dataesr/scientific-paragraphs-categorization huggingface dataset:
The most plausible explanation for this out-of-phase relationship probably lies in the chronological uncertainties of the age models applied to these records. Despite best-efforts to harmonise the different time series in the ACER database using radiometric dating  (Sánchez Goñi et al. , 2017) , the lack of 14 C dates for site MD95-2039 forced us to build the age model for this site by tuning the planktic foraminifera and GRIP ice core oxygen isotopic records  (Roucoux et al. , 2005). This tuning could affect the time series from site MD95-2039 and introduce unacknowledged chronological uncertainties  (Blaauw, 2012; Hu et al. , 2017). To summarise, with the present state of data quality we cannot rule out the idea that timescale uncertainties -rather than climate impact adaptation -caused the lag observed.
Acknowledgements JMPM was funded by a  Basque Government post-doctoral  fellowship. MM's work was supported by the  European Commission via Marie Curie Initial Training Network LINC  (project number  289447 ) under the  Seventh Framework Programme. Thanks to  Charo Sánchez  for help to use the i2BASQUE HPC facilities, to the two anonymous reviewers and Editor ( Olivia Lau ) for their input and comments that have improved the quality of the manuscript. The authors thank the support of the computing infrastructure of the i2BASQUE ( Basque Government ) academic network. The persistence time estimation software is freely available via http://www. climate-risk-analysis. com/software/.
The second step was to separate objects from each other in both bubble and crystal classes. The details of the procedure are described in  Ketcham (2005a; 2005b)  and  Gualda and Rivers (2006). During this procedure, the number of resulting single particles found in each cluster was noted for further statistical analysis. The third procedure of Blob3D, named "EXTRACT," yielded particle shape fabric information such as volume, location, contacts relationships and orientation of the particle's long axis. The latter is called hereafter orientation a p. The three-dimensional directional properties of the shape fabrics were determined with the Quant3D software  (Ketcham, 2005b). Computed axes issued from Quant3D allowed us to define the shape properties of the fabric ellipsoid by using the Jelinek parameters that are the symmetry T, which varies from À1 (prolate ellipsoid) to 1 (oblate ellipsoid) and the eccentricity P' (see  Borradaile and Werner, 1994  for the formulas).
Two-dimensional image analysis Polished sections were prepared from deformed samples parallel to the [XZ] plane (with X the direction of shear, and Z the normal to the [XY] plane of shear), close to the outer surface of the sample. In this plane, deformation occurs approximately by simple shear with a finite strain called hereafter g sec , which should not to be confused with g, the maximum strain reached at the maximum radius of the cylindrical sample. Sectional shape fabric measurements were performed on Scanning Electron Microscopy (SEM) images obtained with a JEOL JSM-6400 (ISTO, Orl eans). The twodimensional shape properties of both alumina and bubble phases were determined by applying the inertia tensor method (program SPO2003. EXE;  Launeau, 2004; Launeau and Cruden, 1998). The shape fabric of both alumina grain and bubble populations is obtained by averaging the single object inertia tensors.
Acknowledgements L. A. acknowledges support by the  French Ministry of Education and Research  ( ACI JC3013 , "Fabriques magmatiques"). This study is a contribution of the  ERC   RHEOLITH  project ( ERC Advanced Grant  no  290864 ). M. B. acknowledges support by the  Swiss National fond  (Project No. 2000-0666. 16 ) in Zurich and by the  European Social Fund in Toulouse.
Objective: The diversity and volume of health data have been rapidly increasing in recent years. While such big data hold significant promise for accelerating discovery, data use entails many challenges including the need for adequate computational infrastructure and secure processes for data sharing and access. In Europe, two nationwide projects have been launched recently to support these objectives. This paper compares the French Health Data Hub initiative (HDH) to the German Medical Informatics Initiatives (MII). Method: We analysed the projects according to the following criteria: (i) Global approach and ambitions, (ii) Use cases, (iii) Governance and organization, (iv) Technical aspects and interoperability, and (v) Data privacy access/data governance. Results: The French and German projects share the same objectives but are different in terms of methodologies.
The HDH project is based on a top-down approach and focuses on a shared computational infrastructure, providing tools and services to speed projects between data producers and data users. The MII project is based on a bottom-up approach and relies on four consortia including academic hospitals, universities, and private partners. Conclusion: Both projects could benefit from each other. A Franco-German cooperation, extended to other countries of the European Union with similar initiatives, should allow sharing and strengthening efforts in a strategic area where competition from other countries has increased.
