dataesr/scientific-paragraphs-categorization huggingface dataset:
As HippoUnit already has its capability functions implemented in a way that it is able to handle models developed in NEURON, the only requirement for such models is that they should contain a HOC file that describes the morphology (including the section lists for the main dendritic types of the dendritic tree) and all the biophysical parameters of the model, without any additional simulations, GUIs or run-time modifications. Currently, such a standalone version of the models is not made available routinely in publications or on-line databases, but could be added by the creators of the models with relatively little effort. On the other hand, applying the tests of HippoUnit to models built in other languages requires the re-implementation of the capability functions that are responsible for running the simulations on the model (see Methods).
In order to save the user from this effort, it would be useful to publish neuronal models in a standard and uniform format that is simulator independent and allows general use in a variety of paradigms. This would allow an easier and more transparent process of community model development and validation, as it avoids the need of reimplementation of parts of software tools (such as validation suites), and the creation of new, (potentially) non-traced software versions. This approach is already initiated for neurons and neuronal networks by the developers of NeuroML  [69] , NineML  [70] , PyNN  [71] , Sonata  [72] , and Brian  [73]. Once a large set of models becomes available in these standardized formats, it will be straightforward to extend HippoUnit (and other similar test suites) to handle these models.
Extensibility of HippoUnit Although we were aiming to develop a test suite that is as comprehensive as possible, and that captures the most typical and basic properties of the hippocampal CA1 pyramidal cell, the list of features that can be tested by HippoUnit is far from complete. Upon availability of the appropriate quantitative experimental data, new tests addressing additional properties of the CA1 pyramidal cell could be included, for example, on the signal integration of the basal or the more distal apical dendrites, or on action potential initiation and propagation in the axon. Therefore, we implemented HippoUnit in a way that makes it possible to extend it by adding new tests. As HippoUnit is based on the SciUnit package  [19]  it inherits SciUnits's modular As HippoUnit is open-source and is shared on GitHub, it is possible for other developers, modelers or scientists to modify or extend the test suite working on their own forks of the repository.
Golding et al. , 2001 model [27] (ModelDB accession number: 64167), the Katz et al. , 2009 model [51] (ModelDB accession number: 127351), the Migliore et al. , 2011 model [52] (ModelDB accession number: 138205), the Poirazi et al. , 2003 model [6,53] (ModelDB accession number: 20212), the Bianchi et al. , 2012 model [25] (ModelDB accession number: 143719), and the Gómez González et al. , 2011 [54] model (ModelDB accession number: 144450).
steady_state_voltage). An exception is the Poirazi et al. 2003 model, where the decay time constant after the stimulus is unusually high (this feature is not included in Fig 3, but the slow decay can be seen in the example trace in Fig 2, and detailed data are available here: https://github. com/KaliLab/HippoUnit_demo). The voltage threshold for action potential generation (AP_begin_voltage) is lower than the experimental value for most of the models (that were able to generate action potentials in response to the examined current intensities), but it is higher than the experimental value for most versions of the Gómez González et al. 2011 model. For negative current steps most of the models gets more hyperpolarized (voltage_deflection) (the most extreme is the Gómez González et al. 2011 model with the n129 morphology), while the Gómez González et al.
5B). The new feature scores for the two different data sets were calculated as the difference of the model's feature value from the mean feature value of each dataset (as before), but divided by a common standard deviation value. This standardized SD value for each eFEL feature was the mean of the standard deviation values over the current steps in the patch clamp dataset (the results were qualitatively similar if we used the SD values from the sharp electrode dataset everywhere instead). Averaging the standard deviation values of the eFEL features over the current steps was required because the current step amplitudes were not the same in the two data sets, and we therefore needed to define SD values that were independent of the amplitude. To get rid of the second bias, only those eFEL features were used in the final score recalculation that are present in both observations (sharp electrode and patch clamp datasets) for at least one current step amplitude.
/github. com/KaliLab/HippoUnit_demo/tree/master/jupyter_notebooks ). The validation results discussed in this paper have also been registered in the Validation Framework, with all their related files (output figures and JSON files) linked to them. These can be accessed using the Model Validation app of the framework. The Brain Simulation Platform of the HBP contains several online 'Use Cases', which are available on the platform and help the users to try and use the various established pipelines. The Use Case called 'Hippocampus Single Cell Model Validation' can be used to apply the tests of HippoUnit to models that were built using automated parameter optimization within the HBP. The Brain Simulation Platform also hosts interactive "Live Paper" documents that refer to published papers related to the models or software tools on the Platform.
