dataesr/scientific-paragraphs-categorization huggingface dataset:
sbgrid. org/DOI. ', where DOI is the digital object identifier for a particular data set. The rsync utility, which is native to Linux and OS X systems, is particularly suitable for downloading large data files and can be restarted in case of interruption. After download, the data integrity of individual data sets can be verified by following instructions on the Data Grid website. With a well defined and permissive CC0 access licence and multiple channels for accessing data (four DAA sites and the rsync download mechanism) our initial infrastructure is well suited to support expansion of the data collection. Data publication cycle. For many SBGrid laboratories, interest in data deposition is driven by a desire to better organize research data and comply with institutional, federal, and projectspecific data preservation requirements.
During the pilot phase, data deposition privileges were limited to SBGrid member laboratories. With recent funding to further support the project, the Data Grid is now open to the entire structural biology community. Non-SBGrid groups would first need to register with the SBDG to obtain proper deposition credentials. Wide adoption of data preservation systems is often hindered by the complexities involved in the data deposition process itself. To mitigate this problem, SBDG deposition involves two simple steps: registration and uploading (Fig. 5b ). To register a data set, the depositor completes a web form with basic information about the sample, data collection facility, related objects (for example, publication, PDB code), and authorship; this information is mapped to the DataCite schema (Fig.
Data citation examples are provided on individual data set pages (Fig. 4 ). The SBDG complements our AppCiter application  28  , which facilitates citation of research software. Both services are now presented to users in a unified publication support workflow (Fig. 7a ). In step 1, the user deposits research-related data that are put on hold until publication. A set of DOIs and corresponding data citations are then generated and provided to the end-user. Users can also use AppCiter to generate a list of software citations for all scientific software used in the project. In step 2, all research data and scientific software citations are included in the References section of the manuscript. In step 3 the user, anticipating manuscript publication, contacts relevant databases to request release of the primary and supporting data.
This process should, ideally, take place before manuscript publication and be timed to coincide with the publication date, allowing the community to access the data when the manuscript is released. When preparing future publications that refer to completed structures, scientists should reference the relevant publications and macromolecular models, unless they are referring to a specific data set. For specific data sets, authors should explicitly reference experimental data using the corresponding data citation (Fig. 7b ). Citation metrics for published data sets will be comparable to those obtained for journal publications. Data grid content. Ease of data deposition and community-wide interest facilitated growth of the initial collection of X-ray diffraction data sets when it opened to the SBGrid community in May 2015.
The data sets deposited during the pilot collection phase represent a wide cross-section of structures and a diverse subset of journal articles and structure determination methods. For example, 68 structures derived from data deposited in the SBDG have been determined by molecular replacement, while For a proof of concept, released data sets in the SBGridDB were reprocessed with xia2 (refs 36-42) in a fully automated manner (Fig. 8a ). In all, 90 of the 110 released data sets with a corresponding PDB ID were successfully reprocessed. In all,86 of those 90 data sets represented high-resolution, native data and for 51 of those xia2 decision making determined a high-resolution limit within 0. 1 Å of the published structure (Fig. 8b ). The point  group determined by reprocessing agreed with that of the published structure in 79 cases; for 65 of these the space groups agreed.
X-ray diffraction reference subset and other collections. To take advantage of data grid diversity, we have selected a small subset of cases that could be used to support software development and teaching of data processing and diverse structure determination techniques (Table  2 ). This subset includes high-resolution (1. 2 Å), low resolution (4. 5 and 7. 0 Å), anisotropic and twinned data sets. Additionally data sets that supported a variety of experimental phasing approaches (for example, phasing with selenium, zinc, uranium, barium/potassium) and molecular replacement cases (for example, with a 9 Å electron microscopy (EM) envelope) are included. The subset also incorporates diffraction data for crystals grown in lipidic cubic phase and an example of multi-crystal averaging. Additionally SBDG is suited to support various other primary data types that are being generated by members of the consortium, and those pilot collections will seed development of community-wide data analysis systems.
MicroED is a promising new technique  50, 51  and inclusions of the early microcrystal data sets might stimulate the community to explore this technique and to fine-tune data processing software. Examples of MicroED data sets that are included in the pilot collection include three MicroED data sets that were used to determine structures of the toxic core of a-synuclein  52  , catalyse  53  and lysozyme  54. Other types of data sets in our pilot collection include a 55 GB computational decoy data set for  Data sets from 5 crystals of SNX17 FERM domain in complex with a peptide corresponding to KRIT1's NPxY2 motif. Separate integration of the data sets and scaling together allows a complete 3. 0 Å data set for molecular replacement solution (original paper used 4GXB as a search model) and structure refinement.
