dataesr/scientific-paragraphs-categorization huggingface dataset:
Interpolation between evolutionary tracks Interpolation between evolutionary tracks amounts to interpolating in the parameter space defined by the grid parameters, excluding age. In this parameter space, each track corresponds to a single point. As a first step, a Delaunay tessellation is carried out for this set of points via the Qhull package 9   (Barber et al. 1996)  as implemented in SciPy 10. As a result, the parameter space is subdivided into a set of simplices (i. e. triangles in two Y. Lebreton and D. R. Reese: SPInS, a pipeline for massive stellar parameter inference dimensions, tetrahedra in three dimensions, etc. ). Then, for any point within the convex hull of the tessellation, SPInS searches for the simplex which contains the point and carries out a linear barycentric interpolation on the simplex.
Stellar clusters As a case study, we consider the Hyades which has long been known to be the nearest open cluster, hosting about 300 members. Hipparcos made it possible to determine secure individual parallaxes of ∼ 300 Hyades members and the cluster remained the only one for which this was true until the delivery of Gaia DR1 (e. g. first data release, Gaia  Collaboration et al. 2017). The distance to the centre of mass of the Hyades, as determined by  Perryman et al. (1998)    metric ground-based measurements. Lebreton et al. (2001)  then estimated the age of the cluster from eye-fitting of isochrones calculated with a metallicity of [Fe/H] = 0. 14 ± 0. 05 as determined by Cayrel de  Strobel et al. (1997). They derived an age of 625 Myr ( 550 Myr) on the basis of stellar models with (respectively without) convective core overshooting calculated with the Cesam code  (Morel & Lebreton 2008).
Starting from the same observed sample as used by  Lebreton et al. (2001) , we re-inferred the properties of the cluster using SPInS. As for the priors, we took the IMF from  Salpeter (1955)  A&A proofs: manuscript no. article as given by Eqs. 2 and 4, a uniform truncated SFR (Eq. 6), and a flat prior on the MDF. The colour-magnitude diagram of the Hyades is presented in Fig. 11. The age of the cluster is found to be 640 ± 7 Myr on the basis of BaSTI stellar models including convective core overshooting, while it is of 543 ± 6 Myr if models with no overshooting are used instead. These results are in excellent agreement with those obtained by  Lebreton et al. (2001). Furthermore, the common metallicity of the cluster stars inferred by SPInS is [M/H] = 0. 094 ± 0. 003 with overshooting, while it is [M/H] = 0.
Conclusion We have presented SPInS, a Python-Fortran tool dedicated to the inference of stellar properties in various observational situations. SPInS is a spin-off of AIMS, a sophisticated tool focusing on thorough asteroseismic inferences using stellar models together with their individual oscillation frequencies. SPInS is simpler than AIMS in the sense that it only requires standard outputs of stellar models and no individual oscillation frequencies to operate. It can be applied to age-date, weigh, and size up stars or groups of stars, as well as to make predictions on their expected global or mean properties such as asteroseismic indices, with a considerable gain in computing time compared to AIMS. SPInS aims to be user-friendly and can run on any computer cluster or even laptop. We have first presented the fundamentals of SPInS as well as its inputs and outputs.
As output, SPInS provides any unknown stellar property available in the grid, including the age, mass, radius, or seismic indices. Any quantity can be provided either as an input if observed or inferred independently, or as an output if unknown. In order to present the different outputs of SPInS, such as histograms of the PDF of stellar parameters or the estimators of a given quantity, we have run the tool on a set of fictitious stars spanning a wide area in the H-R diagram. We then validated the SPInS program by comparing its inferences with results from the literature. We first showed that SPInS is able to reproduce satisfactorily the ages and masses of more than 10 4 stars of the GCS11 survey as derived from their absolute magnitudes, effective temperatures, and metallicities by  Casagrande et al.