Live Papers provide links that make it possible to visualize or download results and data discussed in the respective paper, and even to run the associated simulations on the Platform. We have created a Live Paper (https://humanbrainproject. github. io/hbp-bsp-livepapers/2020/saray_et_al_2020/saray_et_al_2020. html) showing the results of the study presented in this paper in more detail. This interactive document provides links to all the output figures and data files resulting from the validation of the models from literature discussed here. This provides a more detailed insight into their behavior individually. Moreover, as part of this Live Paper a HippoUnit Use Case is also available in the form of a Jupyter Notebook, which guides the user through running the validation tests of HippoUnit on the models from literature that are already registered in the Framework, and makes it possible to reproduce the results presented here.
between the target data and the model's behavior, as quantified by the validation tests, may be due to several different reasons. First, all experimental data contain noise and may have systematic biases associated with the experimental methods employed. Sometimes the experimental protocol is not described in sufficient detail to allow its faithful reproduction in the simulations. It may also occur that a model is based on experimental data that were obtained under conditions that are substantially different from the conditions for the measurement of the validation target dataset. Using different recording techniques, such as sharp electrode or patch clamp recordings or the different circumstances of the experiments (e. g. , the strain, age, and sex of the animal, or the temperature during measurement) can heavily affect the experimental results.
In some cases the data may be explicitly provided in the text of the papers (as for the Oblique Integration and the Depolarization Block tests), therefore their JSON files are easy to make manually. Most typically, the data have to be processed to get the desired feature mean and standard deviation values and create the JSON file. In these cases it is worth writing a script that does this automatically. Some examples on 64 how this was done for the current tests of HippoUnit are available here: https://github. com/sasaray/HippoUnit_demo/tree/master/target_features/Examples_on_creatin g_JSON_files/.
Acknowledgements We thank  Judit Makara  and her group in the  Laboratory of Neuronal Signaling, Institute of Experimental Medicine, Hungary  for the patch clamp recording data used in this study. We also thank  Luca Tar , a member of our group, for her help in testing the validation tests, and in literature review, and for useful discussions. We also would like to thank  Michael Gevaert  ( Blue Brain Project ) for providing the script that finds the apical point, and that were further developed for classifying apical sections.
( steady_state_voltage). An exception is the Poirazi et al. 2003 model, where the decay time constant after the stimulus is unusually high (this feature is not included in Fig 3, but the slow decay can be seen in the example trace in Fig 2, and detailed data are available here: https://github. com/KaliLab/HippoUnit_demo). The voltage threshold for action potential generation (AP_begin_voltage) is lower than the experimental value for most of the models (that were able to generate action potentials in response to the examined current intensities), but it is higher than the experimental value for most versions of the Gómez González et al. 2011 model. For negative current steps most of the models gets more hyperpolarized (voltage_deflection) (the most extreme is the Gómez González et al. 2011 model with the n129 morphology), while the Gómez González et al.
60 discrepancy 60 between the target data and the model's behavior, as quantified by the validation tests, may be due to several different reasons. First, all experimental data contain noise and may have systematic biases associated with the experimental methods employed. Sometimes the experimental protocol is not described in sufficient detail to allow its faithful reproduction in the simulations. It may also occur that a model is based on experimental data that were obtained under conditions that are substantially different from the conditions for the measurement of the validation target dataset. Using different recording techniques, such as sharp electrode or patch clamp recordings or the different circumstances of the experiments (e. g. , the strain, age, and sex of the animal, or the temperature during measurement) can heavily affect the experimental results.
test the Poirazi et al. 2003 model using its own receptor models, we also had to implement a modified version of the synapse functions of the ModelLoader that can deal with the different (pointer-based) implementation of synaptic activation in this model. For this purpose, a child class was implemented that inherits from the ModelLoader class. This modified version is not part of the official HippoUnit version, because this older, more complicated implementation of synaptic models is not generally used anymore; however, this is a good example on how one can modify the capability methods of HippoUnit to match their own models or purposes. The code for this modified ModelLoader is available here: https://github. com/KaliLab/HippoUnit_demo/blob/master/ModelLoader_Poirazi_2003_CA1. py.
Due to this interest, CE is significantly established in industrial and in academic laboratories which represents a huge number of CE users. Regardless of the research field, every CE user have in common a large number of basic problematic. Indeed, CE geometry, instrumentation, background electrolyte (BGE) properties or mobilities determination involves a lot of fastidious calculation that CE users perform on a daily basis, such as for example, injected volume computation, viscosity and conductivity of the BGE or electroosmotic mobility (µeof) value. Software, such as CE Expert (Beckman Coulter, Brea, CA, US) introduced in 1997, were developed to obtain information on CE injection. By entering known parameters such as capillary dimensions, hydrodynamic injection pressure and injection time, several parameters including injection volume can be automatically calculated.
Nevertheless, existing software provide calculations for a limited numbers of parameters and moreover have not been updated to run on recent operating systems. However, it was noted that the interest in tools to help CE user's community is growing, and still much appreciated. For example, ECHMET (http://echmet. natur. cuni. cz/) project developed at Charles University in Prague, proposes several Freewares allowing, among other things, to model and simulate electrophoretic separation. In this project, we have developed CEToolbox application, desiring to provide a tool running on new media platforms, especially mobile platforms, and to increase the level of information in terms of separation characteristics. Within few clicks on your smartphone or your tablet working on Android, CEToolbox provides extensive CE separation information as injected volume, total capillary volume, proportion and amount of injected sample, rinsing calculation and electrical field.