The second phase (2018-2021) is focused on the development and networking of these four consortia (DIFUTURE, HiGHmed, MIRACUM, and SMITH) with funding totaling 120 million euros. Germany will give 30 million euros to enable the integration of the other sites into these four consortia. MII consolidation and further development will be carried out from 2022 to 2025 (third phase). The MII project focuses on German university hospitals and medical centers which treat a total of 1. 8 million patients annually (10% of all inpatient cases in the country). The French Health Data Hub ("Plateforme nationale des données de santé", HDH) is born from the political willingness to promote artificial intelligence (AI) in health, identified by the French government [16] as a priority domain where AI technologies could provide a strategic advantage for the nation and for Europe  [22].
In 2018, President Macron announced two major decisions  [23] : the construction of four highlevel AI research institutes (Institut 3IA), and the creation of the HDH associated with the extension of the National Health Data System ("Système National des Données de Santé", SNDS). SNDS was created in 2015 to merge the national anonymous claim database (SNIIR-AM) with relevant data sources, such as the national diagnosis-related group database (PMSI) and the national death registry (CepiDC)  [24]. SNDS covers 98. 8% of the French population, more than 66 million persons, making it possibly the world's largest continuous homogeneous claim database  [25]. SNDS will be enriched by adding new data, especially clinical data from hospital and primary care electronic health records (EHRs), and will provide tools and services to the different stakeholders (i.
Use Cases The German initiative gave much freedom to the consortia for the choice of use cases. The four selected consortia have proposed a wide range of domains and application areas that are summarized in Table  1. The aim of these use cases is mainly to allow the consortia to focus on the critical requirements to achieve health data sharing, particularly implementing an electronic consent declaration, implementing a trusted third party for identity The French Health Data Hub and the German Medical Informatics Initiatives: Two National Projects to Promote Data Sharing in Healthcare management, define the rules of data use and access, data protection, semantic interoperability, data sharing modalities, audit criteria, patient involvement and empowerment, medical informatics research and education development, roll-out, and further integration.
Regarding the French HDH, during the meetings with HIT players, more than a hundred use cases have been identified in four main domains: medical research, patients' information, decision support systems for health professionals, and public health informatics for health system monitoring. At the beginning of 2019, two groups of pilot projects were selected. The first one included only public or academics partners, while the second one was open also to start-ups and private-sector partners through a call of interest. Finally, 20 projects were selected that covered a wide range of domains. Table  2  provides a global view of these projects. Most of these projects link data coming from the current SNDS with specific registry data. Some projects (i. e. , HugoShare and EpifracTAL) aim at crossing real-life data coming from clinical data warehouses with SNDS data.
They are a new structure within academic medical centers, and could be described as local and central hubs to offer expertise in health data science and medical informatics, and provide secure and reliable access to high quality data integrated from healthcare and research sources. Each DIC is dedicated to a specific area, for example, OMICS in the HiGHmed consortium. In France, the HDH is currently putting in place a governance structure. The Committee had noted that access to each health database was governed by heterogeneous, sometimes complex rules. Indeed, requests for access and matching different sources could take several years to complete. The different actors consulted during the Committee work period had expressed the need for a single access point to ensure simplified, effective, and rapid access to data.
The Committee recognized that the harmonization of data access from the existing sources would be highly desirable, but it would require a huge effort to avoid a proprietary reflex or discretionary access to data by the producer. Therefore, they suggested that data producers and users should share data for innovative projects on a secure third-party platform, and that each step of the project should be given adequate support. A second lever was to extend the SNDS by law to all health data encompassed by the welfare system. The goal is to give the same legal status to all health data, independently from their origin. The French HDH plans to leverage and encourage the existing local initiatives by i) creating local or territorial hubs that provide local medical expertise and healthcare services like at the national level; and by ii) encouraging and funding the local efforts (e.
Technical Aspects and Interoperability Reusing healthcare data for research and sharing data across institutional boundaries immediately raise questions of interoperability and data quality. Regarding the infrastructure, despite some similarities, the strategy seems different from one consortium to the other. HiGHmed should rely on the openEHR platform and components. MIRACUM pursues the strategy to package and extend some existing OpenSource components and software to build its infrastructure, called Miracolix (Medical Informatics ReusAble eCo-system of Open source Linkable and Interoperable)  [30]. DIFUTURE will be based on clinical data warehouses (I2B2  [31]  for clinical data and Transmart  [32]  for OMICS data) that are also used by MIRACUM. Conversely, SMITH and HiGHmed designed their platform using a distributed approach based on API and services to connect data sources of legacy information systems to the platform.
In this same logic, SMITH offers two high-security level services, one for data sharing, and another one (the SMITH Market Place) dedicated to contracting  [33]  and granting access to data for internal and external data users. To ensure care coordination and interoperability, the choice made by the German initiative is to define a minimum core dataset to be transferred from the point of care to the DICs for each individual patient case, all in a standardized pseudonymized form for future use in research. The strategy is then to progressively broaden the core dataset according to mutually agreed upon criteria: relevance to research and patient care, relevance to the use cases, availability and accessibility in the MII sites, extent of data structuring, and availability of reference terminologies.
