dataesr/scientific-paragraphs-categorization huggingface dataset:
As noted in  [7] , a survey on quality assessment for Linked Data, completeness commonly refers to a degree to which the "information required to satisfy some given criteria or a given query" are present in the considered dataset. To complement this very general definition, we distinguish two kinds of completeness.  Constraint-based completeness measures the percentage of elements in a dataset that satisfy explicit representations of what -or how -information must be represented in the dataset. These representations are constraints such as integrity constraints or, more generally, constraints expressed by database schemas, structured document schemas, or schemas enforcing ontology design patterns. E. g. , in a particular dataset, the constraint that at least one movie must be associated to each movie actor, or the constraint that all relations must be binary.
 Real-world-based completeness measures the degree to which particular kinds of real-world information are represented in the dataset. E. g. , regarding movies associated to an actor, calculating this completeness may consist in dividing "the number of movies associated to this actor in the dataset" by "the number of movies he actually played in, i. e. in the real world". Either the missing information are found in a gold standard dataset or the degree is estimated via completeness oracles  [17] , i. e. rules or queries estimating what is missing in the dataset to answer a given query correctly. Tools such as SWIQA and Sieve help perform measures for this kind of completeness. All the completeness criteria/measures collected by  [7]  -schema/property/population/interlinking completeness -"assume that a gold standard dataset is available".
Hence, they are all subkinds of real-world based completeness. However, constraintbased completeness is equally interesting and, for its subkinds, categories named schema/property/population/interlinking completeness could also be used or have been used  [1, 4]. What the comparability ODR can be reused for to ease the measure of completeness is about constraint-based completeness. As illustrated in this article, checking such a completeness may lead the KB authors to represent information that increase the KB precision and then enable the finding of yet-undetected problems. Increasing such a completeness does not mean increasing inferencing speed. This article showed how SPARQL queries could be used for implementing comparability ODRs. More generally, most transformation languages or systems that exploit KRs could be similarly reused.
JASPAR (http://jaspar. genereg. net) is an openaccess database of curated, non-redundant transcription factor (TF)-binding profiles stored as position frequency matrices (PFMs) for TFs across multiple species in six taxonomic groups. In this 8th release of JASPAR, the CORE collection has been expanded with 245 new PFMs (169 for vertebrates, 42 for plants, 17 for nematodes, 10 for insects, and 7 for fungi), and 156 PFMs were updated (125 for vertebrates, 28 for plants and 3 for insects). These new profiles represent an 18% expansion compared to the previous release. JASPAR 2020 comes with a novel collection of unvalidated TF-binding profiles for which our curators did not find orthogonal supporting evidence in the literature. This collection has a dedicated web form to engage the community in the curation of unvalidated TF-binding profiles.
INTRODUCTION Transcription factors (TFs) are proteins involved in the regulation of gene expression at the transcriptional level  (1). They interact with DNA in a sequence-specific manner through their DNA-binding domains (DBDs), which are used to classify TFs into structural families  (2). The genomic locations where TFs bind to DNA are known as TF binding sites (TFBSs), which are typically short  (6-20 bp)  and exhibit sequence variability  (3). Genome-wide identification of TFBSs is key to understanding transcriptional regulation. As it is not possible to identify all TFBSs for every cell type and cellular condition experimentally, computational modeling of TF-binding specificities has been instrumental to predict TFBSs in the genome. These compu-D88 Nucleic Acids Research, 2020, Vol. 48, Database issue tational models aim at representing the complex interplay between nucleotide and/or DNA shape readout at TFBSs (4), and can be used to predict not only the precise location where TFs interact in the genome  (5) , but also TFs with enriched TFBSs in a set of sequences  (6) , or the impact of mutations on TF binding  (7, 8) , amongst others.
