dataesr/scientific-paragraphs-categorization huggingface dataset:
Patients and registries From the patient perspective, improved phenotype data sharing offers individuals and families affected by a disease the opportunity to share readily observable data through patient-centered phenotyping approaches. The recent translation of the clinically focused HPO into layperson terms  52  and ongoing Indigenous language translations of the HPO under the Lyfe Languages initiative will facilitate patient-driven phenotyping and subsequent matching and discovery of additional patients. The ability to use Phenopackets is also incorporated in the new architecture of the Western Australian (WA) Register of Developmental Anomalies, which includes two registers, the WA Birth Defects Register and the WA Cerebral Palsy Register, and is connected to multiple health data sets and is part of the data linkage ecosystem in the WA Health Department.
The requirements are intended to enforce that elements have sufficient data to be unambiguous without requiring information that may not always be available. For instance, if the Quantity element is used, it must have the unit and value fields, but the referenceRange field is optional. The requirements for each element are defined in the online documentation. We have implemented a Java library and command-line application that validates Phenopackets using a JSON schema to enforce these constraints. The library, phenopacket-validator (see Web Resources), enables users to specify additional project-or consortium-specific constraints and to enforce validity of ontology terms used (examples are provided in the GitHub repository).
Outlook Our goal is to provide a computable, well-structured representation of an individual's medically relevant data to serve a wide community of patients and clinicians, scientists, and software developers. A schema analogous to the VCF standard for genetic variation has been lacking for clinical data such as phenotypic features, disease diagnoses, and treatments. We have therefore developed the Phenopacket schema within the broad community of GA4GH to fill this need. Just as VCF is not currently able to represent all types of genomic variation, the Phenopacket schema does not cover all potentially relevant types of clinical information. The Phenopacket schema was designed to be easily integrated with other schemas that use the protobuf or JSON frameworks, and we envision future work to integrate the Phenopacket schema with standards to represent other areas including population background ('race' or 'ethnicity'), social determinants of health, and environmental exposures.
Software has become an essential resource for genomic medicine. A common experience of software developers is that a substantial amount of time is spent on reformatting data to correspond with the input format demanded by analysis software, or in the development of bespoke data schemes for new research projects. We hope that the Phenopacket schema will encourage the development of a collection of software for the analysis of genomic data in the context of clinical information that will accelerate innovation and discovery. While the need for standardized computable and comparable clinical and genomic information is the basis for the development and anticipated implementation of Phenopackets, this does not replace a clear, concise, and nuanced narrative case report that describes the patient and the course of the disease over time.
Core Phenopacket resources Phenopacket schema source code: https://github. com/phenopackets/phenopacket-schema Phenopacket schema documentation: https://phenopacket-schema. readthedocs. io/ Phenopacket validator: https://github. com/phenopackets/phenopacket-validator Phenopacket tools: https://github. com/phenopackets/phenopacket-tools.
Contributions PNR contributed clinical data model expertise, clinical subject matter expertise, data analysis, data curation, data integration, data quality assurance, funding acquisition, manuscript drafting, critical revision of the manuscript for important intellectual content, software engineering, statistical analysis. JSB contributed clinical data model expertise, clinical subject matter expertise, critical revision of the manuscript for important intellectual content. HH collaborated on data integration, data quality assurance, funding acquisition, manuscript drafting, critical revision of the manuscript for important intellectual content, software engineering. PB is acknowledged for biological subject matter expertise. GIS collaborated on biological subject matter expertise, clinical subject matter expertise, data curation, data integration, database / information systems admin, funding acquisition, governance, marketing and communications, project evaluation.
SK contributed clinical data model expertise, data integration. CGC contributed clinical data model expertise, funding acquisition, governance, critical revision of the manuscript for important intellectual content, project management. KCL contributed funding acquisition, critical revision of the manuscript for important intellectual content. PMK contributed biological subject matter expertise, clinical data model expertise, clinical subject matter expertise. MB contributed biological subject matter expertise, clinical data model expertise, database / information systems admin, manuscript drafting, project evaluation, software engineering. AHW contributed clinical data model expertise, data integration, manuscript drafting, software engineering. CFB collaborated on critical revision of the manuscript for important intellectual content.
CJM contributed software engineering. AM-J contributed clinical data model expertise, software engineering. MT is acknowledged for. MP is acknowledged for biological subject matter expertise, clinical data model expertise, clinical subject matter expertise, data analysis, data curation, data integration, data quality assurance, data visualization, database / information systems admin, funding acquisition, governance, manuscript drafting, critical revision of the manuscript for important intellectual content, marketing and communications, project evaluation, project management, regulatory oversight / admin, software engineering, statistical analysis. DS contributed data analysis, data quality assurance, funding acquisition, project evaluation. ST contributed clinical data model expertise, clinical subject matter expertise, data integration.