The first version of this national dataset is limited to diagnosis and procedures (using the ICD-10 and the German OPS coding systems), but should be extended to laboratory data (by standardization of a subset of laboratory tests coded with LOINC  [26] ) and drug prescriptions (coded with a national medication terminology). However, comparison of the four consortia suggests that the interoperability strategy and the local implementation are different or at least not fully settled yet. For instance, HiGHmed proposes to extensively use openEHR  [27]  as the interoperability framework, while SMITH and DIFUTURE are building their strategy mainly on IHE integration profiles and HL7/FHIR  [28]. MIRACUM focuses on OMOP as the starting point framework, but will use HL7/FHIR and IHE for clinical data sources.
Only the articles on DIFUTURE and SMITH cite DICOM for imaging. HiGHmed and SMITH rely on the CTS-2 terminology services  [29]. Concerning the narrative clinical texts, three consortia use NLP methods for information extraction. In France, the HDH strategy concerning the infrastructrure is to leverage existing initiatives and to rely on the emerging local platforms to build the national infrastructure. Clinical data warehouses are thriving in some academic hospitals. For instance, the University Paris hospitals (AP-HP) are developing the AP-HP Clinical Data Repository, a big data platform that integrates all the clinical data sources of the hospital group and are initializing Data Quality programs to assess the characteristics and the quality of the data  [44, 45]. In the same way, the French Cancer Center Federation has been extending a data warehouse network in France  [46].
Among these services, a central catalog will describe all the available data collections and will provide access to data samples for training or testing. Other crit- The French Health Data Hub and the German Medical Informatics Initiatives: Two National Projects to Promote Data Sharing in Healthcare ical IT components and services will be developed, such as a national patient portal to manage patient information and consent. In this persepctive, the French initiative encourages data producers to improve data quality by adopting and implementing interoperability standards. Hence, it has been proven that interoperability is a key factor to reach meaningful use of health data, and to improve data reuse  [34, 38]. Adoption of common data models and semantics based on formal, accessible, and broadly adopted standards participates to the FAIR principles and improves the automation of health data at a large scale  [33].
For instance, HiGHmed has defined a process at the early stage of a request to perform feasibility studies (i. e. , aggregate counts grouped by sites) that does not need patient-specific consent. DIFUTURE has defined the role of a data protection specialist, a DIC member who reviews each request and analyzes the risks of data sharing. MIRACUM has proposed to use an open-source tool for managing consents and data use agreements that was previously developed for consent management of biological samples  [48]. In France, the situation is different because data protection is regulated by a national law. The national agency for data protection (CNIL,  [49] ) is an independent administrative regulatory body that ensures the application of the data privacy law for the collection, storage, and use of personal data.
However, the possibility of large-scale data sharing offered by HDH will lead to the development of new and harmonized procedures of data access and to strengthen SNDS security and data protection. The action plan for this issue encompasses a revision of the law, and the choice of state-of-the-art technologies to address the security aspect of the platform. Currently, health data contained in the SNDS can be accessed without the need of patient consent, upon the agreement of a dedicated national committee (CEREES,  [50] ) and the CNIL authorization. Only public interest studies are allowed. However, the adoption of the European General Data Protection Regulation to enhance patient and citizen information and consent will be imperative. The perspective of crossing multiple data sources at large scale will increase the risk of identification.
Comparing the two projects, we see that the territorial organization of the country influences the project philosophy and structuration. [57]. d) Involving the citizen as a key player and establishing a relationship of trust: HBD and AI technologies hold the promise of innovation and progress for the patient's benefit. However, they also are a potential "Pandora's box" in case of misuse. The cornerstone of data sharing and reuse is trust. Therefore, implementing a trustworthy process for handling citizens' and patients' health data is essential. A system for data reuse should prove its trustworthiness by fulfilling the responsibility of dealing with data within the limits of a social contract regulated by policies between citizens and the organizations that manage the system. Designing a governance system that includes the The French Health Data Hub and the German Medical Informatics Initiatives: Two National Projects to Promote Data Sharing in Healthcare subsidiary principle and the active participation of patients and citizens should strengthen the trust toward national projects.
Rigorous validation of amino acid sequence is fundamental in the characterization of original and biosimilar protein biopharmaceuticals. Widely accepted workflows are based on bottom-up mass spectrometry, and they often require multiple techniques and significant manual work. Here, we demonstrate that optimization of a set of tandem mass spectroscopy (MS/MS) collision energies and automated combination of all available information in the measurements can increase the sequence validated by one technique close to the inherent limits. We created a software (called "Serac") that consumes results of the Mascot database search engine and identifies the amino acids validated by bottom-up MS/MS experiments using the most rigorous, industrially acceptable definition of sequence coverage (we term this "confirmed sequence coverage").
