dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, we programmatically ensure the consistency of CSV files generated by BioCypher, allowing speedy and secure building of large databases on the fly. Since Neo4j v5, admin import also allows incremental updates of already existing databases, increasing the usefulness of this feature. 1A ). This shi s maintenance towards the primary source and thus reduces maintenance effort: instead of maintaining each primary resource at the integrated KG level, only one reusable adapter for each resource is necessary. The primary adapters provide an additional level of flexibility to the user by providing accessible insight into the contents of each primary resource, which can be extensive. For instance, in the adapter for the UniProt knowledge base, the user can select their favourite species, fields of protein information such as the length or mass of the protein, and relationships to import, such as the host organism or the coding gene of each protein.
Tumour board Cancer patients nowadays benefit from a large range of molecular markers that can be used to direct treatment and estimate prognoses  13, 34. In the context of the DECIDER project (www. deciderproject. eu), we are creating a platform to inform the tumour board of actionable molecular phenotypes of high grade serous ovarian cancer patients. The current manual workflow of discovering actionable genetic variants consists of multiple complex database queries to different established cancer genetics databases  13, 35, 36. The returns from each of the individual queries then need to be curated by human experts (geneticists) in regard to their identity (e. g. identify duplicate hits from different databases), biological relevance, level of evidence, and actionability. The heterogeneous nature of results received from different primary database providers makes this a time-consuming task.
To facilitate the discovery of actionable variants and reduce the manual labour of human experts, we use BioCypher to transform the individual primary resources into an integrated, task-specific KG. Through mapping of the contents of each primary resource to ontological classes in the build process, we essentially remove the need to manually curate and harmonise the individual database results. This mapping is determined once, at the beginning of the integration process, and results in a BioCypher schema configuration that details the types of entities in the graph (e. g. , patients, different types of variants, related treatment options, etc. ) and how they are mapped and thus integrated into the underlying ontological framework. As a second step, datasets that are not yet available from pre-existing BioCypher adapters are adapted in similar fashion to yield data ready to be ingested by BioCypher.
The code for this project can be found at https://github. com/oncodash/oncodashkb. We make use of the ontology manipulation facilities provided by BioCypher to extend the broad but basic Biolink ontology at certain branches where it is useful to have more granular information about the data that enters the KG. For example, the exact type of genetic variants are of high importance in the molecular tumour board process, but Biolink only provides a generic "sequence variant" class in its schema. Therefore, we extended the ontology tree at this node with the very granular corresponding subtree of the Sequence Ontology (SO,  37  ), yielding a hybrid ontology with the generality of Biolink and the accuracy of a specialised ontology of sequence variants (Figure  S2 ). Due to the mechanism provided by BioCypher, this hybridisation can be performed by providing only the minimal input of the sequence ontology URL and the nodes that should be the point of merging ("sequence variant" in Biolink and "sequence_variant" in SO).
S2: The manipulation feature used to extend Biolink with more refined ontologies. Since Biolink a but general representation of biomedical classes, we extend the "sequence variant" the corresponding granular from the specialised Sequence Ontology. Similarly, information about cancer and specific tumour types are added from Disease Ontology and OncoTree. Once the database has been created through BioCypher, the process of querying for an actionable variant and its associated treatment options for a given patient is greatly simplified. This kind of approach is also known to improve the concordance of knowledge base sources, the ability to incorporate external clinical resources, and the recovering of evidence only represented in a single resource  13. The major advantage of using BioCypher to integrate several resources is the formal representation of the process provided by the schema configuration, which allows for a simple description and long-term maintenance.
Network expansion Database schemata of large-scale biomedical knowledge providers are tuned for effective storage. For analysis, the user may benefit from a more dedicated schema type corresponding to the biological question under investigation. We created BioCypher with the objective to simplify the transformation from storage-optimised schemas to analysis-focused schemas. Given one or multiple data sources, the user should be able to quickly build a task-specific knowledge graph using only a simple configuration of the desired graph contents. We demonstrate the simplifying capabilities using an interaction-focussed version of the Open Targets graph database as an example  40. Barrio-Hernandez et al. used this graph database to inform their method of network expansion  41. The database runs on Neo4j, containing about 9 million nodes and 43 million edges.