With the advent of high-throughput sequencing more than a decade ago, the number of PFMs derived from in vivo and in vitro experiments has increased dramatically, leading to the creation of multiple databases storing PFMs or more complex TF-binding profiles such as JASPAR  (19) , CIS-BP  (20)  and HOCOMOCO  (21)  (see  (22)  for a comprehensive review). The JASPAR database (http://jaspar. genereg. net/) is one of the most popular databases of TF-binding profiles, and has been maintained for over 15 years  (23). As such, many computational tools dedicated to the study of gene regulation incorporate profiles from JASPAR (e. g. TF-BSshape  (24, 25) , RSAT  (26) , MEME  (27)  or i-cisTarget (6)). At the heart of JASPAR is its CORE collection, which contains TF-binding profiles that are: (i) manually curated (meaning that orthogonal supporting evidence from the literature is required for each profile); (ii) non-redundant (one profile per TF with the exception of TFs with multiple DNA-binding sequence preferences (28)); (iii) associated with TFs from one of six taxa (vertebrates, nematodes, insects, plants, fungi, and urochordata) and (iv) freely available to the community through a user-friendly web interface, a RESTful API  (29) , and a dedicated R/Bioconductor data package ('JASPAR2020').
EXPANSION AND UPDATE OF THE JASPAR CORE COLLECTION For this 8th release of JASPAR, we added to the CORE collection 245 new TF-binding profiles for TFs in the following taxa: vertebrates (169 profiles, corresponding to an expansion of 29% for this taxon), plants (42 profiles, 9% expansion), nematodes (17 profiles, 65% expansion), insects (10 profiles, 8% expansion) and fungi (7 profiles, 4% expansion). We updated 156 profiles (Table  1 ). The new PFMs were derived from HT-SELEX  (30) , PBMs  (20) , ChIP-seq and DAP-seq experiments (data sourced from CistromeDB  (31) , ReMap  (32, 33) , GTRD  (34) , ChIP-atlas  (35)  and ModERN  (36) , see Supplementary Text for method details). As pre- viously described, the newly introduced profiles were manually curated to be supported by an orthogonal reference from the literature, which is provided in the metadata of the profiles.
Moreover, the TF DBD class and family (following the TFClass classification (2)), the TF UniProt ID  (37) , and links to the TFBSshape (24,25), ReMap  (32, 33)  and UniBind  (38)  databases are provided in the profiles metadata (whenever possible). Finally, the profiles previously associated with ID2, ID4 and TRB2 were removed from the CORE collection as these proteins are not TFs (1). Overall, the JASPAR 2020 CORE collection includes 1646 non-redundant PFMs (746 for vertebrates, 530 for plants, 183 for fungi, 143 for insects, 43 for nematodes and 1 for urochordates) (Table  1 ; Figure  1 ). Moreover, we continued with the incorporation of novel transcription factor flexible models (TFFMs), which are hidden Markov-based models capturing dinucleotide dependencies in TF-DNA interactions  (11).
We introduced new TFFMs for 217 TFs (136 for vertebrates, 38 for plants, 21 for insects, 17 for nematodes, and 5 for fungi) and updated TFFMs for 20 verte-D90 Nucleic Acids Research, 2020, Vol. 48, Database issue brates TFs, which represents a 50% increase in the number of TFFMs available. All data is available on the JASPAR website, its associated RESTful API, and through the JAS-PAR2020 R/Bioconductor package.
CLUSTERED PROFILES, GENOMIC TRACKS AND PROFILE INFERENCE TOOL In the previous releases, we introduced novel features such as hierarchical clustering of TF-binding profiles in the CORE collection to visualize profile similarities, genomic tracks of predicted TFBSs, and an inference tool to predict TF-binding profiles likely recognized by TFs not available in the JASPAR CORE. We improved the profile inference tool using our own implementation of a recently described similarity regression method  (20). We updated the generation of genomic tracks that are publicly available through the UCSC Genome Browser data hub (39) for 7 organisms: human (hg19, hg38), mouse (mm10), zebrafish (dan-Rer11), Drosophila melanogaster (dm6), Caenorhabditis elegans (ce10), Arabidopsis thaliana (araTha1) and baker's yeast (sacCer3).
CONCLUSIONS AND PERSPECTIVES Similar to previous releases, we substantially expanded the CORE collection of the JASPAR database. For this 8th release, we processed more than 18,000 ChIP-seq datasets. As a large number of the obtained high-quality TF-binding profiles were not supported with orthogonal supporting evidence, it motivated us to create the novel 'unvalidated' collection of profiles. We expect that upcoming experiments and publications will provide additional supporting evidence to some profiles to be incorporated into the JASPAR CORE collection. Meanwhile, we would like to extend our invitation to the research community to 1) help us curate these unvalidated profiles (e. g. by pointing us to supporting literature), and 2) send us their own novel profiles (e. g. determined experimentally) for incorporation in the next release of JASPAR.
