dataesr/scientific-paragraphs-categorization huggingface dataset:
A secondary goal of this paper was to gather data on what features are needed in a traceability system/tool that can be adopted successfully in industry. There has also been some work on the visualization of traceability links. Marcus et al. [43]  was the first paper attempting to visualize traceability links with TraceViz, and presents a list of visualization requirements. More recently Chen's work  [44]  focuses on visualizing traceability links in a project using a graph toolkit in Eclipse. VisMatrix  [45]  generates a graphical representation of the requirements traceability matrix. None of the above work conduct empirical studies to determine the feasibility of these visual representations in practice. We consider this paper to be the first attempt to conduct an empirical study to understand what industry developers actually need in a traceability tool.
The above work complements the work in this paper. Neumuller et al. [50]  also developed and applied a traceability system in a small company and reported their findings. The findings from our study corroborate with  [50] ; in both cases a need for better tools is called for. The study presented in this paper is a controlled experiment and differs from  [50]  because in this study, focused software maintenance tasks were used to determine the usefulness of the links via a prototype tool, TraceLink. Heindl et al. [51]  discussed the application of requirements traceability to risk assessment and developed a cost-benefit model to help project managers. Klimpke et al. [2]  conducted a case study at five industrial firms to determine how traceability was realized in practice. They also pointed out what needed to be considered in order to adopt traceability in industry.
IX. CONCLUSION This paper presents a study to assess the effect of the presence of traceability links during software maintenance tasks. A prototype tool namely TraceLink was developed to test the hypotheses in the study. Twenty-eight subjects from industry and academia participated in the study. Results indicate a significant increase in task accuracy when traceability links were used to solve the tasks. Subjects using traceability links were 86. 06% more accurate. Using traceability links (via TraceLink) also reduced the gap between high and low ability subjects as well as between academic and industry subjects. This implies that using a traceability system provides more gains for low ability subjects making them more productive. Difficult tasks benefitted more from traceability links than the ones that were easier to solve.
Statistical analysis Results were tabulated and statistically analysed by using the IBM SPSS (version 17, SPSS Inc. Chicago, Illinois, USA). The SPSS database was uniquely designed for our patient population and the questions adapted with the help of patient notes samples before starting the study. Frequency and relative distribution were presented in tabular form for categorical variables. Comparative analysis was performed with the Chi-Square or Fisher Exact (for 2x2 tables) test for categorical variables. Before analysing continuous variables, the data sets were assessed for normality by performing the Shapiro-Wilk test. When distribution was considered to be normal, twosided Student's t-test was performed; otherwise the Mann-Whitney test was used (confidence level for all tests = 95%).
Competing interests CB is an employee of Stryker Osteosynthesis GmbH, Germany, GT is a consultant and AJB was an employee of Stryker Osteosynthesis GmbH during the data collection period. Authors' contributions AJB carried out the data collection, analysis and participated in manuscript writing. CB participated in study design performed statistical data analysis. DC evaluated the fracture reduction for the cut-out and matched groups. AJ and CE participated in data analysis and manuscript writing. AS and GT participated in study design and coordination and help to draft the manuscript. All authors read and approved the final manuscript.
Small angle X-ray scattering Data acquisition and data reduction. The synchrotron radiation X-ray scattering data were collected at the highbrilliance beamline ID02 of the ESRF in Grenoble, France,  13  operating at 12. 46 keV. The scattering intensity was measured as a function of momentum transfer vector q = 4p(sinh)/l, where l = 0. 1 nm is the radiation wavelength and 2h is the scattering angle. Two sample-to-detector distances of 1. 5 and 3 m were used to cover an angular range of 0. 064 , q , 3. 85 nm  21. Samples were measured in a polycarbonate (ENKI, KI-Beam) flow through capillary with a diameter of d = 1. 9 mm kept in a temperature-controlled holder at T = 5 uC. The twodimensional SAXS patterns were normalized to an absolute intensity scale using the calibrated detector response function, known sample-detector distance, and measured incident and transmitted beam intensities.