It focuses on interactions between biomedical agents such as proteins, DNA/RNA, and small molecules. Returning one particular interaction from the graph requires a Cypher query of ~13 lines which returns ~15 nodes with ~25 edges (variable depending on the amount of information on each interaction). A procedure to collect information about these interactions from the graph is provided with the original manuscript  41  , containing Cypher query code of almost 400 lines (http:// p. ebi. ac. uk/pub/databases/intact/various/ot_graphdb/current/apoc_proce dures_ot_data. txt). Still, this extensive query only covers 11 of the 37 source labels, 10 of the 43 target labels, and 24 of the 76 relationship labels that are used in the graph database, offering a large margin for optimisation in creating a task-specific KG.
A er BioCypher adaptation, the KG (covering all information used by Barrio-Hernandez et al. ) has been reduced to ~700k nodes and 2. 6 million edges, a more than ten-fold reduction, without loss of information with regard this specific task. Compared to the original file of the database dump (zipped, 1. 1 GB), the BioCypher output is ~20-fold smaller (zipped, 63 MB), which greatly facilitates sharing and accessibility (e. g. by simplifying online access via Jupyter notebooks). The Cypher query for an interaction has been reduced from 13 query lines, 15 nodes, and 25 edges to 2 query lines, 3 nodes, and 2 edges (Figure  S3 ).
ion)-[:interactorA]-(:Interactor)-[:preferredIdentifier]-("EP300")-[:database]-("hgnc")`to qualify one protein-protein-interaction can be reduced to("EP300")-[:enzyme]-("phosphorylation")-[:enzyme target]-("SNAI1")`. Arguably, the shorter BioCypher query is also more informative, since it details the type of interaction as well as the roles of the participants. In addition, this representation returns sources of information about the proteins and the interaction as properties on nodes, and the hierarchical ontology-derived labels provide rich information about the biological context. For instance, the first ancestor labels of the "phosphorylation" node are "enzymatic interaction", "direct interaction", and "physical association", grounding this specific interaction in its biological context and enabling flexible queries for broader or more specific terms.
Subgraph extraction For many practical tasks in the workflow of a research scientist, the full KG is not required. For this reason, building complex and extensive KGs such as the CKG  20  or the Bioteque  7  would not be sensible in all use cases. For instance, in the context of a proteomics analysis, the user would only like to contextualise their list of differentially abundant proteins using literature connections in the CKG, rendering much of the information on genetics and clinical parameters unnecessary. In addition, the KG may contain sensitive data on previous projects or patient samples, which cannot be shared (e. g. in the case of publishing the analysis), causing reproducibility issues. Likewise, some datasets cannot be shared due to their licences. With BioCypher, a subset of the entire knowledge collection can be quickly and easily created, taking care to not include sensitive, irrelevant, or unlicenced data.
The analyst merely needs to select the relevant species (e. g. proteins, diseases, and articles) and their relationships in the BioCypher configuration. BioCypher then queries the original KG and extracts the required knowledge, conserving all provenance information, and yielding a much reduced data set ready for sharing. Since a complete CKG adapter already existed (found at https://github. com/saezlab/CKG-BioCypher/), the subsetting required minimal effort; i. e. , the only required step was to remove unwanted contents from the complete schema configuration. The code for this task can be found at https://github. com/saezlab/CKG-BioCypher/tree/subset.
