can be downloaded from either Open GWAS or the University of Bristol data repository at https://doi. org/10. 5523/bris. aed0u12w0ede20olb0m77p4b9. Summary genetic data corresponding to the fatty acid SNP set (i.
Diacritic-free morpho-phonology The analysis we propose builds on the analytic achievements of  Passino (2009)  and  Lampitelli (2010 Lampitelli ( , 2011 Lampitelli ( , 2014) )  further developing their decompositional approach. The novelty of current proposal is its considerable uniformity, while maintaining strict modularity. Unlike  Faust & Lampitelli (2012)  all nouns are syntactically identical regardless of their phonological shape, and class features are abolished from this dataset, which acts as a ready explanation for class never being used as syntactic feature in Italian  (Acquaviva 2008). Moreover, all alternations and all the variation in root-behaviour is derived by the phonology in a unified manner, there are no morphologically exceptional classes. Not even loanwords are exceptional, in our analysis they can be fully integrated into the same derivation as native nouns.
The discussion demonstrated the need to share some thoughts, set out the publisher's and journals' position, and help widen the discussion to the research community via this editorial. We focus first on the recently launched ChatGPT3, a "large language model" AI chatbot built by OpenAI [3], a company now heavily invested in by  Microsoft (chat. openai. com [4] ). The software allows free question-and-answer discussion, with conversation history allowing refinement of previous question-responses. It is 'trained' to give realistic responses, although these can sometimes be factually incorrect. It represents a step change from earlier clumsy AI-language models, and 5 min spent playing with ChatGPT3 can quickly convince you of its potential for disruptive impact across many walks of life. The challenges for science and scientific publication will only become more pronounced as it moves incrementally up the S-curve in product development.
Microsoft has recently implemented a variant into their search engine "Bing", into Microsoft Teams where it will be able to write meeting summaries from video conference calls. Microsoft is also suggesting that it will soon be a feature in their Office suite. The impact on science and science publishing of "large language models" such as this, and AI in a broader sense, will be significant. In this context, Elsevier has released a general policy concerning the use of such models in the preparation of documents. We summarize here the key points relevant for "Carbon" and its companion journal "Carbon Trends": • Where authors use AI and AI-assisted technologies in the writing process, these technologies should only be used to improvereadability and language of the work and not to replace key researcher tasks such as producing scientific insights, analyzing and interpreting data or drawing scientific conclusions.
It is important to support forums discussing this and other related issues in data reproducibility, access and standards and ultimately converge to a common view. Furthermore, with the advent of AI tools, it is more important than ever to stress the importance of reporting negative results: just like us humans, AI tools can learn as much from negative results as they do from positive ones. It is almost impossible to predict how much impact AI-and AI-tools will have in even 12 months' time, and thus it is premature for us to develop a general editorial stance on AI and AI-tools. The Carbon and Carbon Trends editorial team is continuously monitoring the field and our journals' policy will evolve as appropriate in a timely manner. We encourage our authors to focus their efforts on improving the scientific method emphasizing novelty, reproducibility, and data sharing.
This unbalanced progress has then, as we can see in C. Buckl et al. ,  [3] , considerably increased the complexity of the system and cost for both developing and maintaining new services, while complicating the path to evolve application development methodology and bring in new innovative software-related business proposals. In this context, adapting the current Electrical/Electronic (E/E) and Software architectures to the upcoming requirements, without forgetting about the already present base, has become a vital necessity, but also a major challenge for the automotive sector. As could not be otherwise, multiple industrial and academic researchers center themselves nowadays on trying to stage the phases of this transformation. On the one hand, if we look more closely at the industrial research initiatives, we can appreciate an increasing emergence of new standardization groups and projects such as SOAFEE,  [4] , Eclipse Software-Defined Vehicle (SDV),  [5] , Eclipse Automotive,  [6] , Android Automotive,  [7] , MIH EV,  [8] , eSync,  [9]  or Connected Vehicle Systems Alliance (COVESA)  [10] , previously called GENIVI Alliance, trying to establish some inter-company basis for the architectural transformation.