dataesr/scientific-paragraphs-categorization huggingface dataset:
The categorisation system can be used to derive FDO attributes and values for such FDOs. FDOs for the sensitive data itself, when levels of sensitivity and specific access protocols need to be specified is an interesting possible extension, and the categorisation system could support as a backbone information for access governance and technical choices. FDOs are to be "machine actionable", so desirable mappings between different categorisation systems will be operationalisable. New European projects such as FAIRCORE4EOSC (https:// fairc ore4e osc. eu/), FAIR-IMPACT (https:// fair-impact. eu/) and other projects working on pragmatic semantic improvements for FAIR appliance will provide possibilities for registering metadata schemas and mappings that should reuse interdisciplinary approaches in the heterogeneous field of life sciences.
wmo. int/ en/ progr ammes/ global-clima te-obser ving-system/ essen tialclima te-varia bles) and essential biodiversity variables for mapping and monitoring species populations  40. There are biases and gaps in the existing system that need to be tackled in the future. The initial content of the toolbox demonstrator, consisting of 110 resources related to sensitive data, has been primarily selected by four RIs with a focus on clinical and biomedical research (BBMRI, EATRIS, ECRIN, Euro-Bioimaging). Other areas and sensitive data types, such as environmental, classified, and proprietary data are under-represented, as are some disciplines, such as zoology, ecology, plant and mycological sciences, and microbiology. This indicates a need for a broader coverage of resources linked to sensitive data in the future work.
In the development of the categorisation system, existing ontologies, classifications, and terminologies were taken into consideration (Table  2 ). However, many more have relationships to the categorisation system. An example is the Subject Resource Application Ontology (SRAO), an application ontology describing subject areas/ academic disciplines used within FAIRsharing records by curators and the user community  42. A first crosswalk has demonstrated considerable agreement between the toolbox category "research field" and subsections of SRAO  42  and EDAM  15. The toolbox has been registered as a resource (database) at FAIRsharing, a curated, informative, and educational resource on data and metadata standards, inter-related to databases and data policies (https:// fairs haring. org/ 3577).
It is planned to create a collection group of resources (standards, databases, policies) in FAIRsharing linked to the toolbox and the underlying categorisation system. This will also cover relationships to ontologies and classifications. There is a need to explore the applicability of the toolbox to specific domains. One example could be the European Joint Programme on Rare Diseases (EJP RD), where resources are made progressively FAIR at the record level to support innovative basic, translational and clinical research (https:// www. ejpra redis eases. org/ coord inated-access-data-servi ces/ fairi ficat ion-suppo rt/). The goal is to identify, refine and expose core standards for dataset interoperability, asset (data, sample, subject) discovery, and responsible data sharing, concentrating on data level rather than resource level information.
Knowledge exchange between EJP RD and the toolbox could be of benefit in exploring the complementary of both approaches in adequately characterising resources linked to sensitive data and thus improving data discoverability. The first pilot study demonstrated major variation in tagging of resources if independent taggers are assessing the same resource (inter-observer variation). The example of BBMRI has shown that this variation can be considerably reduced if adequate training is performed; which in return is resource intense. Thus, to arrive at a valid and reliable tagging process, there is a necessity for adequate training and support to reduce inter-observer variation. Specific training sets and training programs as well as intercalibration tools need to be developed and implemented and approved by the community.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  " EOSC-Life-Providing an open collaborative space for digital biology in Europe"  under Grant agreement No  824087. The authors wish to thank  Martin Houde  (ERINHA),  Sergio Contrino  (ECRIN),  Sara Raza-Khan  (ECRIN),  Bénédicte Madon  ( La Rochelle Université, France ),  Miriam Beusink  (Health-RI),  Lisa Scheefhals  ( Lygature ) for performing the testing of the toolbox. The graphical abstract was created with Flaticon. com.
