dataesr/scientific-paragraphs-categorization huggingface dataset:
First, the identification of relevant projects was conducted using high-level public data describing their scientific activities. This initial identification was followed by a detailed analysis of individual project datasets in collaboration with the data owners. The starting point for the identification of projects with data resources suitable for FAIRification was text mining from websites and catalogues provided by the IMI (https://www. imi. europa. eu/projects-results/project-factsheets). Basic parameters were extracted, including project start and end dates, participating academic and European Federation of Pharmaceutical Industries and Associations (EFPIA) partners, the contact information of individuals, and a summary of the scientific focus and objectives of each consortium. Pilot-stage evaluations showed that having access to the project-associated personnel with knowledge of the data and its scientific context was a key aspect in deciding FAIRification success.
Therefore, projects were grouped into 'ongoing', 'close-to-completion', and 'completed'. Active projects with engaged counterparts were later identified. FAIRplus sought out projects working on 'antimicrobial resistance', 'neurodegenerative disease', 'healthy aging', and 'chronic disease', to align with its own strategic objective to maximise societal impact. Ensuring strategic alignment with organisational objectives is a general requirement of data set prioritisation efforts. We made use of a Python code for this step of the pipeline. This code is available on GitHub at https://github. com/ Fraunhofer-ITMP/IMI-Project-Prioritization/tree/v1. 0. Following initial prioritisation based on strategic alignment with FAIRplus objectives and ability to engage data owners, individual IMI project data resources were analysed to establish the technical, methodological, and ethical features of the data for the establishment of a complete picture of the project, starting from the requirements of the project to the product to be disseminated.
Information collected included potential impacts of project and outputs, such as the innovation level (methodological), the comprehensiveness (size), the data availability and accessibility, as well as possible legal aspects associated with the data access, transfer, and/or manipulation. A Data Transfer Agreement (DTA) was developed for use in projects processing sensitive data to provide a legal framework for the processing and reuse of data as part of a FAIRification effort. Additional factors quantified included project maturity, whether data had been made public via other mechanisms, such as publications, or any Intellectual Property (IP) requirements that necessitated that data access would be restricted. The overall FAIR awareness of the consortia was established, which was important in terms of understanding the long-term uptake of the FAIRification measures by the team.
One crucial factor was to ensure that collection and processing of all data was carried out in accordance with ethical guidelines and the terms of any informed consent requirements. Phase 2: Data set evaluation and selection Using the information from the data set analysis, projects were scored across three criteria: societal, scientific, and methodological/technical with a range of 1-4, where 1 was defined as low impact and 4 as high. Societal value was rated according to the research focus of the project and alignment with the FAIRplus strategic priorities. For scientific value, the uniqueness and novelty of the process, the potential synergies across the community, domain coverage, data quality, and the in-house availability of a project or data champions were assessed. Methodological maturity scoring was based on the access model and DMP alongside technical factors, including availability of machine-readable data, presence and status of metadata, the extent of ontology application, data models, workflow documentation, and so on.
In situations in which sensitive data were not accessible, their substitution with synthetic data was evaluated. Following the scoring and ranking process by the FAIRplus consortium, 15 of more than 120 IMI projects were prioritised for further engagement and collaborations were initiated around data resource FAIRification. The findings from the ongoing FAIRification work on these projects, including the use of tools for capability assessment and recipes for elevating the FAIRness of specific data resources, are outside the scope of this paper. The template of the scorecard used by FAIRplus during this process can be found on Zenodo. 41 The impact of decision-making based on the resources is described in detail in the FAIRplus cookbook recipe at https://w3id. org/faircookbook/FCB055.