The JASPAR CORE vertebrates collection now contains 746 profiles, 637 of which are associated with human TFs with known DNA-binding profiles  (1) , which corresponds to a 58% of the 1,107 reported by Lambert et al. (1). While this is an impressive collective achievement by the field (the original JASPAR database only contained 81 profiles, a ∼7% coverage for human TFs), it suggests that targeted experimental efforts to find the binding preferences for remaining TFs will be important. Although computational approaches can be used to infer missing TF-binding profiles  (20, 41) , especially for non-model organisms, the JAS-PAR approach is conservative, including profiles supported by at least two experiments in the literature. This is very important as we stand by the reliability of our data.
Since its initial publication in 2004  (23) , the JASPAR database has been committed to provide the research community with high-quality, manually curated, non-redundant TF-binding profiles. Lastly, although PFMs have dominated the field of gene regulation for decades, new profile representations have emerged. For example, profiles with expanded alphabets to represent methylated bases  (42, 43) , modelling binding energy  (44)  or derived from deep learning importance scores  (45). Depending on how the field evolves and how popular these profiles become, we will consider them for inclusion in JASPAR in the future.
ACKNOWLEDGEMENTS We thank the user community for useful input and the scientific community for performing experimental assays of TF-DNA interactions and for publicly releasing the data. We thank  Giovanna Ambrosini  for her help with PWMScan, the  UCSC Genome Browser Project Team  for their assistance with the genome tracks, WestGrid (https://www. westgrid. ca), Compute Canada  (https://www. computecanada. ca), Georgios Magklaras  and  Georgios Marselis  for their IT support,  Jacques van Helden  and  Adam Handel  for contacting us to add and validate TF binding profiles, and  Dora Pak  and  Ingrid Kjelsvik  for administrative support.
FUNDING Norwegian Research Council  [187615];  Helse Sør-Øst ;  University of Oslo  through the  Centre for Molecular Medicine Norway (NCMM)  (to A. M. , J. A. C. -M. , A. K. , M. G. );  Norwegian Research Council  [ 288404  to J. A. C. -M. and Mathelier group]; The  Norwegian Cancer Society  [197884 to Mathelier group]; O. F. , X. Z. , P. A. R. , S. C. and W. W. W. were supported by grants from the  Canadian Institutes of Health Nucleic Acids Research ,  2020 , Vol. 48, Database issue  D91 Research [BOP-149430  and  PJT-162120 ];  Genome Canada  and  Genome British Columbia  [ 255ONT  and  275SIL ]; Michael Smith Foundation for Health Research [17746];  Natural Sciences and Engineering Research Council of Canada   Discovery Grant  [ RGPIN-2017-06824 ]; CREATE programs;  Weston Brain Institute  [20R74681];  BC Children's Hospital Foundation and Research Institute; Netherlands Organization for Scientific Research [Rubicon fellowship  to R.
v. d. L. , 452172015];  Genome British Columbia  [ SIP007  to B. P. M. ]; A. S. was supported by grants from the  Lundbeck Foundation , the  Danish Cancer Foundation , the  Danish Innovation Fund  and the  Danish Council for Independent Research. F. P. was supported by the  French National Agency for Research [FloPiNet   ANR-16-CE92-0023-01 ;  GRAL ,  ANR-10-LABX-49-01 ]; D. B. is a recipient of a  Rutherford Fund Fellowship.
MXCuBE2 is the second-generation evolution of the MXCuBE beamline control software, initially developed and used at ESRF -the European Synchrotron. MXCuBE2 extends, in an intuitive graphical user interface (GUI), the functionalities and data collection methods available to users while keeping all previously available features and allowing for the straightforward incorporation of ongoing and future developments. MXCuBE2 introduces an extended abstraction layer that allows easy interfacing of any kind of macromolecular crystallography (MX) hardware component, whether this is a diffractometer, sample changer, detector or optical element. MXCuBE2 also works in strong synergy with the ISPyB Laboratory Information Management System, accessing the list of samples available for a particular experimental session and associating, either from instructions contained in ISPyB or from user input via the MXCuBE2 GUI, different data collection types to them.