Data availability The detailed results of pilot study 1 are available at Data Intelligence's data repository at the Science Data Bank, http:// www. doi. org/ 10. 11922/ scien cedb. 01529, under an Attribution 4. 0 International (CC BY 4. 0). Detailed results of pilot study 2 and 3 are included in the Supplementary Information (S3 Report on pilot study 2, S4 Report on pilot study 3).
The animations and the subtomogram group averages reveal accurate trajectories of macromolecular motion based on a novel mathematical model that makes use of OF properties. This paper describes TomoFlow with tests on simulated datasets generated using different techniques, namely Normal Mode Analysis and Molecular Dynamics Simulation. It also shows an application of TomoFlow on a dataset of nucleosomes in situ, which provided promising results coherent with previous findings using the same dataset but without imposing any prior knowledge on the analysis of the conformational variability. The method is discussed with its potential uses and limitations.
Subtomogram classification methods can be divided into two families. The first family performs classification during StA, commonly via maximum-likelihood estimation and multi-reference alignment  [21, 22]. The second family performs classification on an aligned dataset (posterior to StA)  [23, 24] , typically based on the data covariance matrix of pairwise constrained cross-correlations between the subtomograms. There are significant challenges that are witnessed when classifying a dataset exhibiting continuous variability, including but not limited to i) the higher the number of classes requested, the lower the number of particles in each class, ii) redundant or empty classes are commonly observed, iii) stability of classes is not guaranteed, and iv) different algorithms may yield different results.
Harastani et al. doi: https://doi. org/10. 1016/j. jmb. 2021. 167381 This is a provisional file, not the final typeset article 5 In this work, we propose TomoFlow, a method for an exhaustive analysis of continuous conformational variability in a given cryo-ET dataset, which uses no prior knowledge of possible motions of the macromolecules and analyses individual subtomograms based on 3D dense OF. Optical flow (OF) is a computer vision algorithm that is used in a wide range of applications. The concept of OF was proposed in the 1940s, and throughout the years, it has undergone massive improvements in the accuracy and computational efficiency of the algorithm until our days  [30]. Dense OF takes two images of a similar scene as input and finds a vector per pixel that relates the pixels of one image with the pixels of the other image.
The This is a provisional file, not the final typeset article 9 two features mentioned above increase robustness to noise, brightness differences, and larger displacements, leading to improved accuracy of the OF calculation. An extension of the 2D OF method of Farnebäck  [40]  to deal with volumetric data (Farneback-3D) has been recently implemented (https://pypi. org/project/farneback3d) and this 3D OF calculation method was used in TomoFlow that is presented here. For more information on the iterative multiscale (pyramidal) approach for 3D OF calculation used in TomoFlow, the reader is referred to Supplementary Method Description 1 section of Supplementary Material. The concept of OF can be employed for 3D elastic and rigid-body matching, as explained hereafter. Let  be a reference volume with a high SNR (e.
Tests on simulated datasets with continuous and discrete conformational variability The experiments presented in this section were carefully designed to demonstrate the ability of the method to retrieve continuous and discrete conformational variabilities under simulated microscope conditions. These experiments are not claimed realistic in terms of their biological significance; rather, they are as realistic as possible in terms of the sophistication of simulating noise, MW artifacts, CTF, and radiation damage compared to other works  [22, 23, 51]. For a quantitative assessment of the algorithm for mapping conformations while disentangling it from the subtomographic-approach limitations such as MW and rigid-body (angular and shift) variability, the reader is referred to Supplementary Experiment 1 section of Supplementary Material.
Simulating datasets with discrete and continuous macromolecular conformational variability In order to test the proposed method, we synthesized two conformationally different datasets, each with different noise intensities. The first dataset simulates discrete conformational variability. It was created using Normal Mode Analysis (NMA)  [52]. We will call this dataset the "NMA-dataset". NMA is a method for molecular mechanics simulation with wide usage in flexible fitting and conformational variability analysis  [6, 13, 18, 53]. This dataset comprises 999 subtomograms at three simulated conformations of chain A of the atomic PDB:4AKE structure of adenylate kinase. More precisely, we synthesized 333 subtomograms for each of the three conformations simulated using normal modes 7 and 8 of chain A of the atomic PDB:4AKE structure (in this example, we have chosen to simulate the data using the first two non-rigidbody normal modes of the atomic structure, considering that the first six modes correspond to rigidbody movements and are commonly not used).