Concluding remarks 4] [15]  Nevertheless, on a practical level, when biomedicalfocussed organisations implement FAIR, data owners and managers must determine which resources should be prioritised for FAIRification, as well as the costs versus benefits balance for individuals, institutions, or research communities. Here, we have identified factors that influence the selection and prioritisation of data for FAIR implementation and describe how these can be assessed and used to prioritise projects and data resources, using the experiences of the IMI FAIRplus projects as an example. Given the limited types of exemplar data set that have undergone the FAIRplus FAIRification process, it is envisaged that other types of data set would likely pose a different set of challenges. Legal and ethical issues have been identified as important factors and, therefore, should be assessed before the FAIRification process because they might require compliance with certain requirements that, if not met or inadequately met, might result in an obstacle for FAIRification.
However, addition of even small noise to the source variables damages the specificity of the measures when permutation-tested. Further, continuous-valued PID measures produce infinite values for noiseless data, and thus we only test them using datasets where all variables have at least some noise. For such noisy data, continuous-variable measures result in false positives similarly to the discrete-variable case. As a partial remedy for this problem, we propose a null hypothesis which corrects the bias introduced by noise. Compared to permutation-testing, this approach significantly reduces the false positive rate at the expense of increasing the false negative rate. This approach should be beneficial in exploratory neuroscience research, aiming to preserve robustness of the stronger findings at the expense of losing some of the weaker ones.
5) ν y ∼ Ber(0. 5) ν z ∼ Ber(0. 5) In the Results section we study the performance of the tripartite measures as function of noise fractions and data size. To do so, datasets of desired size are sampled from the observable models. Since there are three noise fractions, one for each of the three observables, we further reduce the number of parameters by designing three different noise strategies, all of which have only one parameter (table  4 ). The noise fractions used in the plots of the main text will refer to this single parameter.
Significance Testing As a standard method, we employed permutation testing to assess significance of the estimated information atoms. The above-described observable models were used to produce datasets of three observable variables X, Y and Z. Data size of N sample = 10000 was used everywhere, except when the p z = ν p x = p z = ν p y = 0 p x = p y = p z = ν Table  4. Three observable models. In the pure sources model (PureSrc), only the target observable Z has non-zero noise, the sources were equal to the underlying ground truth variables. In the noisy source X model (NoisyX) both the target Z and the source X observables have noise (equal noise fractions), whereas the source Y is kept pure. In the Noisy model, all three observables have added noise (equal noise fractions). Thus, each observable model is parameterized by a single noise fraction ν dependence on data size was investigated.
For each dataset, the model information atom was computed. The information atom was then re-computed after permuting the data along the target variable Z. This approach is more robust than permuting all three variables because the measure implementations in practice may be sensitive to source correlations even in cases where theoretically source correlations should have no impact on the result. This procedure was repeated multiple times (N test = 10000), obtaining the distributions of the information atom for original and permuted data. The critical value corresponding to the desired p-value (0. 01) was estimated as the corresponding quantile of the empirical shuffled distribution of the information atom. The critical value was then used to test significance of individual original datapoints, computing the fraction of significant information atoms.
Researchers are invited to run the simulations in the python code provided (for a given measure and data Authors: Aleksejs Fomins size) to find the corresponding conservative critical values that then can be applied to testing experimental data. While we refer to underlying multivariate interactions extensively in this work, we emphasize that our main focus is on the estimation of statistical relations from neuronal data as opposed to the estimation of causal interactions. The set of ground truth models employed in this study is used for illustrative purposes only, as we actually focus on the statistical distributions induced by these ground truth models. Information theory, and thus PID measures, are by design a set of tools for statistical inference and not causal inference. They may be used to narrow down the possible set of causal explanations Reid et al.
Namely, variance of the sum σ 2 xy of independent variables is exactly equal to the sum of variances σ 2 x + σ 2 y , suggesting two purely unique relations, whereas the joint mutual information I(XY : Z) is greater than the sum of two Our results also rely on several simplifying assumptions, some of which are worth to be improved upon in future studies. First, we computed information atoms using data distribution across trials for a fixed time step. A related question is the study of information atoms across time, for example in long recordings of resting-state activity. Compared to the former, across-time analysis is complicated by autocorrelation in data. We refer the reader to related recent work addressing autocorrelation effects in functional connectivity estimation  (Cliff, Novelli, Fulcher, Shine, & Lizier, 2020; Harris, 2020).