(2011). We then re-visited the properties of different categories of single stars for which we have access to an extended set of observational constraints, such as radii from interferometric measurements or seismic indices. Overall, we obtained results in excellent agreement with what has been published before. Finally, we applied SPInS to the study of coeval stars. As case studies, we took the Hyades open cluster stars and the components of the eclipsing SB2 binary system AI Phe once more showing an excellent agreement with previous results. We therefore release SPInS 15 as a public tool in the hopes that it will prove to be useful in deciphering the large quantities of exquisite data currently available thanks to current (Gaia, CoRoT, Kepler, TESS) and future (PLATO) space missions and surveys.
Acknowledgements. We thank the referee,  Ted von Hippel , for helpful suggestions that improved our manuscript. We thank  Misha Haywood  and our colleagues of the Scientific Organizing Committee of the 5 th International Young Astronomer School on Scientific Exploitation of Gaia Data to have invited us to pilot a hands-on session on stellar age determination which motivated the development of the SPInS public tool. We thank our colleague  Santi Cassisi  for allowing us to provide the BaSTI evolutionary tracks on our website, in a format directly readable by SPInS.
2 mM CaCl 2 , 0. 2 mM ATP, 0. 5 mM DTT. The degree of labeling (DOL) was determined by absorption spectroscopy, using the known extinction coefficients for the dyes and proteins, or using coefficients predicted from the amino acid sequences using PredictProtein software (ExPASy, Swiss Institute Bioinformatics Resource portal). Actin, vinculin, and a-actinin 1 DOLs ranged between 25-35%. Purified polypeptides were supplemented with 10-20% glycerol, aliquoted, flash-frozen in liquid nitrogen and stored at À80˚C. Ca-monomeric actin was stored on ice and used within 2 weeks for TIRFM and micropatterning assays.
43), a Leica SuMo Stage, a PIFOC piezo nanofocusing system (Physik Instrumente, Germany) to minimize the drift for an accurate imaging, and combined with an Andor iXon Ultra 897 EMCCD camera (Andor, Oxford Instruments). Fluorescent proteins were excited using three solid-state diode lasers, 488 nm (300 mW), 532 nm (500 mW), 642 nm (500 mW). Laser power was set to 5% for Alexa-labeled proteins, and dyes were excited for 50 ms. Image acquisition was performed with 25 degrees-equilibrated samples and microscope stage. The microscope and devices were driven by Leica LAS X software (Leica Microsystems, GmbH, Germany).
Image acquisition was performed with 25 degrees Celsius-equilibrated samples and microscope stage. The microscope and devices were driven by Leica LAS X software (Leica Microsystems, GmbH, Germany). Samples were also explored using an Olympus IXplore SpinSR10 spinning disk confocal imaging system (Olympus Scientific Solutions, USA), equipped with a 60x Silicon oil objective (UPLSAPO UPlan S Apo 60x, NA 1. 3) and with two Prime BSI scientific CMOS cameras (Teledyne Photometrics, USA).
Image processing and data analysis of fluorescence images Time-lapse videos of filament growth were processed with Fiji software (NIH). For each sample, we measured the rates of barbed end elongation of at least ~10 filaments, typically from at least 20-40 frames of imaging over a span of ~200-400 s, using TIRFM. The growth rate of actin filaments was calculated using Multikymograph Fiji software plugins or a matrix-to-8-bit-image conversion program written in MATLAB, Kymographs were generated over the time course of actin assembly along the actin filament or bundle trace. For filaments or bundles that were stable in the observation field, traces were automatically picked using Multikymograph Fiji software plugins. For mobile structures, traces were manually picked. The alignment of filament pointed-end and internal fluorescence speckles of actin filaments in MATLAB-generated kymograghs accounted for the accuracy of the method.
To measure the zippering velocity of vinculin-induced crosslinking, the length of the zippered region of single filaments was measured over time for in three different experiments where 0. 7 to 1. 1 mM actin was polymerized in the presence of 600 nM vinculin and 2 mM Talin VBS1, or 300 nM vinculin and 1 mM Talin VBS1, or 300 nM vinculin, 1 mM Talin VBS1, 12 nM Arp2/3 complex and 80 nM WAVE WA. In all three experiments, measured velocities varied within the same range. Determining the number of filaments per vinculin-mediated bundle was conducted by analyzing the fluorescence intensity of bundles using the line-scan tool in Fiji software, and subsequently normalized to the fluorescence intensity of a single actin filament from the same experiment and that was as many times illuminated as the bundle.