We performed NMA using the ContinuousFlex plugin with default parameters  [13]. The three conformations in this dataset correspond to the following normal mode amplitudes (mode 7, mode 8) ∈ {(-100, 0), (100, 0), (0, 100)}. The ground-truth conformational space for NMA-dataset is determined by the amplitudes along normal modes 7 and 8. A visual representation of this space and the conformations it contains is presented in Figure  4 (A). The second dataset simulates continuous conformational variability. It was created using Molecular Dynamics (MD)  [54]. We will call this dataset the "MD-dataset". MD is a simulation approach for exploring conformational dynamics by generating trajectories describing a structure evolving over time. This dataset comprises 1000 subtomograms representing a continuum of conformations generated using an MD trajectory between two conformations of adenylate kinase chain A from the PDB structures PDB:4AKE (most open conformation) and PDB:1AKE (most closed conformation).
We simulated this trajectory using GENESIS  [55]. The ground-truth conformational space of the MD-dataset will be presented here by its first two principal axes (PCA). A visual representation of this space and the conformations it contains is presented in Figure  4  (B). While simulating data, we followed the best practices presented in the literature  [18, 22, 23, 51]  to make the data challenging while keeping the objectives clear and letting the method achieve its goals. For each subtomogram, we convert the PDB structure that represents the desired conformation (i. e. , either one of the three conformations in the NMA-dataset or one of the continuum of conformations in the MD-dataset) to a volume of size 64 3 voxels and voxel size of 2. 2 Å 3  [56]. Then, we lowpass filter the volume to 6 Å resolution in order to simulate radiation damage and other effects such as data misalignments incorporated at the tomogram reconstruction step (skipping lowpass filtering would result in better retrieval of conformational variability but is less realistic).
Rigid-body alignment and refinement with MW correction We applied the proposed procedure in 2. 2 and Figure  3  for rigid-body alignment and refinement with MW correction on the simulated datasets as follows: Step 1. Performed reference-free rigid-body alignment using the StA protocol in  [49]. This StA protocol uses an exhaustive angular search (with FRM), a shifting search within a region of interest, and MW compensation. The shifting search was set to the range of 10 voxels from the center, and the maximum searched normalized frequency to 0. 25. The iterative alignment was performed for 15 iterations (complete stability was achieved in the rigid-body alignment parameters and the resulting average). Step 2. Filled the MW region in Fourier space for each subtomogram by the corresponding region of the aligned subtomogram average.
Step 3. Rigid-body aligned the MW-corrected subtomograms. Step 4. Calculated the 3D OFs between the subtomogram average and each MW-filled and aligned subtomogram using Farneback-3D after multiplying both volumes with a mask. We generated this mask by binarizing the subtomogram average, dilating it by a structural element of size three, keeping its largest connected component, and smoothing its boundaries with a Gaussian filter of standard deviation equal to two. This step results in "matched subtomograms". In all experiments in this article, Farneback-3D was run with a 2-level volume pyramid of scaling factor of 0. 5 (meaning a pyramid with the levels of 64 3 and 32 3 voxels in the case of these two test datasets), a window size of 10 × 10 × 10 voxels for integrating the displacement field over a neighborhood of each voxel, 10 iterations of the algorithm at each pyramid level, and with default values of all other parameters.