10. 15785/SBGRID/117 Baxter Laboratory Reference Case 2: MR/Low resolution, twinned with rotational pseudosymmetry. 3. 70 Å data set collected on a crystal of thioester-containing protein 1 *S1 allele (TEP1*S1). Initial data processing suggested P4 3 2 1 2, but one of the two molecules (B1300 aa. each) in the ASU overlapped with its symmetry-mate. Comparison of alternative scenarios in refinement identified the true space group as P4 3 with twinning and rotational pseudosymmetry. Refinement was completed with TLS, NCS (local) and external restraints derived by ProSMART 65 using TEP1*R1 (PDB 4D94) as reference. Reference Case 4: Ba/K SAD; 91 nt RNA-chromophore complex. 2. 5 Å data set collected at ALS BL 5. 0. 2 using 6. 0 keV X-rays from a crystal of 'Spinach' a fluorescent RNA analogue of GFP.
Although anomalous signal was very weak, a heavy atom substructure comprised of one barium and six potassium ions resulted in good quality SAD electron density maps. 10. 15785/SBGRID/3 Sliz Laboratory Reference Case 5: Zn SAD; 4 Zn/ASU protein/RNA complex. 2. 9 Å Zn SAD data set was sufficient to determine a crystal structure of Lin28/let-7d protein-microRNA complex. X-ray beam size was adjusted to maximize flux and minimize radiation damage. One swappeddimer is located in each asymmetric unit. Two native zinc atoms are located in each tandem CCHC zinc knuckles domain. 10. 15785/SBGRID/123 Heldwein Laboratory Reference Case 6: 3. 29-Å SeMet SAD 9 Se/ASU This 3. 29-Å selenomethionine SAD data set, collected at 0. 9789 Å wavelength at BNL X25 beamline, was sufficient to determine the phases and to trace the structure of HSV-2 gH/gL complex  66.
Discussion We have developed a flexible data publication system, the SBDG, to support deposition of a variety of large primary data sets. The data repository complements the wwPDB efforts by preserving the raw data that supports PDB-deposited structure models. The pilot phase of the project, which was limited to SBGrid laboratories, demonstrated both feasibility and strong participation, with the deposition and publication of 117 data sets (as of 1 September 2015, collected over 3 months). To support annotated data collection, we have established data processing pipelines that will evolve the post-deposition data-analysis process. For example, the pipeline presented in the results section allows depositors and SBDG curators to quickly identify image-header problems, and parameters that are refined or corrected will be included in the expanded Dataverse schema  [61] [62] [63] [64].
The outliers and failures of the current reprocessing pipeline illustrate areas of potential improvement to metadata accuracy and the pipeline itself. Data depositors and other community members will be able to provide data annotations to assist with the convergence of this process. Access to this growing collection of X-ray diffraction data sets will support the proposed paradigm shift in the community  6  from the static archive towards a much more dynamic body of continuously improving refined models. Despite being in the age of 'big data science', universal storage of large, biomedical data sets is an issue that has not yet been resolved, as infrastructure and support responsibilities have not been well defined. Shifting the burdens of data management from individual research groups and institutions to global infrastructures is an effective and economical strategy to address this issue that has previously been proven successful by the wwPDB and would now be demonstrated by the SBDG.
By virtue of the consortium's global presence, SBDG is well positioned to stimulate community-wide participation. SBGrid may facilitate integration of the data grid with regional projects and facilityrelated efforts to preserve primary diffraction data sets. This data distribution model is similar to those established in other fields. For example, the Data Preservation Alliance (www. data-pass. org) replicates and indexes quantitative data for the social sciences. Data collected at the Large Hadron Collider are made available under a multi-tier processing and storage framework. As a large international consortium backed by diverse funding mechanisms and DAA storage contributions of its members, SBGrid is uniquely capable of bypassing grant limitations that would otherwise deter such a long-term global infrastructure effort.
Given recently secured funding to support data curation and technology integration under the Dataverse research data management, and with gradual community investment, SBDG is poised to scale up to support the entire community. While access to experimental data is critical to ensuring research reproducibility, metadata quality is also crucial. Data sets that are poorly annotated have limited use to the research community. With a focus on deployment of a sustainable and flexible data management infrastructure, the SBDG takes a unique approach on metadata preservation. The repository employs an accommodating DataCite schema, which preserves basic information about experiments. The depositions are selfmoderated by contributing laboratories, with data publication subject to approval of the principal investigators.
As our results demonstrated, this approach worked well for the vast majority of data sets deposited in the SBDG, 82% of which were automatically reprocessed with current data processing software and the majority of the remaining data sets could be easily reprocessed manually. This success rate for reprocessing diffraction data sets was achieved without any explicit quality control to ensure that the data sets contained sufficient information for reprocessing-in other words, using image headers as the only source of experimental (geometry and detector) parameters. Two possibilities under consideration for maintaining and improving this success rate are allowing depositors to annotate updated experimental parameters (for example, beam center) and explicit checks for metadata required for reprocessing prior to data publication.