(4) We then generated ratiometric images (488 fluorescence intensity/568 fluorescence intensity, as shown in Figure  2B ), using the default plugins in Fiji. (5) Since we kept constant all illumination parameters and all biochemical parameters of the reconstituted assays (except vinculin concentration that was 0. 25 or 0. 5 mM), we were able to perform quantitative analyses. For all patterns (>1000), the fluorescence density of actin (from actin fluorescence images) and related vinculin to actin fluorescence ratio (from ratiometric images) were measured for each network along the pixel selections presented in Figure  2C. For data analyses, we wrote a MATLAB program to correlate, for each pixel, the vinculin/actin fluorescence ratio to the actin fluorescence density. For each series of pattern geometry (repeated 29 to 70 times), we computed the distribution and the mean value of each network (branched, uniform or mixed polarity) separately.
Gold nanoparticles labeling was conducted as followed: 0. 3 mM capped actin filaments were incubated for 1 hr at 25˚C with 2 mM vinculin, 0. 8 mM AuNP-GST-VBS1, and 7. 2 mM GST-VBS1. For the experiments using vinculin tail fragment, 0. 3 mM capped actin filaments were incubated for 30 min at 25˚C with 2. 2 mM tail fragment. Next, 4 mL of the reconstituted solution and 1 mL of a 10 nm fiducial gold marker solution (Aurion, Netherlands) was applied onto glow-discharged EM grids coated with a holey carbon mesh (Cu R2/1, 200 mesh, Quantifoil). Finally, the EM grids were manually blotted and plunge frozen in liquid ethane. The EM grids were stored in liquid N 2 until used for cryo-ET data acquisition.
Cryo-ET data collection Cryo-ET data was acquired on a FEI Titan Krios transmission electron microscope (FEI, Hillsboro, USA) equipped with a GIF Quantum energy filter and a K2-Summit direct electron detector (Gatan, Pleasanton, USA). The microscope was operated at 300 keV in zero-loss mode with the slit width of the energy filter set to 20 eV. Using SerialEM  (Mastronarde, 2005) , image stacks were recorded at each tilt angle in super-resolution mode with an electron flux of ~8 electrons per pixel per second. The image stacks were acquired at a dose-fractionated frame rate of 1 frame per 0. 2 s at a magnification of 42,000Â, resulting in a pixel size of 0. 17 nm. The tilt-series covered an angular range of À 60˚to + 60˚and were recorded at increments of 2˚, at a defocus of À4 mm and a total accumulated electron dose of ~70 electrons per A ˚2.
Subsequently, we performed one round of 2D classification with the pre-aligned using RELION  (Scheres, 2012). Thereby, we removed low-quality particles and false positive particles (e. g. gold markers, background), resulting in a cleaned particle set of 29,643 actin segments. Representative 2D classes are shown in Figure  3 -figure  supplement 4B. Next, we subjected these particles to a helical 3D-refinement using RELION, using a cylinder as an initial template  (He and Scheres, 2017) , a helical rise of 27. 6 A ˚and a helical twist of 166. 7 degrees  (Galkin et al. , 2015). The obtained 3D reconstruction of actin (Figure  3 -figure supplement 4C) resolved to ~14 A ˚, as indicated by the RELION software. Actin structures were visualized with UCSF Chimera  (Pettersen et al. , 2004).