NAV contributed data curation. BPC collaborated on clinical data model expertise, clinical subject matter expertise, critical revision of the manuscript for important intellectual content. MD collaborated on biological subject matter expertise, clinical data model expertise, data integration, data quality assurance, manuscript drafting, critical revision of the manuscript for important intellectual content. HC collaborated on clinical subject matter expertise, data analysis, data curation, data integration, critical revision of the manuscript for important intellectual content. GSB contributed clinical subject matter expertise, funding acquisition, manuscript drafting, marketing and communications. LB collaborated on data analysis, data integration, software engineering. MC contributed biological subject matter expertise, clinical data model expertise, data curation, data integration, data quality assurance, data visualization, database / information systems admin, critical revision of the manuscript for important intellectual content, software engineering.
MAH contributed biological subject matter expertise, clinical data model expertise, data curation, funding acquisition, governance, manuscript drafting, critical revision of the. manuscript for important intellectual content, marketing and communications, project management. NLH collaborated on funding acquisition, governance, critical revision of the manuscript for important intellectual content, project management. NP contributed database / information systems admin, critical revision of the manuscript for important intellectual content. NQ-R contributed clinical data model expertise, manuscript drafting, critical revision of the manuscript for important intellectual content. MCM-T contributed governance, project evaluation, project management. CW contributed data integration. AK contributed clinical data model expertise, data analysis, governance, critical revision of the manuscript for important intellectual content.
OJB is acknowledged for critical revision of the manuscript for important intellectual content, marketing and communications. EMS contributed clinical data model expertise, clinical subject matter expertise, data curation, data integration, data quality assurance, critical revision of the manuscript for important intellectual content. MA collaborated on biological subject matter expertise, critical revision of the manuscript for important intellectual content. JS contributed data curation, critical revision of the manuscript for important intellectual content. MHA is acknowledged for clinical data model expertise, clinical subject matter expertise, data analysis, data integration, data quality assurance, database / information systems admin, governance, manuscript drafting, critical revision of the manuscript for important intellectual content, statistical analysis.
DFT has thus over the years proven to be an invaluable tool that can be used to understand many features and properties of this and many other alloys. Recent work combined the predictive power of DFT with machine learning, using a kinetic Monte Carlo model driven by a neural network that was trained directly on DFT calculated migration barriers in the Fe-Cr system. This work showed how well DFT captures the physics of phase separation in supersaturated alloys through the near perfect agreement with thermal ageing experiments  [58] [59]. It is thus well-established that systematic DFT studies of defects are of crucial importance. In this project, a database of 2000 vacancy and 5600 selfinterstitial atom (SIA) migration barriers has been generated for Fe-Cr using DFT calculations, with either a vacancy or a SIA defect and with Cr concentrations ranging from 0 to 40 at.
Without parallelization, the systematic study of diffusion in concentrated alloys and the simulation of precipitation kinetics would be very difficult. The parallel code reproduces well the results that were previously obtained in pure iron, with good scalability. Subsequently, the AKMC method will be used to calculate diffusion coefficients of Fe, Cr and C, accounting for the effects of the magnetic transition and of the alloy composition. For the diffusion of Fe and Cr (which occurs by the vacancy mechanism) the evolution of the Gibbs free energy of formation and migration is determined independently. The method has been recently successfully applied to the selfdiffusion and impurity diffusion of Cu in iron  [67]. These AKMC models will be eventually validated on experiments of determination of interdiffusion coefficients at 500 • C in multilayer alloys that are being performed combining nanometer-scale transmission electron microscopy (TEM) techniques and atom-probe tomography (APT).
For this purpose, the three-dimensional vacancy and self-interstitial distributions corresponding to a given spectrum up to very high recoil energy were obtained, using a database of collision cascades from simulations performed with a hybrid BCA-MD (molecular dynamics) approach  [97]. By comparing the metrics obtained for different neutron facilities and those obtained for protons, it emerged that proton damage does not resemble neutron damage. This is due to the fact that the maximum PKA energy expected under proton irradiation is about 10 keV, whereas PKAs of 1 MeV are expected under neutron irradiation. On the other hand, the BCA calculations showed that self-ion (Fe) irradiation at 5 and 8 MeV produces very similar results to those of neutrons and is therefore more suitable to emulate the neutron damage.
g. , vacancy-solute clusters. All these models need as prerequisite the knowledge of a large number of parameters that describe the stability and the mobility of all the defects introduced in the simulation, as well as the reactions between them and with the preexisting microstructure. In order to define the parameter set, all the relevant information available in the literature and usable as input for the simulations in Fe and Fe-Cr alloys, including carbon, has been collected  [98, 99]. While sharing principle and parameters, each of the codes has different, and largely complementary, inherent features. The three codes are illustrated in Fig. 7 : (a) MEGA-OKMC (Microstructure Evolution GPU-Accelerated OKMC), developed by CIEMAT; (b) MATEO, developed by SCK CEN; and (c) MMonCa, a code developed initially at IMDEA Materials  [78]  and now being extended by the University of Alicante.