16, 26, 27  Furthermore, even the use of multiple settings of the same fragmentation methods, e. g. , collision energy in CID or reaction time in ETD experiments, can have beneficial effects. 15, 18, 23, 24  Moreover, different ionization and separation techniques may lead to valuable results by providing different peptides to be measured from the same sample solution. Matrix-assisted laser desorption ionization in-source decay (MALDI-ISD) was shown to be good in sequencing, especially in combination with ETD. 28, 29 [8] [9] [10] [11] 30  Results from bottom-up experiments are often analyzed using standard proteomics software, matching the spectra against peptides from an in silico digestion of protein sequences in a database. The definition of sequence coverage in typical bottom-up analysis software packages is to consider all amino acids covered/determined for which a peptide was identified.
e. , bottom-up LC-MS/MS measurements of a tryptic digest using CID (or more recently HCD) fragmentation. We developed software capable of automatically computing, and providing, an annotated report of protein CSC from LC-MS/MS runs, analyzed via the frequently used Mascot database search engine. We then performed a series of experiments on a Bruker QTof mass spectrometer on two therapeutic mAb digests (rituximab and trastuzumab) and analyzed the results to map the collision energy dependence of peptide and protein CSC and find the optimal settings. Finally, and most importantly, we studied how CSC can be increased via combining information from several sources (e. g. , multiple spectra on different charge states, multiple runs collected under different settings) at multiple levels (peptide, amino acid, fragment ion) while keeping an adequate level of transparency for manual validation.
Even though the latter remains key for quality control, our methodology can significantly reduce the necessary efforts by delivering more condensed, more valuable information, in terms of better spectra and direct CSC annotation, and, at the same time, ensure a notably higher overall CSC of ∼85%, compared to 55%-60% in a basic approach. Future extension to other techniques, as well as to the emerging middle-down approaches, is straightforward and has the potential to achieve full CSC without tedious manual data analysis. ■ EXPERIMENTAL SECTION Chemical Reagents. Unless otherwise stated, reagents and consumables were obtained from Sigma-Aldrich (Sigma-Aldrich Kft. , Budapest, Hungary). RapiGest SF was purchased from Waters (Milford, MA); trypsin/Lys-C mix and trypsin digestion enzymes were obtained from Promega (Madison, WI).
coli standards. 32 Finally, during the collision energy fine-tuning process in the present work, the collision energies applied to fragment the peptides were chosen to be the sum of the m/z-dependent pre-optimized collision energy and a collision energy shift, mapping the -20 eV to +20 eV range in 2 eV steps in 21 separate runs. Hence, 21 different collision energy values for each peptide were measured, centered at a peptide specific default value, subject to a lower limit on the applied collision energy of 5 eV. The significance of using an m/z-specific default energy is only to avoid measuring peptides at highly irrelevant energies, and its exact value does not affect our conclusions. Data Analysis. The raw Q-TOF data were first recalibrated using Bruker Compass DataAnalysis software 4. 3 (Bruker Daltonik GmbH, Bremen, Germany) for the internal calibrant.
MS/MS peak list generation was performed using Protein-Scape software 3. 1 (Bruker Daltonik GmbH, Bremen, Germany). MS/MS spectra were searched against the amino acid sequence of the mAb under study (obtained from DrugBank database, as of December 2018), using the Mascot search engine version 2. 5 (Matrix Science, London, U. K. ). During the Mascot search, the following search parameters were set: trypsin enzyme, 7 ppm precursor mass tolerance, 0. 05 Da fragment mass tolerance, max. two missed cleavages, carbamidomethylation of cysteines as fixed modification, deamidation (NQ), and oxidation (M) as variable modifications. The output files of the Mascot search were used as input files for further analysis of confirmed sequence coverage. During the energy dependence analysis and CSC determination, we only considered peptide identifications if their Mascot score exceeded 13, corresponding to p < 0.
05. In order to extract the relevant MS/MS information, to compute CSC, and to combine data from one or more measurements as needed, we created a computer program to post-process Mascot search results files into CSC reports. This program, which is called Serac (for Search Engine Results Aggregation and Combination) and based on the Mascot Parser API, is capable of the following: (1) Annotating the peptide fragment ions within individual spectra in a Mascot results file. We only took into account the most intense fragment peaks that were selected by Mascot's iterative matching/scoring procedure to ensure that CSC is only based on real peaks, not noise. 33, 34 2) Determining the CSC from single spectra by following the definition described at the beginning of this paper (rules are described in detail in the SI).