To facilitate interoperability with other projects and further stimulate uniform data evaluations, we will work in parallel to develop tools that will support download of archived data sets in community accepted master formats supporting intrinsic metadata, such as OME-TIFF or HDF5. This process will allow annotation of downloadable data sets with additional information from analysis pipelines, and will be guided by feedback from projects that interface with SBDG. Ideally, publication of data sets will encourage the communities to adopt standardized formats and ensure complete population of experimental metadata with adequate accuracy to support reprocessing. While the SBDG immediately serves the well-defined area of X-ray crystallography, our pilot project has demonstrated that our infrastructure can preserve additional data types, such as decoy data sets for NMR computations or MicroED data sets.
SBDG will duplicate XFEL data sets that are currently accessible through the Coherent X-ray Imaging Data Bank (http:// www. cxidb. org/) and support their distribution by DAA. In addition, SBDG will collaborate with MicroED and XFEL collection curators who will moderate development of community driven efforts to automate data analysis pipelines to parallel automatic processing of X-ray diffraction data sets with packages like DIALS or xia2. We envision that the tools and technologies that arise from this project will ultimately lead to the development of a fully featured, primary data publication system. Features of such a system would include the capability of supporting a variety of experimental data types and automatic incorporation of pertinent data set information during data collection at local, regional and national facilities.
The integration of primary data management with a base set of scientific software enables repositories to progress towards dynamically improving sources of knowledge, as well as providing an integrated computing environment for ongoing research. In summary, we have presented the SBDG, a new system for the preservation and publication of large experimental data sets. The system is the latest product of SBGrid's mission to maintain a community-wide research-software infrastructure. Through disclosure, adoption, transparency, management of external dependencies, permissible licensing, and technical protection mechanisms, the SBDG is committed to compliance with evolving community standards of data preservation. We expect that the widespread sharing of experimental data will support methods development and will ultimately lead to better quality of structural models that are subject to continuous methods improvement.
Methods Current implementation. The databank deposition process involves five stages: (1) recording associated metadata, (2) local checksum calculation, (3) data transfer, (4) post-transfer verification and (5) public identifier registration. A publicly accessible web frontend is used for handling user interactions with the databank. Built using the Python-based Django web framework, this frontend runs on an Ubuntu 14 LTS Server with a PostgreSQL 9. 3 database. It collects the necessary metadata during deposition and informs the backend systems about deposition requests. A cryptographic checksum (SHA1 SUM, FIPS 180-4) is calculated before data transfer. This ensures that the data set is unchanged. Data transfer is handled by rsync over ssh. Once data transfer is complete, the databank verifies that the data set has been transferred uncorrupted, or reports a problem with the data set.
If necessary, extraneous files (intermediate data files, processing or transfer scripts) are removed, data files are uncompressed and checksums re-computed. In the event of any modifications to the data set, an unmodified copy is stored in an offline file system. Upon data set release, the DOI reserved during data set registration is registered using the recorded metadata, and the data set (including checksum information) is made available for download over anonymous rsync. Metadata schema. DOIs are issued through EZID, through the Harvard University Library and the California Digital Library. Metadata are organized following the DataCite schema (Fig. 6 ). Reprocessing details. Data sets that had been publicly released by 1 September 2015 were reprocessed by xia2 in a fully automated manner.
Acknowledgements Development of the Structural Biology Data Grid is funded by  The Leona M. and Harry B. Helmsley Charitable Trust   2016PG-BRI002  to PS and MC. Development of citation workflows is supported NSF  1448069  (to PS). DAA is being developed as a pilot project of the National Data Service, with additional funds to support storage and technology development, including  NIH   P41 GM103403  ( NE-CAT)  and  1S10RR028832 (HMS)  and  DOE DE-AC02-06CH11357 ;  NIH 1U54EB020406-01 ,  Big Data  for Discovery Science Center; and  NIST   60NANB15D077  ( Globus Project ). AB acknowledges  Ariel Chaparro  for assistance with the  DAA setup (Inst Pasteur Montevideo ). Collections of pilot data sets were supported by various grants (see Supplementary Table 1).