The reason for using three codes is that the complexity of the problems to be addressed advises that each issue is first dealt with separately, being it currently not feasible to produce a single, all-including, tool. Nevertheless, the information obtained from calculations with one code can be transferred to the other OKMC codes, in an effective way, having demonstrated that all codes provide the same result when fed with equivalent parameters. An example taken from  [100]  is provided in Fig. 7d. MEGA-OKMC is based on graphic processing unit (GPU) programming to gain efficiency. It uses a parallelisation algorithm in which the most appropriate time step is found according to the defects that are present in the simulation box, thereby advancing time by steps that are orders of magnitude larger than in standard models  [95].
This strongly reduces the number of computational steps that are necessary to reach a given physical time. In particular, it is not limited in space and thus can follow the evolution of defects in large simulation boxes. This is very useful to simulate the evolution of defects in materials irradiated with energetic ions that reach large depths, when compared to the typical length scale of OKMC models. As an example, Fig. 8a  shows the implantation profile corresponding to 5 MeV Fe + ions in Fe simulated with the MARLOWE code  [101] : as can be seen in Fig. 8a , ions penetrate up to a depth of about 2 μm, which is about 10 times the typical length of volumes simulated with customary OKMC models. Preliminary simulations performed with the MEGA-OKMC code show that simulations with defect accumulation in boxes of 2.
28 and 3. 43 μm length are possible, as shown in Fig. 8b  and 8c. A simulation box of 2. 28 μm represents a good compromise between size and computational efficiency. In addition, simulations have been performed in an Fe matrix with different carbon concentrations, exploring also the effect of pulsed vs. continuous irradiation, as well as analyzing the damage profile versus depth and dose, thereby demonstrating that the code can be used to study the consequences of these ion irradiation features on the microstructure. The OKMC code MATEO, has the capability of modeling the nucleation and evolution of solute rich clusters, by including explicitly the description of how minor solute atoms are transported by point-defects. Solute-rich clusters are a major and important microstructural feature that is observed in irradiated Fe-Cr-based alloys that contain small concentrations of other solutes  [38, 39]  (see also references in section 3.
Thus, the simulation results are discussed jointly with the experimental results in the section 3. 1. 3. The segregation and precipitation of Cr when present in high concentrations, in the presence of defects produced by the irradiation, are addressed with a different OKMC model, named cellular (Cell) OKMC. This model is implemented in the third OKMC code used in the project, MMonCa. This code also includes long range elastic interactions between loops following the description by Dudarev and co-workers  [104] [105] [106]. In the Cell-OKMC model the simulation volume is partitioned in cells, each of which has an associated Cr concentration. Defect migration is biased according to the local Cr concentration in a given cell and in the neighbouring ones, following the thermodynamics of the system.
Moreover, in support of the experiment, a C-free Si sample was irradiated using the same setup and characterized on the basis of a combination of Rutherford backscattering spectroscopy (RBS) and nuclear reaction analysis (NRA). NRA measurements were conducted with 1. 25 MeV D + ions. RBS was based on the nuclear reaction 12C(d,p0) 13C. The carbon profile was approximated as a three-layer profile and derived by way of fitting the measured NRA and RBS spectra simultaneously, using the NDF V9. 6 software. This particular experiment indicated a C concentration of about 10 at% at depths smaller than 14 nm, 0. 03 at% at depths between 14 nm and 1. 2 µm, and no carbon at larger depths. In conclusion, a near-surface region of 20 nm extension should be avoided or at least taken with care in post-irradiation examinations in M4F.
• Optimisation of surface preparation of Fe (G379 in Table  1 ), Fe-9Cr (G385 in Table  1 ) and Eurofer97 for nanoindentation testing. It is important to note that the surface preparation of ion-irradiated samples has to be performed prior to ion irradiation and cannot be modified after ion irradiation. • Execution of nanoindentation testing on pure Fe (G379 in Table  1 ), Fe-9Cr (G385 in Table  1 ) and Eurofer97, irradiated with 5 MeV Fe 2+ ions at 300 • C and 450 • C, up to 0. 1 and 1 dpa. • Development of a methodology to derive information on radiation effects from nanoindentation curves, using mechanically deformed Fe as a proxy and supported by optimized nanoindentation testing protocols and TEM analyses of the as-irradiated and as-indented materials. • Collection of datasets in an engineering materials database and an interoperable nanoindentation test data format developed within the framework of a CEN Workshop (see Appendix).