For monodisperse globular proteins, the Guinier approximation is valid for qR g ¡ 1. 3 and R g and I 0 were determined from the slope and y-intercept of the Guinier plot ln(dS/dV(q)) vs. q 2 using PRIMUS from the ATSAS software package. 15 For a rodlike particle, a modified Guinier approximation can be used in the q-range where dS/dV(q) 3 q 2a with a y 1 to extract the cross-sectional radius of the cylinder from the determined cross-sectional radius of gyration, with R cs ~ffiffi ffi 2 p R acs. Using the obtained radius of gyration and the crosssectional radius from the Guinier approximation, the length of a cylinder can be calculated by R 2 g ~R2 cs 2 z L 2 12 (2) Subsequently, I 0 = dS/dV(q A 0) was used to calculate the molar mass of the protein (complex), which is listed in Table  1 , according to M SAXS ~I0 N av c(Dr" u) 2 (3) with the molecular weight M SAXS in g mol  21  , the forward scattering intensity I 0 in cm  21  , concentration c in g cm 23 , Avogadro's number N av , the scattering length density difference Dr in cm 22 (r protein 2 r H 2 O , where r protein = 1.
Protein complex has unique molecular structure Given the extraordinary shape of the protein that is very distinct from any other AFP found so far, we have searched in the Protein Database (PDB) for proteins with similar shape based on four criteria: (1) high a-helicity, (2) dimeric state, (3) number of residues close to 195 as found for wfAFP 1h and (4) length and axial ratio. We have compared the experimental data with theoretical scattering profiles of 10 different crystal structures, and computed the theoretical scattering curves from the electron density profiles of the selected proteins using the known atomic coordinates deposited in the PDB by using CRYSOL. 26 The theoretical scattering curves were then compared to the experimental scattering data (see Fig. S6-7  of the ESI 3 ). Only a handful of the selected a-helix rich dimers of the protein database have a structure that is comparable yet not identical to that of wfAFP 1h.
Acknowledgements The authors acknowledge valuable suggestions by  Dr Maxim Petoukhov. The small angle X-ray experiments were performed at the high-brilliance beamline ID02 of the  European Synchrotron Radiation Facility, Grenoble, France. I. K. V. is grateful for financial support from the  Netherlands Organisation for Scientific Research (NWO -VENI  Grant:  700. 10. 406 ) and the  European Union  through the  Marie Curie Fellowship  program  FP7-PEOPLE-2011-CIG  (Contract No. 293788 ). P. L. D. holds a  Canada Research Chair in Protein Engineering  and acknowledges financial support from the  Canadian Institutes for Health Research.
The experimental set-up The two proposed routines were implemented with customized software developed by our team using the Microsoft Visual Basic Express programming package. After verifying the correct operation of these routines during simulations and laboratory tests, the initial experiments in rats were performed. For stimulation and signal acquisition, the programmable current stimulator with integrated signal analyzer PULSEGEN/ANA-16-10 (Creotech Ltd. , Poland) was used. It has 16 current-controlled outputs and eight inputs for signal acquisition. Pulse duration and current amplitude on each output channel can be independently adjusted with a precision of up to 62 ns and 8 μA, respectively. The sampling rate of input channels is 60 kHz with a resolution of 8 bits. The custom software in which the proposed routines were implemented was installed on a laptop (Dell Studio 1537).
Acknowledgment This work was supported by the  European Union  within the framework of the  European Social Fund  through the  Warsaw University of Technology Development Program. The authors would like to thank  Mr David Guiraud  for his critical review of our work, as well as  Ms Catherine Carmeni  and  Mr Surya Subianto  for correction of the manuscript. PM also would like to thank the  Center for Advanced Studies of the Warsaw University of Technology  for the support that he received during work on this project.