Partial First the ground truth model is used to generate multiple samples of the ground truth variables X * , Y * , Z *. Then, the observable model adds noise to the data, producing observables X, Y , Z. Finally, the measure is used to compute information atoms for the given data sample. C) We explored four ground truth models (mRed, mUnq, mXOR, mSum), three observable models (PureSrc, NoisyX, Noisy), four measures (PCorr, VP, BROJA PID, MMI PID), which each report 4 different information atoms (except PCorr, see below). In the observational model, green color denotes pure variables (no unexplained variance), and yellow denotes noisy variables. All models had discrete and continuous versions. -51-.
ACKNOWLEDGMENTS We thank  Joseph Lizier ,  Patricia Wollstadt , and  Leonardo Novelli  for initial support in using the library IDTxl. We are grateful to  Michael Wibral  and  Abdullah Makkeh  for extensive support on theory underlying partial information decomposition, especially in terms of interpretation of results. We thank  Peter Rupprecht ,  Adrian Hoffmann ,  Christopher Lewis , and many other members of the  Helmchen Lab  for suggestions on improving the manuscript. Finally, we thank  William Huber ,  Ruben van Bergen  and  Frank Harrell  for useful suggestions with respect to our questions on the state of the art in statistical analysis. This work was supported by grants to F. H. from the  Swiss National Science Foundation  (No. 310030B 170269 ) and the  European Research Council  ( ERC Advanced Grant , project No.
Hera will characterize the binary system in detail, in particular the physical and compositional properties of Dimorphos, including a survey of its subsurface and internal structure with a low-frequency radar on board one of the two associated Cubesats, in addition to observing the crater produced by DART  (Michel et al. 2022, this issue). Most of the modeling activities in support of the development and data interpretation of Hera are supported by a consortium called NEO-MAPP (Near-Earth Object Modeling and Payload for Protection) funded by the Horizon 2020 program of the European  Commission (2020 Commission ( -2023)). The main objective of NEO-MAPP is to offer significant advances both in terms of the understanding of the response of asteroids to external forces (in particular, a kinetic impact or tidal forces during planetary approaches) and in terms of measurements by a spacecraft (including those necessary to characterize the physical and dynamical properties of the asteroid).
Shock Physics Codes All three codes applied in this study solve similar forms of the governing equations of mass, momentum, and energy conservation. However, they employ different numerical schemes: iSALE-2D uses an Eulerian grid-based approach, while miluphcuda and Bern SPH use the Lagrangian, grid-free smooth particle hydrodynamics (SPH). In SPH, each particle represents the local volume in 3D space. Individual SPH "particles" discretize the simulated materials, but are rather numerical interpolation points, instead of directly representing physical particles. In the following, we introduce each code and give some background information. The iSALE-2D code  (Wünnemann et al. 2006 ) is based on the SALE hydrocode solution algorithm  (Amsden et al. 1980). It is a grid-based ALE (Arbitrary Lagrangian Eulerian) code and uses a finite difference scheme.
The package includes different material models, including an elasto-plastic constitutive model, fragmentation models, various equations of state, and multiple materials  (Melosh et al. 1992; Ivanov et al. 1997). More recent updates include a modified damage model with an intact and a damaged yield surface  (Collins et al. 2004 ) and the ε-α porosity compaction model  (Wünnemann et al. 2006; Collins et al. 2011). Although a 3D version of iSALE exists, we apply the 2D version in this study, which reduces computational costs. The code makes use of a cylindrical symmetry around the vertical axis along the projectile trajectory to simulate 3D behavior. As a consequence, impact angles are limited to 90°, and each cell represents a hollow cylindrical-shaped element (torus with a rectangular cross-sectional area).