Conformational variability analysis The datasets are ready for continuous conformational variability analysis after applying the MW correction and rigid-body refinement algorithm. Subsequently, we calculated the OFs between the refined reference and the MW corrected and rigid-body aligned subtomograms for each dataset. Then, we found the Gram matrix of OFs based on equations (5-6) and applied PCA. The conformational space, represented by the space of the first two principal vectors, for each dataset, at different noise intensities, is shown in Figure  6. A comparison between the ground-truth conformational spaces in Figure  4  and the retrieved conformational spaces in Figure  6  shows that i) for the NMA-dataset, the separation between the three conformations in the retrieved conformational space is evident for all the tested noise intensities, and ii) for the MD-dataset, the trajectory is more evident for lower noise intensity (higher SNR), and it is the least evident when the SNR is 0.
Conformational variability analysis for the NMA-dataset This subsection presents the results of analyzing the conformational variability in the NMA-dataset at SNR = 0. 01 using TomoFlow. Figure  7  presents the retrieved conformational space of this dataset, highlighting three distinct groups of points in this space and their corresponding subtomogram averages. Moreover, each group average is compared with its ground-truth atomic structure at the corresponding conformation by docking this atomic structure into the average volume and displaying the volume at 40% opacity. We note here that only the distribution should be compared with the ground-truth, which indicates that the interrelationship between the conformations was retrieved correctly (i. e. , similar conformations were mapped to close points and vice versa); the limits of the horizontal and vertical axes do not correspond to those of the groundtruth since the ground-truth conformational space relates atomic structures (NM amplitudes or PCA of MD trajectory) and retrieved conformational space relates OFs.
Conformational variability analysis for the MD-dataset This subsection presents the results of analyzing the conformational variability in the MD-dataset at SNR = 0. 01. Figure  8  (A) presents the retrieved conformational space of this dataset, highlighting six selected groups of subtomograms in this space and their corresponding averages. Moreover, each group average is compared with the ground-truth atomic structure found as the group's centroid at the corresponding conformation by docking this atomic structure in the average volume and displaying the volume at 40% opacity (Figure  8 (A) ). Regarding the number of groups and their locations, it is encouraged to try more and fewer groups, which may help to better understand the conformational variability. TomoFlow software provides a graphical interface for an interactive selection of the regions in the low-dimensional conformational space in which subtomograms will be summed and their averages computed.
In general, the higher the resolution and the number of subtomograms, the more it is possible to select denser regions of smaller radii and reveal the conformational variability of the targeted macromolecule, and vice versa. Figure  7  The conformational space found using TomoFlow on the NMA-dataset with SNR = 0. 01 (the ground-truth conformational space is shown in Figure  4 ). The shown volumes are averages of three groups of subtomograms identified by the highlighted ellipses. The number shown inside an ellipse corresponds to the number of points it encloses. The bottom row displays the averages at 40% opacity with their corresponding ground-truth atomic structure docked inside for comparison. The averages obtained from the six selected groups of subtomograms in Figure  8  (A) (the selected regions of the conformational space of the MD-dataset at SNR = 0.
Conformational variability of nucleosomes in situ This section describes the application and results of TomoFlow on nucleosomes in situ, in their interphase nucleus context. We use a dataset containing 666 subtomograms (EMPIAR-10679) of nucleosomes extracted from cryo tomographic reconstruction of a vitreous section of a high-pressure frozen Drosophila embryonic brain  [17, 18] , with a subtomogram volume size of 64 3 and voxel size of 4. 4 Å 3. Reference-free rigid-body StA of this dataset was previously performed using SubTomogramAveraging script of Artiatomi (https://github. com/uermel/Artiatomi) [58]  and the average was deposited in EMDB under the accession code EMDB-12699  [18]. For the full details about this dataset acquisition and StA, we direct the reader to Section S1 (supplementary materials) of  [18].