The focus is now on optimizing the correction of hardness data using elastic modulus  [130, 131]  and the procedures to fit data sets to semi-empirical models, so as to obtain the relationship between the derived fitting parameters and the involved length scales due to the test size (a combination of indent size and indenter geometry), the microstructure, and the damage density. Nanoindentation tests have been performed in four different ferritic/ martensitic materials: the Eurofer97 steel, the Fe-9Cr model alloy with ferritic microstructure (G385 in Table  1 ), the Fe-9Cr model alloy with more complex martensitic-ferritic-bainitic microstructure (L252 in Table  1 ) and pure Fe (G379 in Table  1 ). Berkovich and spherical nanoindentations have been carried out on the non-irradiated materials and in the materials irradiated with the following conditions: -Fe 2+ self-ions at 5 MeV and the following temperatures and doses: o RT, 1 dpa o 300 • C, 0.
[152]  seems promising in the simulation of channelling beyond nucleation. The goal to achieve better precision should be to simulate this at single crystal level, by using detailed discrete dislocation dynamics (DDD) approaches, which can treat the interaction of dislocation segments with irradiation defects, for instance using a nodal code such as NUMODIS. [153]. However, simulations of this type are very timeconsuming and require handling numerous parameters, among which a detailed description of the interaction of the dislocation segments with every possible radiation defect, while describing correctly the mobility of the dislocation segments, thus it is crucial to both pursue methods to accelerate the computation and identify generalized methods to describe dislocation mobility including the effect of the interaction with defects.
a) Molecular dynamics studies The density functional tight binding (DFTB) method, an approximate density functional theory (DFT) approach, has been investigated as a way to model dislocations in iron and its alloys. The freely available dftb + code (http://www. dftbplus. org) was used. A new parametrization of DFTB for bulk bcc iron was developed, which provided much better elastic constants and lattice parameter than the previous one, as is shown in Table  5 , as well as providing information about magnetization and electronic band structure. The new parametrization was then used to model the core of a dislocation in pure iron and in presence of carbon interstitials, as shown in Fig. 19. However, even if DFTB is much less computationally expensive than DFT, this method remains significantly expensive.
These types of effects need to be taken into account in the parameterization of detailed dislocation dynamics simulations that, such as in the case of the code NUMODIS, can reproduce in detail the processes of absorption of loops by gliding dislocations  [153]. However, since DDD simulations do not contain any explicit chemical information, this can mainly be done implicitly, through some sort of calibration that still needs to be applied. MD simulations are also applied to study the interaction of dislocations with three different 〈1 1 0〉 tilt grain boundaries (GB) and a 〈1 0 0〉 tilt-GB in α-Fe  [158]. The GBs that have been considered are: (1) Σ3(1 1 2) < 1-10 > and vicinal to GB112, (2) Σ11(3 3 2) < 1-10 > and (3) Σ5(3 1 0) 〈1 0 0〉. Two calculation methods have been used: MD and a hybrid continuum-atomistic model  [159] , the latter was modified to include all dislocation types.
While performing such a calculation appears straightforward, the relevant formulae are not available in literature and need to be derived. Such a derivation has now been performed within the project. In parallel, the DD simulation code NUMODIS  [153, 167]  was coupled to the elastic solver dedicated to boundary value problems resolution AMITEX_FFTP  [168] , following the example of a similar coupling performed with the code microMegas (Fig. 23 ). Preliminary benchmarks have been performed to assess the accuracy of this elastic solver against a full DD simulation, but the work is still in course. As an example of benchmark, Fig. 23  shows how the Lomer junction formation is described in classical DD using microMegas and when using the FFT solver. DD simulations accounting for dislocation/defect interactions and cross-slip  [23]  have been also used to predict plastic strain spreading in post-irradiated ferritic grain aggregates, using a dedicated micro-model  [169].
Further experiments are planned, in order to constitute a statistically representative database of interactions. Concerning plastic flow localization, the shear band thickness t B , the spacing d and the surface marking displacement h are closely interrelated quantities, according to the above sketched strain spreading dislocation dynamics model. The theoretical thickness t B and spacing d distributions results were calculated with the model described above for two different ferritic materials, holding distinct irradiation defect populations, namely: (i) a large number density of small defect clusters; (ii) a smaller number density of larger defect clusters. The model predictions were directly compared to the surface slip marking arrangements and distributions observed in corresponding postirradiation TEM experiments, after tensile deformation, on the G385-Fe9Cr alloy, which was ion-irradiated with 5 MeV Fe + to 0.