Acknowledgements This study was supported by the  Swiss National Foundation , grant no. 31003A_179418 , and the  Ma ¨xi Foundation  (to OM), the  Israel Science Foundation  (to BG) and the  Klaus Tschira Foundation  (to BG). The authors would like to thank the  Center of Microscopy and Image Analysis of the University of Zu ¨rich , and  Sascha Weidner  from the  Workshop of the Department of Biochemistry , the  University of Zu ¨rich , for the design and production of the mask holder used for the micropatterning. The authors are grateful to  Prof. Hans-Werner Fink ,  Prof. Ju ¨rg Osterwalder  and  Prof. Marta Gibert  from the  Physik-Institut of the University of Zu ¨rich  for their help with the UVO cleaner. We thank  Ed Egelman  ( University of Virginia ) for fruitful discussions during the initial stages of the project.
Introduction The collection and exchange of research information (e. g. , information about research staff, organizations, publications, project funding, patents, partners, etc. ) are integral parts and success factors in the research information management process. More and more, Higher Education and research institutes collect, integrate and store their research information in Research Information Systems (RIS) for evaluation, reporting and presentation of research results. The term RIS describes a central database or federated information system that aggregates information about research activities, results and impact. The development of RIS includes the emergence of different research information standardization initiatives at both international and national levels  [4]. The most widely used data model for RIS is the euroCRIS Common European Research Information Format (CERIF)  [14].
The CERIF Data Model is an international standard for the management and exchange of research information and describes relevant object types from all areas of research and development  [13]. Another international initiative, the Canada-based non-profit Consortia Advancing Standards in Research Information (CASRAI), develops and maintains a standard vocabulary for research information focusing on semantics  [4]   [15]. In Germany, the Council of Science and Humanities (WR) initiated in 2013 a standard for the German science system, the so-called Research Core Dataset (RCD)  1  in order to harmonize reporting across a wide variety of institutions  [9]. The RCD specifies definitions and aggregation rules for research information on staff, early-career researchers, third-party funding and budget, patents and spin-offs, publications, and research infrastructures  [5].
No particular technical system is required for its implementation; the RCD is system-agnostic. Because of the variety of RIS in German institutions, the RCD does not contain specifications for the underlying base data. Implementation of the standard is, however, aided by the provision of a technical data model in XML-format based on CERIF, which describes both base and aggregate data formats and their respective relationships. The WR recommends the RCD as a voluntary standard for German Higher Education and research institutions  [10]. However, its relevance is not limited to Germany or German speaking countries because of the international character of research and related infrastructures. A new standard for German research information management has a direct and immediate impact on RIS in other, international, European or foreign research institutions and networks.
Also, the way how the large German research community process its essential data and information may serve as a model for other national communities. Our paper investigates the potential impact of the RCD standard on RIS data quality and, moreover, on workload, usage and interoperability with other systems. Following the introduction, the second section briefly describes the peculiarities of the current RIS compared to other information systems. The two following sections three and four deal with data quality issues related to RIS implementation and provide a general concept for RIS data quality managing, together with a workflow. Based on these elements, section five explores the potential usefulness of the RCD standard for the management of RIS data quality. The conclusion summarizes our findings and provides some perspectives for further investigation.
Specifics of RIS compared to other information systems Information Systems (IS) has long been concerned with the research and development of sociotechnical systems for the capture, storage, dissemination, retrieval and management of information and knowledge  [12]. IS are built to obtain information to aid strategic decision-making. Without them, reporting processes in Higher Education and research institutes often take considerable effort: Research information is extracted manually from Excel spreadsheets, accounting systems (such as SAP, PAISY, etc. ), publication repositories (ArXiv, SSOAR, etc. ), bibliometric databases (e. g. , PubMed, Scopus, Web of Science, etc. ), identifiers (ORCID, DOI, etc. ), and other systems used. This data often goes through the hands of various employees. Next to institutional requirements of more efficient research information management, researchers themselves desire precise descriptions of their research profiles, taking into account special criteria of the evaluation, the activity of the respective discipline or they want to find and use exact subject-specific research information of other scientists.