The nucleosome conformational variability detected in this dataset in previous works  [17, 18]  was mainly described as gapping and breathing motions of the nucleosome  [35]. However, these conformational variabilities were previously identified either via manual measurements  [17]  or via selecting a set of motions of interest (set of normal modes) for data analysis  [18]. Here, our goal is to check if TomoFlow can detect the expected movements without using any prior knowledge about the nucleosome conformational variability. First, we used the StA parameters to reproduce the global subtomogram average. We used the average to generate a mask of the region of interest, which was then used to refine rigid-body alignment and analyze the conformations in the data. We generated the mask by binarizing the subtomogram average, dilating it by a structuring element of size three, keeping its largest connected component, and smoothing its boundaries with a Gaussian filter of standard deviation equal to two.
We selectively generated subtomogram averages from groups of points at the beginning and the end of the data distribution. The regions for the groups of points are shown as highlighted areas in Figure  9    The main differences between the two group averages (Figure  9  (A)) and the motion observed along line D (Figure  9  (B) and Supplementary Movie 2) indicate that TomoFlow detected a combined breathing and gapping motion of the nucleosome, with the breathing motion more expressed. These results are consistent with those of our previous study of conformational variability using the same dataset  [18]  but without imposing any prior knowledge on the analysis of the conformational variability.
Software implementation The software of TomoFlow is open-source and available as a part of the ContinuousFlex plugin of Scipion  [59, 60]. Our software provides a user-friendly Graphical User Interface (GUI). Using the default parameters, the current implementation takes 32 seconds to calculate an OF between a subtomogram and a reference of size 64 3 voxels (tested on Nvidia Quadro RTX 5000). One calculation of OF per subtomogram is required per rigid-body refinement iteration, and one more OF calculation is required per subtomogram for continuous variability analysis. The OF calculation requires the most data processing time, and therefore, the OF calculation is GPU parallelized. The number of OFs that can be calculated simultaneously depends on the number of available GPUs (one OF calculation uses one entire GPU).
Our method's second most time-demanding task is rigid-body alignment (used during rigid-body refinement), which we implemented using FRM  [49]. We parallelized the FRM implementation using a Message Passing Interface (MPI) scheme. The number of subtomograms that can be processed simultaneously using our parallelized version of FRM depends on the number of available CPU threads (one subtomogram is processed using one CPU thread). Three refinement iterations of a dataset of 1000 subtomograms of size 64 3 require around 36 hours using a single GPU card and 20 CPU threads (tested on Nvidia Quadro RTX 5000 and 2. 2 GHz Intel Xeon Silver 4214 CPU processors). TomoFlow requires StA to be performed in advance. TomoFlow software provides one StA implementation that is based on FRM  [49] , but it also allows importing StA parameters obtained with Dynamo or TOM2-toolbox (other StA software may be made compatible with TomoFlow in the future).
We presented the method with simplified yet sufficient mathematical derivations that are necessary for understanding and implementing it. We tested the method by synthesizing two datasets under challenging conditions and testing its capability in retrieving their ground-truth conformational spaces. The results of the tests with simulated data indicate that i) the proposed rigid-body refinement can improve the alignment quality in the presence of conformational variability, and ii) the proposed conformational variability analysis can accurately recover hidden conformations. Additionally, we tested TomoFlow using a cryo-ET dataset of nucleosomes in situ, which provided promising results coherent with previous findings using the same dataset  [18]  but without imposing any prior knowledge on the analysis of the conformational variability.
It should be reminded that better statistics (including those produced by PCA) are obtained for larger datasets. The cryo-ET datasets that can be collected (thus, analyzed by TomoFlow method) nowadays are still much smaller than those produced by single-particle cryo-EM. However, the goal of the PCA in TomoFlow is to reveal the major motions of the complex. In this context, a dataset of 2000 subtomograms that can be obtained nowadays may be considered large enough for such PCA and should allow revealing the main motions of the complex. In this article, we have shown the results of an experiment with 666 in-situ cryo-ET nucleosome subtomograms, which appears to be sufficient for revealing the breathing and gapping motions of the nucleosome, the two main motions of the nucleosome that have also been detected using two different methods in situ  [17, 18]  as well as in a theoretical study  [35].