Federated learning Federated learning is a machine learning approach that enables multiple parties to collaboratively train a shared model while keeping their data decentralised and private  44, 45. This is achieved by allowing each party to train a local version of the model on their own data, and then sharing the updated model parameters with a central server that aggregates these updates. However, most machine learning algorithms depend on a unified structure of the input; when it comes to algorithms that combine prior knowledge with patient data, a large amount of harmonisation needs to occur before the algorithms can be applied. BioCypher facilitates federated machine learning by providing an unambiguous blueprint for the process of mapping input data to ontology. Once a schema for a specific machine learning task has been decided on by the organisers, the BioCypher schema configuration can be distributed, ensuring the same database layout in all training instances.
Larger ontologies such as HPO  48  and SNOMED-CT  49  are complex and expanded constantly. In addition to the technical challenges, the legal requirements of patient confidentiality and data protection necessitate extreme care in the processing of all data, hindering information sharing between collaborators. All of the above poses great challenges in data integration in the clinical setting. Using BioCypher, we enable a federated learning pipeline by supplying build instructions for each local database in the form of the schema configuration. At each location, a task-specific KG is created from public data with the Clinical Knowledge Graph as baseline, using the subsetting facilities described in the case study "Subsetting large datasets". A erwards, the sensitive patient data (e. g. , germ-line genetic variants) are integrated into this KG at each location, using the BioCypher schema configuration to specify the type of data involved (e.
Data integration The German Center for Diabetes Research (DZD, www. dzd-ev. de) has developed a knowledge graph to support data integration for translational research. The internal KG instance provided the foundation of the open-source CovidGraph project  50  which is now maintained by the HealthECCO community (www. healthecco. org). At the core of the DZD KG is a data ingestion pipeline for PubMed that transforms publication data into a detailed graph representation, including authors, affiliations, references, and MeSH term annotations. The PubMed graph contains 350 million nodes and 850 million relationships, as well as data on biological entities (genes, transcripts, proteins), their functional annotations, and biochemical interactions. This KG is used to link internal research data to public knowledge and to generate new research hypotheses.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  965193  for  DECIDER  and No  116030  for TransQST, and the  German Federal Ministry of Education and Research (BMBF, Computational Life Sciences  grant no. 031L0181B ). We are thankful to  Henning Hermjakob ,  Benjamin Haibe-Kains ,  Pablo Rodriguez-Mier ,  Daniel Dimitrov , and  Olga Ivanova  for feedback on the initial dra of the manuscript.
This study investigates the variations of surface Moho inverted from Bouguer gravity data beneath the northwestern Arabian plate. The results were obtained by applying the 3DINVER program developed in a MATLAB environment using the Parker-Oldenburg method. The calculation results show that the maximum Moho depth observed in the western Arabian shield approaches more than 40 km, while the minimum values of 16-20 km are mainly concentrated beneath the Mediterranean Sea. A key innovation of this study is its implementation of exponential density contrast decrease in the sediment-basement interface in the Syrian part of the Arabian plate. The gravity anomaly of the sediment is computed using a density contrast which varies exponentially with depth, leading to a more accurate representation of the subsurface structure and offering valuable insights into the geodynamic processes and seismogenic potential of the area.
We concluded that density contrast decreases following an exponential function with depth, based on seismic velocity measurements provided by the Syrian Petroleum Company (Figure  3 ). This exponential function was used in Granser's method  (Chakravarthi et al. , 2003; Pham et al. , 2018)  to compute the Bouguer gravity effect of sediment-basement from the basement data digitized by  Rybakov (2004)  and Jassim and  Goff (2006). Bouguer gravity was corrected for the sediment-basement effect before inverting the data. We obtained the residual Bouguer gravity data that were ready to implement in the inversion process. Before performing the inversion, we applied some processes to achieve the gravity anomaly from the Moho boundary (g m ). Many anomalies and trends are clearly observed on the simplified Bouguer map.