To further reduce the computational efforts required for a single computation, we follow the approach described by  Raducan et al. (2019)  and halve the resolution in iSALE simulations several times, as indicated in the validation and benchmark sections (Sections 3 and 4). Such a regridding procedure proved to speed up calculations, while maintaining the accuracy of crater and momentum enhancement calculations (e. g. , 1% deviation between a high-resolution model and a regridded model;  Raducan et al. 2019). The 3D SPH code miluphcuda  (Schäfer et al. 2016 (Schäfer et al. , 2020) )  has been mainly developed for modeling astrophysical impact and collision processes of multi-material, self-gravitating bodies, and utilizes fast and efficient GPU hardware. It includes several rheology models to simulate elasto-plastic flows as well as brittle failure, various equations of state, and different porosity compaction models like the P-α model.
In previous works, miluphcuda has been successfully used for simulations of granular media (e. g. ,  Schäfer et al. 2017 ) and porous materials (e. g. ,  Haghighipour et al. 2018; Grishin et al. 2020 ). Bern's SPH code was originally developed by  Benz & Asphaug (1995 , 1994)  to model the collisional fragmentation of rocky bodies. This code was parallelized  (Nyffeler 2004 ) and further extended by  Jutzi et al. (2013 Jutzi et al. ( , 2008) )  and  Jutzi (2015)  to model porous and granular materials. The most recent version of the code includes a tensile fracture model  (Benz & Asphaug 1994 , 1995) , a porosity model based on the P-α model  (Jutzi et al. 2008 (Jutzi et al. , 2009)) , pressure-dependent strength models  (Jutzi 2015) , and self-gravity. Like miluphcuda, this is a 3D code.
By aligning the crush curves, we make sure that all three codes simulate the same material response, not only in terms of initial porosity, but also in terms of its compaction behavior and the energy going into crushing. Comparing the cumulative ejecta masses over velocity for the three codes (Figure  7 ), we note a difference in the fastest ejecta, which has a mass of less than a few impactor masses. The ejection speed of the fastest ejecta resolved by the gridbased code iSALE is considerably slower than that obtained with the particle-based Bern SPH and (even more so) miluphcuda. The mass of the different fastest ejecta accounts for about one projectile mass or less. This difference is likely related to the principal numerical differences between the codes: Groups of particles can be ejected in SPH at the observed high velocity, while the material interface algorithm in iSALE, which is meant to suppress numerical diffusion, rather avoids the separation of material into single, underresolved cells.
g. , to describe and compare the formation of the DART-induced ejecta curtain, which will be observed by LICIACube. These values are based on cumulative mass distributions versus velocity and are averaged between the codes. While the uncertainty of μ for a cohesion of 100 kPa reaches up to +/-20%, the values for smaller cohesions down to ∼1 kPa agree to within +/-10%. We are thankful for the helpful and constructive reviews from two anonymous reviewers, who helped to improve this manuscript. We gratefully acknowledge the developers of iSALE-2D, including Dirk Elbeshausen, Boris Ivanov, and Jay Melosh. This work used the pySALEPlot software. We also acknowledge the developer of the VIMoD software, Dirk Elbeshausen. iSALE website: http://www. isalecode. de/. The SPH code miluphcuda is freely available at https://github.
S. D. R. , R. L. , M. J. , P. M. , and Y. Z. appreciate the funding from the European Union's Horizon 2020 research and innovation program, grant agreement No. 870377 (project NEO-MAPP). C. B. and C. M. S. appreciate support by the German Research Foundation (DFG) project 398488521. P. M. also acknowledges support from CNES and the CNRS through the MITI interdisciplinary programs. TMD and GSC were funded by STFC grant ST/S000615/1. This work was supported by the DART mission, NASA Contract No. 80MSFC20D0004. C. B. and C. M. S. acknowledge support by the High Performance and Cloud Computing Group at the Zentrum für Datenverarbeitung of the University of Tübingen, the state of Baden-Württemberg through bwHPC and the German Research Foundation (DFG) through grant No INST 37/935-1 FUGG.