In summary, this brief discussion should serve to alert users of halo catalogues that there are choices that need to be made before a halo catalogue can be produced. Different code authors naturally make difference choices, as often there is no 'correct' method and more often than not the definition adopted depends upon the problem being addressed. In what follows we will discuss the range in derived properties that arises due to these different choices as well as addressing the question of whether or not the halo finders agree when applied to the same data set with a common set of assumptions.
Haloes going MAD 2010 The start-up gathering and first comparison with respect to mock and field haloes: during the last week of May 2010 we held the workshop "Haloes going MAD" in Miraflores de la Sierra close to Madrid dedicated to the issues surrounding identifying haloes in cosmological simulations. Amongst other participants 15 halo finder representatives were present. The aim of this workshop was to define (and use!) a unique set of test scenarios for verifying the credibility and reliability of such programs. We applied each and every halo finder to our newly established suite of test cases and cross-compared the results. To date most halo finders were introduced (if at all) in their respective code papers which presented their underlying principles and generally subjected them to tests within a full cosmological environment, primarily matching (sub-)halo mass functions to theoretical models and fitting functions.
The follow-up meeting "Subhaloes going Notts" then took place during the second week of May 2012 in Dovedale, probably one of the most remote locations in England. The focus of this meeting was to better understand the differences in (sub-)halo properties that emerged during the analysis of the two comparison papers  Knebe et al. (2011)  and  Onions et al. (2012). 3 Furthermore, we also took the collaboration of the 9 code representatives present at that meeting and all other participants actively interested in halo finding one step further: having at our disposal the analysis and expertise for various codes in the field we intended to address scientific questions (as opposed to academic comparisons) using the 'code scatter' as error bars on the results. To this extent we focused on the development of a common post-processing pipeline.
This 'catalogue cleaning' process is in practice a problem area as it may be difficult to implement in parallel for the largest datasets. The issue is that particles from rejected haloes may need to be re-added to either another halo or the background pool in a self-consistent way. Thus whether or not the cleaning process is carried out 'on-the-fly' as the halo catalogue is built up or as a separate post-processing step can make a difference to the finally generated catalogue. We would advocate that the final catalogue generated after any halo cleaning algorithm has been applied should be independent of the location of the halo cleaning in the analysis chain. Unfortunately this is presently not always the case.
Summary We have seen that halo finding is not as simple as passing the raw simulation data through some filter (may that be velocity or position filtering). It involves several steps starting from initially generating a putative list of halo candidates to eventually locating an edge for an object possibly defined by them. Presenting the detailed implementation of each of these steps in every single halo finder is beyond the scope of this article. We nevertheless provide in Appendix C a brief descriptions of all the codes that participated in one way or the other in the comparison project; please refer to the references therein for more details. But we would also like to highlight that there is no unique implementation: each code applies its own way of realising the necessary steps for going from the raw simulation data to the final halo catalogue.
For instance, adhering to a certain edge definition method will lead to a code that upfront collects its particles in a way tailored to suite that definition. For example, a code aiming at collecting out to the zero-velocity radius certainly collects particles differently than a code using the first shell crossing approach. But the particle collection will influence the unbinding procedure as we will have different centres and bulk velocities to start with. We will see below that this 'freedom of realisation' will lead to unavoidable scatter when recovering halo properties with different finders. Although there are of course many possible variations, the steps outlined here underlie the architecture of almost every halo finder, and each of them will introduce some scatter in the physical properties of the haloes returned by the different algorithms.
DEFINITION OF HALO PROPERTIES Even if one had a perfect, uncontaminated set of particles associated with an object of interest representing the dark matter halo of a galaxy or a galaxy cluster, or maybe a stream of tidal debris material, it would still remain unclear how to define its physical properties. The most relevant and fundamental are probably the position, mass, radius, and bulk velocity of the object. All other properties (e. g. Vmax, spin parameter, shape, velocity dispersion, concentration, etc. ) are actually derived properties that mostly require the determination of the position and bulk velocity in order to place the associated particles into the rest frame of the halo. This is an important Section even for end users of halo finders, as not every code uses the same definitions for extracting halo properties.