Introduction The past 15 years or so have seen a dramatic increase in the productivity of synchrotron macromolecular crystallography (MX) beamlines, both in terms of the number of crystals analysed and the number of new crystal structures deposited in the Protein Data Bank (PDB)  (Berman et al. , 2000). This evolution has, to a large extent, been driven by improvements in beamline hardware and software. In particular, the smaller X-ray beams provided by third-generation synchrotron ISSN 1600-5775 sources have opened new avenues in MX, making it possible to collect diffraction data, using increasingly advanced experimental protocols, from smaller crystals of increasingly intricate and important structural biology targets. However, in order that experimenters can take best advantage of the increasingly complex data collection options available, a robust, user-friendly and adaptable beamline control graphical user interface (GUI) is essential.
In 2006, the ESRF and the Medical Research Council UK (MRC-UK) started to develop the MXCuBE beamline control GUI  (Gabadinho et al. , 2010)  with the aim of providing the users of its various MX facilities with an almost identical user-control environment regardless of any differences in beamline hardware. MXCuBE was initially deployed both at the ESRF MX beamlines and at the Collaborative Research Group (CRG) beamline BM14. This was quickly followed by installations on the MAX-lab beamline I911-3 and the Helmholtz-Zentrum Berlin (HZB, BESSY II) beamline BL14. 1  (Mueller et al. , 2012). However, installation at sites other than the ESRF was not always straightforward due to differences in the low-level beamline server and control software installed. Discussions concerning the development of the second-generation MXCuBE2, designed to be independent of both beamline hardware and lower-level server and control software, were thus instigated and the MXCuBE Collaboration was formed (x2).
The MXCuBE2 GUI resulting from the collaboration was also simplified, permitting a closer interaction with the sample under study while, at the same time, including a growing number of new MX experimental methodologies. In particular and by design, MXCuBE2 is able to interact with external 'experiment descriptors'  (Brockhauser et al. , 2012)  which can iteratively define and adjust data collection parameters according to results obtained by connected analyses. Although there is no reciprocal dependency, user experience is greatly enhanced by an increased synergy between MXCuBE2 and the ISPyB Laboratory Information Management System (LIMS)  (Delageniere et al. , 2011). To better interact with MXCuBE2, ISPyB has been expanded to include fuller sample descriptors and 'diffraction plans' and now stores all the details of all the experiments (including the results of on-line data analyses) performed on a given sample.
The MXCuBE Collaboration Experience gained at ESRF, first with ProDC  (Arzt et al. , 2005) , then with initial versions of MXCuBE  (Gabadinho et al. , 2010) , showed that the development, maintenance and continuous upgrade of GUIs for the control of experiments at synchrotron-based MX beamlines is an extremely labourintensive endeavour requiring significant human resources from beamline scientists and software engineers. This is particularly the case when the beamline control GUI is linked to synchrotron-based LIMS such as PXWeb  (Arzt et al. , 2005)  or ISPyB  (Delageniere et al. , 2011). In Grenoble, the ProDC/PXWeb combination for synchrotron beamline control/experiment-tracking was developed by the ESRF-EMBL Joint Structural Biology Group (JSBG) while initial versions of the MXCuBE/ISPyB combination were developed in collaboration with the MRC-UK operated BM14 CRG.
The JSBG-BM14 link-up had two aims: in tandem with funding from the BIOXHIT [Biocrystallography (X) on a Highly Integrated Technology Platform for European Structural Genomics] project, the collaboration helped ensure the availability of sufficient human resources to develop, maintain and upgrade MXCuBE/ISPyB and, at the same time, provided a standard beamline control GUI for experiments at eight ESRF-based MX beamlines. The MRC-UK BM14 CRG ceased operating in 2011. In addition to the ESRF, MXCuBE was also installed on MX beamlines at the MAX-II, HZB (BESSY II), EMBL-Hamburg and SOLEIL synchrotron sites who, along with the protein crystallography software company Global Phasing Ltd, continued to provide ideas and human resources for its development. This informal collaboration was officialized by the signing in 2012 of a Memorandum of Understanding (MoU), the aims of which were to define the organization of a collaboration, to oversee the installation, support, documentation and further development of MXCuBE at several European Synchrotron sites, to present a similar and familiar interface for MX experiments at all of the synchrotron sites involved, and to share the human resources costs of the development, support and maintenance of the software.