Mutation detection by next-generation sequencing is routinely used for cancer diagnosis. Selecting an optimal set of genes for a given cancer is not trivial as it has to optimize informativity (ie, the number of patients with at least one mutation in the panel), while minimizing panel length to reduce sequencing costs and increase sensitivity. We propose herein Panel Informativity Optimizer (PIO), an open-source software developed as an R package with a user-friendly graphical interface to help optimize cancer next-generation sequencing panel informativity. Using patient-level mutational data from either private data sets or preloaded data set of 91 independent cohorts from 31 different cancer types, PIO selects an optimal set of genomic intervals to maximize informativity and panel size in a given cancer type.
Different options are offered, such as the definition of genomic intervals at the gene or exon level and the use of optimization strategy at the patient or patient per kilobase level. PIO can also propose an optimal set of genomic intervals to increase informativity of custom panels. A panel tester function is also available for panel benchmarking. Using public databases, as well as data from real-life settings, we demonstrate that PIO allows panel size reduction of up to 1000 kb, and accurately predicts the performance of custom or commercial panels. (J Mol Diagn 2022, 24: 697e709; https:// doi. org/10. 1016/j. jmoldx. 2022. 03. 005) In the era of personalized medicine, molecular analysis of cancer genomes by next-generation sequencing (NGS) has become an essential step to establish a precise diagnosis, decipher patient prognosis, and identify therapeutic targets.
On the other hand, a large panel is required to ensure detection of mutations with clinical impact, and optimal informativity, which can be defined as the proportion of patients with at least a given number of mutations in the panel analyzed. Although the list of genes with clinical impact in a given cancer is well described and consensual, 2 there is no generic tool to optimize the informativity of the panel. We present herein the Panel Informativity Optimizer (PIO), an open-source, publicly available software to optimize panel informativity. On the basis of individual mutational profiles available in public cancer genomics databases or in private databases, PIO enables the design of an optimal panel (defined at the level of whole genes or exons/introns) to maximize informativity. In addition, PIO can be used to assess the informativity of a given panel in a cancer of interest.
Preloaded Mutation Data Set Selection and Genomic Annotations Preloaded data sets available in PIO have been downloaded from cBioPortal (http://www. cbioportal. org, last accessed November 22, 2021). 5, 6 Only diseases with at least one available The Cancer Genome Atlas data set from the 2018 The Cancer Genome Atlas pan-cancer study were selected. 7 he complete list of data sets used and their corresponding studies is provided in the Supplemental Table  S1. 7e81  Additional genomics information was retrieved from the GRCH37 human genome reference downloaded from the National Center for Biotechnology Information Reference Sequence (RefSeq) Database [RefSeq identifier (ID) GCF_000001405. 13, https://www. ncbi. nlm. nih. gov/assembly/ GCF_000001405. 13, last accessed November 22, 2021]. Exon coordinates were extracted using the RefSeq Select annotation.
Length Analysis Module The length analysis module allows the comparison of four different approaches for panel length optimization. In the classic approaches, mutated genes or exons are ranked according to their informativity metric (either UP or UPKB) computed on the total cohort. Panel informativity is then calculated by taking the number of new unique patients diagnosed for each new gene/exon using the panel tester jmdjournal. org -The Journal of Molecular Diagnostics function. The two other approaches use the PIO algorithm with selected parameters.
Coding and Online Tool Implementation The algorithm was written in R language with the R software version 4. 0. 3 (https://www. R-project. org, last accessed November 22, 2021). A while loop was used to perform the recurrent iterations across the input data set. The graphical user interface allowing to use the algorithm on different preloaded or custom data sets in a user-friendly environment requiring no coding skill was implemented using the R Shiny package version 1. 7. 1 (https://CRAN. R-project. org/ packageZshiny, last accessed November 22, 2021). The resulting application, PIO, was then released as an opensource software under the MIT license and uploaded on github (https://github. com/VincentAlcazer/PIO, last accessed November 22, 2021) and on the shinyapps. io servers (https://vincentalcazer.