Therefore it only appears natural that they span the scatter seen in Fig. 1 , i. e. a non-unified post-processing of the halo catalogues in a cosmological box. 5  We find that the scatter in mass is at the 10 per cent level and actually within the limits given by the two analytical functions. This scatter is driven by a variety of sources and is due to the different choices made by the finders. In particular the finders differ on whether or not they include the mass of any substructures in the halo mass, whether or not they do unbinding and the precise definition both the outer edge and halo centre. We note that the differences in Vmax are -in the case that each code uses its own method to determine Rmax and Vmax -substantially larger than we shall see later and of order 20 -30 per cent. This level of accuracy may be perfectly acceptable for some measurements and indeed within any particular code where the assumptions employed are conserved convergence would be expected at a much higher level.
Sub-Haloes For the rest of this section we will employ a more challenging and realistic dataset to answer the question: how well could we expect to do if we force the finders to use a common set of definitions? The Aquarius simulations  (Springel et al. 2008 ) are a set of Milky Way sized haloes studied at a range of resolutions. We have processed the A-4 dataset using a wide range of substructure finders and compared the results in  Onions et al. (2012). This is a more difficult problem as now a single host halo contains several thousand subhaloes and it is the properties of these we wish to compare. Using the same ordering of panels as for Fig. 1 , we show in Fig. 2  the subhaloes' mass M200c and Vmax functions. In contrast to the field halo comparison, the mass and Vmax were calculated using a common post-processing pipeline, i.
e. only the candidate identification, particle collection, and unbinding procedure were different (cf. Section 2). For more details about this pipeline and the way to calculate M200c and Vmax we refer the reader to Section 4. 1 of  Onions et al. (2012)  where also the choice of using M500c is discussed. We find that for this more complex problem a successful implementation of unbinding is essential in order to obtain reliable number counts anywhere near the resolution threshold. Note that the two finders ADAPTAHOP and MENDIETA do not feature a (reliable) unbinding procedure: ADAPTAHOP (without any unbinding) finds far too many small objects; MENDIETA does not contain a reliable unbinding procedure and hence finds too few objects across a large range in mass. If we were to use a common unbinding scheme for both their pre-unbinding datasets their results then agree with the majority of the finders.
However, one may raise the issue that differences in the scatter can be due to either moving from field to sub-haloes or the fact that the processing of the particles has been outsourced. To shed some more light into this we also calculated the subhalo mass function (i. e. left panel of Fig. 2 ) for the values directly returned by the respective code (not shown here though): we found that the differences are minuscule and hence the scatter -at least in mass -is not driven by the implementation to calculate it. As Vmax values have not been returned by the finders themselves we are unfortunately unable to draw any conclusions about the reduction in scatter seen in the right panels of Fig. 1  and Fig. 2  due to our common postprocessing. Further, for subhaloes, which can be significantly tidally stripped, it is not guaranteed that the mass profile reaches the peak of the velocity curve.
Candidate Identification & "Excess" Objects There is one element that could make a sizeable contribution to the scatter in the mass and Vmax functions: whether a given halo is detected or not. Remember that the deviations reported in Table  4  were based upon a common set of objects, but that each finder certainly found (substantially) more objects than defined by this common set (cf. Table  3 ). For every subhalo found by a given reference code in the Aquarius A-4 data we now identify its counterpart in the particle ID lists of all other codes. This was again accomplished by examining the particle lists and maximizing the merit function C = N 2 shared /(N1N2), where N shared is the number of particles shared by two objects, and N1 and N2 are the number of particles in each object, respectively (see Appendix B for more details).
g. from SDSS 9 or the upcoming GAIA mission. The comparison of various snapshot-based stream detectors to a full tracking code revealed that basic properties of the total substructure distribution (mass, velocity dispersion, position) are recovered with a scatter of ∼20 per cent; and tidal debris with purities of ∼50 per cent -where purity is defined as the fraction of particles in debris originating from a distinct progenitor halo  (Elahi et al. , submitted). The wealth of extra information provided by identifying the tidal features associated with a subhalo and tidal debris has a number of applications. For instance, the orientation and shape of a subhalos tidal features could be incorporated into semi-analytic models of galaxy formation to determine the morphology of the satellite galaxy.