Concrete problem statement -Introduction of RIS in academic institutions The handling of research information in RIS and its numerous applications make the issue of data quality more imminent. Many Higher Education and research institutions have difficulties in guaranteeing the quality of their data, for example, at each institution, personal data, information about their scientific activities, projects and publications are entered and recorded. Low data quality states that analyzes and evaluations are faulty or difficult to interpret. Another point is the additional work required due to low data quality. These quality issues (such as spelling errors, duplicates, missing values, incorrect data, incorrect formatting, wrong data representations and contradictions, etc. ) may arise when collecting (especially when entering manually), integrating and storing research information in different RIS  [3].
Detailed studies on the techniques, methods for improving and increasing the data quality in RIS can be found in the papers (  [2] ,  [3] ). To formulate the problem of our paper well, these questions must be answered: firstly, how to restore decision-makers' confidence in the research information that is manipulated and collected in a RIS. These are bulky, completely heterogeneous, distributed and of varying quality. In order to facilitate integration, the standardization of data models and interfaces can reduce the autonomy of the systems and thus enforce homogeneity. Here is the second question to answer: how can the RCD standard analyze the research information and improve its data quality? Understanding the research information is an important task in their integration into RIS. The goal is to better extract, interpret and reuse the research information.
) during the integration into RIS. At the beginning of the workflow, research information from internal and external data sources (e. g. , publication databases, project databases, etc. ) is collected by the management or technical staff of an organization, and subsequently these data collections are subjected to inspection and their quality is checked. In the first phase (definition of data quality), relevant data quality dimensions must be selected and corresponding requirements defined for them. Requirements can be set by a RIS administrator or manager. Examples of such requirements are presented in Table  1.
The resulting improvements are quickly lost, especially with frequently modifying research information. Therefore, data quality should not be considered a one-time action. In order to effectively improve data quality in RIS, these investigative methods and techniques need to be applied to research information throughout their lifecycle to guarantee a defined level of quality. After implementing and testing our workflow with a Quadient® DataCleaner tool using a sample of Web of Science publication data (2,600 datasets) before their integration into RIS, we were able to resolve different data quality errors and abnormalities relating to: • Incorrect author names (errors and/or name changes) • Incorrect capture of special characters in author names • Incorrect and incomplete collection of institutional information of the authors • Author name disambiguation • Multiple attributions of identical DOI to different titles and vice versa • Incorrect order of institutional information • Faulty multiple detections (e.
For this reason, the implementation of the standard like RCD in Germany should improve the research coverage of each institution. RCD aims to standardize the basics for data-driven reports on scientific activities. The RCD specification describes data grouping by content and formats for implementation in RIS. RIS architecture consists of three layers: integration layer, data retention layer, and presentation layer. RCD standard is flexible in its modeling. RCD Standard can be implemented while integrating research information into RIS or exporting data. We focus on integrating RCD into the RIS because universities and research institutions integrate many different heterogeneous data sources into RIS. In fact, collecting and integrating data is a key task in the decision-making process. In addition, the RCD should be able to model and query these multiple data sources.
An application of the RCD standard in RIS is shown in Figure  2. According to RCD standard is intended to eliminate the ambiguity in the collection of research information, thereby improving the quality of these. Internal and external source data is often contaminated by schema conflicts (such as name and structural conflicts and conflicts by data representation, etc. ) and data conflicts (such as conflicting data contents and different definitions of attributes, etc. ). According to RCD standard, the ambiguity or the finding of the existing impurities in the collection of research information to eliminate and thereby improve the quality of these. The schema and data conflicts are remedied by necessary integration steps. Using practical examples, different conflict situations and adequate solutions will be considered below using the RCD standard.
The following presents and explains the schema conflicts and data conflicts listed. • Name conflicts: Name conflicts differ in synonyms and homonyms. Synonyms are semantically equivalent objects that use different relationships for attributes and relations. Homonyms arise when semantically different objects are named the same. • Structural conflicts: In structural conflicts identity conflicts and missing attributes occur. Identity conflicts are when two relatively identical database tables or contents are identified using different primary keys. Missing attributes must be created in the target database, but care should be taken to allow null values. If there are attributes in the tables of the source databases that only occur exactly in one of the source databases, these attributes must be maintained in the target database.