EXPERIMENTAL RESULTS AND DISCUSSIONS The MR structure (Fig. 1a, 1b ) based on PS ridge waveguides is made by the formation of two layers of PS with different porosities followed by a thermal oxidation and then a standard photolithography process patterning with polymer photoresist and etching steps (wet and dry). The thermal oxidation is necessary to stabilize the PS structure and can be adapted to obtain a partial or total oxidation of PS. The MR PS structure which is studied in this abstract has been totally oxidized in order to get transparent porous silica ridge waveguides. The dimensions of the porous silica core waveguide (2×2. 5 µm 2 ) shown in Fig. 1b , have been chosen to provide single mode propagation at 1. 55 µm. The effective refractive index method, involving solutions to the Maxwell equations, and Matlab software were used to calculate the effective refractive index of the ridge porous silica waveguide as a function of its width and height for three different TE modes: the 00 fundamental mode, the 01 mode and the 10 mode.
Introduction Protein-protein interactions (PPIs) can provide useful targets for the development of novel antiviral and antibacterial therapeutic agents. PPIs are also of particular interest for cancer treatments where there is a growing need to target more precisely unhealthy cells and to avoid unnecessary damage to healthy cells. However, such approaches require knowledge of the three-dimensional (3D) structure of the target complex. Exploiting existing protein structures in the Protein Data Bank (PDB) can help, but only a small fraction of these structures are complexes  (Berman et al. , 2000). Therefore, computational protein docking predictions, being low-cost and easy to perform, would be very attractive if they could reliably identify the most probable binding conformation of a pair of interacting proteins.
Empirical scoring functions consist of a linear combination of terms that reflect important factors such as hydrophobic contacts, hydrogen bonding, accessible and buried surface area (e. g. Bo ¨hm, 1994; Eldridge et al. , 1997; Friesner et al. , 2004; Wang et al. , 2002). Multivariate regression analysis is used to determine the weights of the different terms such that scoring provides good agreement with a training set of complexes. These scoring methods can be easily adapted to a specific type of interaction  (Korb et al. , 2009). However, they strongly depend on the dataset. Data-driven potentials are typically a sum of pairwise statistical potentials calculated from structural information from databases of molecular complexes  (Chuang et al. , 2008; Huang and Zou, 2011; Mooij and Verdonk, 2005; Muegge and Martin, 1999; Popov and Grudinin, 2015; Zhou and Skolnick, 2011).
We deduce the potentials using a convex optimization of a well-defined problem. Our potentials represent 210 types of distance-dependent pairwise interactions of free functional form. We optimize the shapes of the potentials such that the ability of the scoring function to discriminate native from nonnative poses is maximized. While our potential still depends on the dataset, its expression is simpler than a physics-based potential and does not depend on a reference state. Once learned, the potentials can be stored and used later for docking predictions, almost with no cost. The only current limitation is the linearity with respect to coordinates of atoms and the rigid-body assumption. This approach is very versatile and can be readily adjusted to other types of interactions. Indeed, we have already used the same methodology to derive potentials for the refinement step of proteinligand docking  (Grudinin et al.
Training set We used the training database of 851 non-redundant protein-protein complex structures prepared by  Huang and Zou (2008). This database contains protein-protein complexes extracted from the PDB  (Berman et al. , 2000)  and includes 655 homodimers and 195 heterodimers. We used only the structures of the heterodimers for the training. For each native conformation of the complex from the training set, we added 199 non-native conformations (decoys) generated by applying rigid transformations to the ligand. These are the false-positive predictions sorted by the score and generated by the default Hex shapecomplementarity function. We defined the decoys as the structures with a ligand RMS (L-RMS) greater than 10 A ˚from the native pose. L-RMS is the RMS deviation between the true and the predicted ligands when the receptors are aligned.
In particular we formally demonstrate that the problem of rewiring a signed and directed network preserving its functional connectivity (F-rewiring) reduces to the problem of rewiring two induced bipartite networks. Additionally, we reformulate the lower bound to the iterations' number of the switching-algorithm to make it suitable for the F-rewiring of networks of any size. Finally, we present BiRewire3, an open-source Bioconductor package enabling the F-rewiring of any type of unweighted network. We illustrate its application to a case study about the identification of modules from gene expression data mapped on protein interaction networks, and a second one focused on building logic models from more complex signed-directed reference signaling networks and phosphoproteomic data. Conclusions: BiRewire3 it is freely available at https://www.
Such proof exists for methods rewiring directed binary networks based on swap-and-fill strategies applied to their adjacency matrices  [18]  but not dealing with DSNs. Finally, some recent methods have been proposed to solve the related (but yet different from FCL preserving rewiring) problem of randomizing metabolic networks in a mass-balanced way  [19]. In  [20]  we showed how an algorithm based on a Monte Carlo procedure known as the switching-algorithm (SA)  [21]  can be used to efficiently randomize large cancer genomics datasets preserving the mutation burdens observed across patients and the number of mutations harbored by individual genes (hence to efficiently rewire large bipartite networks). To this aim, we derived a novel lower bound for the number of steps required by the SA in order for its underlying Markov chain to reach a stationary distribution.