The basement depths used in the aforementioned algorithm were digitized from  Rybakov (2004)  and  Jassim & Goff (2006)  over an area of (33-43 °N and 27-37 °E) described in Figure  4A. The resulting gravity data will be subtracted from the Bouguer gravity data to obtain the residual gravity effect of the Moho discontinuity that will be used in the inversion procedures as g m Δg -g sed + g sb. ( 4 ) The result obtained for gravitational Bouguer anomaly g m is used for inversion to calculate the Moho depth over our study area. Gómez-Ortiz and Agarwal (2005) modified the Parker-Oldenburg iterative method to construct a Moho map under the eastern Mediterranean study area by inverting the gravity anomaly along the horizontal density interface geometry. This code was modified again because it misused Parker's formula by giving incorrect forward and inversion results  (Gao & Sun, 2019).
This technique depends on the Fourier transform for the topography interface, the gravity anomaly, and the relationship between them. Many authors have established methods to determine Moho depth (e. g. ,  Cordell & Henderson, 1968; Rao et al. , 1990). Eq. 5 represents the Fourier transform of gravity data  (Parker, 1973)  in a one-dimensional direction as F Δg x ( ) -2πγρe -k | |z0 ( ) ∞ n 1 -k | | ( ) n-1 n! F h n x ( ) [ ]. ( ) 5 The depth to the uneven interface is computed using MATLAB code with several iterative procedures based on  Oldenburg's (1974)  equation. : h x ( ) F -1 ( ) - F Δg x ( ) e k | |z0 2πγρ - ∞ n 2 k | | ( ) n-1 n! F h n x ( ) [ ] ⎡ ⎣ ⎤ ⎦ , (6) where F(Δg) denotes the Fourier transform of the gravity anomaly ρ and the term h(x) represents the interface topography at the horizontal interface average depth z 0.
The Moho gravity effect after correction by removing the gravity anomaly of the sedimentary-basement boundary and shallow blocks is presented in Figure  5. The anomaly ranges between -60 mGal and 300 mGal. We note the negative anomalies centered beneath the western Arabian shield chains while the maximum values are located in the Mediterranean Sea. The gravity anomaly values range from 100-150 mGal in Syria. These data will be used in the inversion process to obtain the Moho depth under the eastern Mediterranean Sea. A comparison of the two maps in Figures  5, 7  shows good agreement where the gravity anomaly values are almost the same, especially in the northeast. The inverted Bouguer anomaly coincides with the theoretical gravity data at many parts of the study region. The RMS error was just 0.
Conclusion We have applied the 3DINVER program developed in a MATLAB environment using the Parker-Oldenburg method to determine the Moho depth inverted from the Bouguer gravity anomaly with many iterations. Prior to executing the inversion process, we conducted some corrections to remove the gravity effect of the sedimentary-basement boundary by forward modeling  Granser's (1987a)  method that takes into account the variation of density contrast exponentially with the depth. It is clear that there is a sharp and major difference in Moho depth between offshore and onshore areas, confirmed by the inversion of the seismological and gravity data. Both maps coincide with the shallowing of Moho depth toward the west of the DSFS toward the Mediterranean Sea because of the ocean crust and high density.
Acknowledgments The authors express many thanks to  Qing Liang ,  Jinsong Du , and  Dr. Shida Sun  for their sincere criticisms and helpful advice during this research. Thanks are due to  Prof. Jamal Abou-deeb  of  Damascus University  for revising the manuscript. This work was supported by the  National Natural Science Foundation of China  (No. 41604060 ). The gravity data of the investigated region were acquired from the Earth Geopotential Model EGM2008 estimated from altimetry missions, ground data, and GRACE satellite mission (Pavlis et al. , 2013) with resolution (1′ × 1′) shown in Figure 2.
Background In this era of global health crises, public trust in scientists is a crucial determinant of adherence to public health recommendations. Studies of trust in scientists often link sociodemographic and other factors to such adherence but rely on assumptions about scientists and neglect scientific uncertainty. We undertook a cross-sectional mixed-methods study evaluating factors associated with public trust of scientists in Europe, investigating how and why respondents embraced certain claims in scientific debates. Methods A survey was administered to 7000 participants across seven European countries in December 2020. Data concerning sociodemographic characteristics, trust in scientists, information source preferences, COVID-19 experiences and beliefs about pandemic origins were analysed using a multiple regression model.