2011; Carlesi et al. 2011; Baldi 2012a ). However, along with the production simulation code the halo finding algorithm also requires adjustment in compliance with the adopted non-standard model: the calculation of many halo properties (for example virial radius, rotation curve and thus Vmax, spin, and concentration) assume general relativity (GR) and so must be modified for modified gravity (MG) simulations according to the specific model. This is obviously best left to the users and not the code providers to derive, but it is important that enough GRindependent (i. e. dynamical) parameters are included in halo catalogs to allow for calibration. This also means that results for MG simulations depend strongly on the unbinding procedure, which itself depends on environment. For example, in most MG models gravity is very different for field haloes and haloes in lowdensity environments, so the unbinding procedures in the above codes would be invalid, although, as noted in Section 4.
While all previous comparison projects mainly dealt with distribution functions of properties, we were so far unable to disentangle the relative strength of these two sources. Here (i. e. in Secs. 4. 2. 1 through 4. 2. 5) we now focused on the former by restricting the analysis to only those objects that had been found by all finders. We further utilized a common post-processing pipeline that only deals with particle ID lists as returned by each code and calculates all halo properties in the same manner to avoid contamination from different definitions. Our results are best summarized in Table  4  indicating that for the most basic properties (i. e. position, velocity, Vmax, and possibly mass) and structures that are found by all participating finders the agreement across codes is at the 1 per cent level, sufficient for the so-called era of 'Precision Cosmology'  (Smoot 2003; Primack 2005; Coles 2005; Primack 2007 ).
ACKNOWLEDGMENTS This paper was initiated at the Subhaloes going Notts workshop in Dovedale, UK, which was funded by the European Commissions Framework Programme 7, through the Marie Curie Initial Training Network Cosmo-Comp (PITN-  GA-2009-238356). It also uses data and results from the first halo finder comparison workshop "Haloes going MAD" in Madrid, Spain, which was funded by the ASTROSIM network of the European Science Foundation (Science Meeting 2910). AK is supported by the Spanish Ministerio de Ciencia e Innovación (MICINN) in Spain through the Ramón y Cajal programme as well as the grants AYA 2009-13875-C03-02, AYA2009-12792-C03-03, CSD2009-00064, CAM S2009/ESP-1496 (from the AS-TROMADRID network) and the Ministerio de Economa y Competitividad (MINECO) through grant AYA2012-31101.
He further thanks Calexico for tres avisos. HL acknowledges a fellowship from the European Commissions Framework Programme 7, through the Marie Curie Initial Training Network CosmoComp  (PITN-GA-2009-238356). YA receives financial support from project AYA2010-21887-C04-03 from the MICINN (Spain), as well as the Ramón y Cajal programme (RyC-2011-09461), now managed by the MINECO (fiercely cutting back on the Spanish scientific infrastructure). PB received support from NASA HST Theory grant HST-AR-12159. 01-A and was additionally supported by the U. S. Department of Energy under contract number DE-AC02-76SF00515. JC is supported by a contract from MICINN/MINECO (Spain) through the grant AYA2009-12792-C03-03 from the PNAyA. RDT is supported by the MICINN (Spain) through the grant AYA2009-12792-C03-03 from the PNAyA, as well as by the regional Madrid V PRICIT program through the ASTROMADRID network (CAM S2009/ESP-1496) and the "Supercomputación y e-Ciencia" Consolider-Ingenio CSD2007-0050 project.