The collaboration has continued to expand and the partners now comprise ESRF, EMBL, Synchrotron SOLEIL, HZB (BESSY II), ALBA, DESY, MAX IV Laboratory and Global Phasing Ltd, while Elettra and LNLS are in the process of joining the collaboration. Management of the collaboration is handled by three committees: the MXCuBE Steering Committee (STC), the MXCuBE Scientific Committee (SC) and the MXCuBE Developers Committee (DC). As a minimum condition for joining the collaboration each partner is required to assign a software engineer who should contribute at least 10% of working time to developments carried on within the collaboration. The role of the STC is to define the long-term strategy of the collaboration ensuring, in particular, adequate manpower and financial resources for the smooth functioning of the collaboration.
The SC defines the scientific requirements and development priorities for MXCuBE while members of the DC are charged with implementing software developments requested by the SC. The DC also ensures that at any given time there is only a single official MXCuBE version, available through a publicly accessible website for download and collaboration on source code (https:// github. com/mxcube; https://www. mxcube. org). The STC usually convenes only at biannual MXCuBE Collaboration meetings, organized by each of the partners in a round-robin fashion, at which progress is presented and strategic issues discussed. Meetings of the SC and the DC are held more frequently with, outside of the MXCuBE Collaboration meetings, sessions organized on an ad hoc basis as and when necessary. New members of the collaboration may join at any time.
All that is required is the agreement of the STC and the subsequent signing by the new partner of the MXCuBE MoU. The setting up of the MXCuBE Collaboration has provided, to the benefit of the European structural biology community, a stable, productive environment for the development of MXCuBE. Such has been the success of the collaboration that its MoU-based membership and management structure have now been adopted by the ISPyB Collaboration (https:// github. com/ispyb) recently set up to oversee the joint development of the ISPyB LIMS.
The design of MXCuBE2 MXCuBE2 comprises two main components: a GUI and a data acquisition control layer. The MXCuBE2 GUI, similarly to its predecessor, is built using the Bliss framework  (Guijarro et al. , 2004 ; http://github. com/mxcube/BlissFramework), a tool developed at the ESRF for building graphical interfaces for beamline instrumentation control and based on Python and the Qt3 toolkit. Porting to more recent versions of Qt (versions 4 and 5, respectively) has been carried out within the MXCuBE Collaboration and has allowed the extension of the lifetime of the MXCuBE2 GUI beyond the official support provided by the Qt3 packages in different Linux distributions. The GUI is built by combining different graphical widgets named bricks. Bricks are individual graphical objects integrated in the GUI.
Beamline abstraction layer -hardware objects Hardware objects are Python classes directly associated with a configuration eXtensible Markup Language (XML) file which make the link between beamline physical devices and the user interface graphical components. Hardware objects are based on abstract classes and are accessed through a common application programming interface (API) from MXCuBE2. This allows the straightforward adaptation of MXCuBE2 to the different hardware and beamline specificities of different synchrotron sites. Hardware object XML configuration files can be made available either by a Hardware Repository Server or just read from a directory. The added benefit of the Hardware Repository Server is that it allows shared read and write access to hardware objects. Indeed, when the server modifies XML files it can send notifications to listening instances of MXCuBE2, which can, in turn, reload hardware objects accordingly.
e. GrenADES  (Monaco et al. , 2013) ; XDSAPP  (Sparta et al. , 2016) ; autoPROC  (Vonrhein et al. , 2011), etc. ]. 4. 3. 2. Characterization. Characterization of crystals has the scope of determining an optimal standard data collection strategy that fulfils certain requirements, most notably the taking into account of radiation damage to a sample during a data collection, defined by the user. The MXCuBE2 characterization pipeline is embedded into the EDNA framework  (Incardona et al. , 2009)  and links software for the indexing and/or integration of diffraction images (MOSFLM, LABELIT, XDS)  (Leslie & Powell, 2007; Sauter & Poon, 2010; Kabsch, 2010) , absorbed dose calculation/prediction (RADDOSE)  (Paithankar & Garman, 2010)  and data collection strategy calculation (BEST)  (Bourenkov & Popov, 2010).