Following its standard protocol, Ipsos set quotas aligned with nationally representative proportions based on age (18-65 years), gender, geographical region and working status for each country. Ipsos developed a sample of participants from its existing online research panels, contacting potential participants by email to participate. When each quota was filled, Ipsos closed the quota immediately. One thousand respondents between 18 and 65 years old in each country participated. Ipsos did not survey those over age 65 years because its panel surveys cannot ensure representative sampling of this population. The following quantitative data were collected among respondents: socioeconomic and demographic characteristics, including gender identification (male/female/ other/prefer not to answer); trust in sources of medical and scientific information; trust in national, European, and international institutions and authorities, as well as in pharmaceutical companies; perception of vaccine contents, purposes and safety; and political affiliation.
4. 1. 1. Survey participants' open-text responses about COVID-19 treatment, vaccination and SARS-CoV-2 origins were evaluated using thematic analysis. 21  We developed a global codebook and conducted inductive and deductive thematic coding using NVivo software (QSR international, V. 1. 7. 1). We also organised our analyses to categorise descriptions of existing COVID-19 scientific research according to expressed trust in scientists, participant sentiments towards key actors and explanations of responses contending that SARS-CoV-2 was deliberately released. Blank (unanswered) text boxes were not evaluated. To ensure high-quality text analysis, a native speaker of each country language (French, Italian, Spanish, Ukrainian, German, Flemish and Swedish) performed the coding. All coders conferred frequently during the coding process to address and compare transversal codes and themes.
Both Raoult (French dataset) and Trinca (Italian dataset) were applauded for their willingness to treat patients with drugs not recommended by national authorities (ie, hydroxychloroquine). Another respondent cited a 'Nobel prize winner' as a source for the claim that SARS-CoV-2 was not a 'natural' virus. Participants trusting scientists responded somewhat more positively about scientists but mentioned no individual scientists in their responses. Few scientists were named and applauded for their merits, but those mentioned had all challenged mainstream scientific discourses about COVID-19 origins and treatments. Many respondents, although neither lauding or attacking scientists, suggested that scientists themselves were powerless, serving instead more powerful actors, including states, economic interests and pharmaceutical companies.
20 29 30  Our analyses of open-text responses effectively recast our questions about public trust in scientists. First, we found that both respondents trusting and distrusting scientists supported their claims by citing existing scientific research and expressed a need for additional scientific data and research. These results suggest that respondents-even those not trusting national scientists-sought to employ evidence-based thinking. This finding appears to counter claims that those not trusting mainstream scientific discourse act out of emotion or irrationality. 15  Although this question requires further investigation, our results suggest that scepticism of existing scientific knowledge on the eve of COVID-19 vaccine rollout in Europe fuelled demands for more scientific evidence. Whereas physicist Edwin Hubble  31  argued that 'a healthy dose of scepticism' is a prerequisite to scientific thinking, European respondents in this study were highly sceptical of expert judgement.
34  The information revolution once raised hopes for greater public participation in science,  19  but new concerns about a post-truth era  35  have displaced these earlier aspirations. Still, some respondent efforts to 'outscience' the scientists may reflect a continued desire to engage more fully in scientific investigation and findings, to discover truth for themselves. Respondents' clamours for more data may also result from a conscious strategy to set impossible standards of certainty, to generate doubts and to postpone decision-making about viral origins, vaccines or treatments. Proctor and Schiebinger have described a similar strategy among interest groups engaged in scientific controversies. 36  Second, our findings raised the question about who can be considered a scientist. Respondents who did not trust scientists in their own countries nevertheless appeared to mention and trust individuals whom they considered to be scientists, but who were controversial or whose status was disputed (eg, Raoult, Trinca).