Moreover, using an optimized interface, four additional tabs allow to calculate viscosity and conductivity of BGE, separation flow rate and values of µeof and effective mobilities (µeff). CEtoolbox provides CE information regardless the CE system used. CEToolbox is a Free software running on Android platform. Developed in Java and distributed under the Apache 2 license, the code is available on GitHub and deposited at Zenodo In order to be compatible with all the existing CE systems, pressure can be expressed in two different units, millibar (mbar) or pound per square inch (psi). Moreover, in order to be universal in terms of amount calculation, concentration can also be expressed in two different units, millimole per liter (mmol/L) or gram per liter (g/L). After clicking on the "Calculate" button, a window appears displaying all injection information (Figure  2 ).
Finally, CEToolbox remains a theoretical calculator and all the theoretical principles used to develop this application have been exposed in this article. In consequence, the results presented should be taken as approximations and not as absolutes. We have developed CEToolbox application, desiring to provide a useful calculator for CE users in the aim to facilitate their daily experiments in the laboratory. This application is anchored in the use of new media platform that have already integrated the life of the laboratory. Today, CEToolbox appears to be the most comprehensive specialized calculator for the CE User. Whatever CE system is used, CEToolbox provides extensive CE separation information as injection, viscosity, conductivity, flow rate and mobility properties. CEToolbox is a software running on Android platform and is available freely on Google and F-Droid application store, and also from the project web page: https://cetoolbox.
http://doi. org/10. 5281/zenodo. 4284345). The application can be downloaded freely from Google Play (https://play. google. com/store/apps/details?id=com. github. cetoolbox), F-Droid (https://fdroid. org/en/packages/com. github. cetoolbox/) or from the project web page (https://cetoolbox. github. io/). The first releases of CEToolbox have been developed using the Eclispe software (https://www. eclipse. org/) and the Android Development Tools (ADT). This environment changed from version 2 of the software, as Eclipse was replaced by Android Studio(https://developer. android. com/studio). This integrated development environment facilitates the development, the maintenance and the publication of CEToolbox. The implementation of the scientific code has been done in a dedicated Java class, which can be easily taken over by other projects.
( http://doi. org/10. 5281/zenodo. 4284345). The application can be downloaded freely from Google Play (https://play. google. com/store/apps/details?id=com. github. cetoolbox), F-Droid (https://fdroid. org/en/packages/com. github. cetoolbox/) or from the project web page (https://cetoolbox. github. io/). The first releases of CEToolbox have been developed using the Eclispe software (https://www. eclipse. org/) and the Android Development Tools (ADT). This environment changed from version 2 of the software, as Eclipse was replaced by Android Studio(https://developer. android. com/studio). This integrated development environment facilitates the development, the maintenance and the publication of CEToolbox. The implementation of the scientific code has been done in a dedicated Java class, which can be easily taken over by other projects.
Assessment of face validity and feasibility was done by the working group, using standardized questionnaires from the OMERACT Instrument Selection Workbook. Assessment of numeric performance (e. g. , missing data patterns, floor/ceiling effects, score distribution) was done in baseline data from the HOSTAS cohort, a Dutch observational cohort of hand OA patients (n = 383)  [15]. The assessment of domain match and feasibility was presented and discussed among working group members in a SIG at OMERACT 2018  [8]. Patients with hand OA (n = 48) from eight centers in different settings (primary and secondary care) in seven countries worldwide (Adelaide (Australia), Vienna (Austria), Ghent (Belgium), Leiden (The Netherlands), Oslo (Norway), A Coruña (Spain), Keele and Nottingham (United Kingdom)) were asked for their opinion on domain match and feasibility of the instruments, using a standardized questionnaire developed by OMERACT for this purpose, and translated by working group members to the local language if applicable.
For AUSCAN, data from a previous SLR of the OMERACT hand OA working group  [10]  (databases: PubMed, Embase, Web of Science, COCHRANE, CINAHL, Academic Search Premier, ScienceDirect; date of search from inception up to January 2014) and from a complementary SLR focusing on thumb base OA  [11]  (databases: PubMed, EMBASE, Web of Science, COCHRANE, CINAHL, Academic Search Premier, ScienceDirect, PEDro; date of search from inception up to November 2010) were complemented with a literature search for studies of thresholds of meaning (database: PubMed; date of search: January 2014 to November 2019). For MHQ, no SLR was available, so a literature search for all metric properties was performed (database: PubMed; date of search: inception up to October 2019). Clinical trials to extract data on clinical trial discrimination for both instruments were identified from a recent SLR for the EULAR hand OA management recommendations (databases: PubMed/ MEDLINE, Embase, Cochrane CENTRAL; up to June 2017)  [16].
Percentiles were calculated using Stata V15. 1 software. The quality of the methods used was independently reviewed by the OMERACT technical advisory group (TAG). Final rating. Evidence on each metric property from studies using good or amber methods was extracted and summarized in Summary of Measurement Properties (SOMP) tables. Each measurement property was given a final rating based on the gathered evidence according to OMERACT guidance. A green rating indicates consistently good performance from multiple studies identified as having good methods; amber indicates a noncritical limitation in the evidence, which merits a research plan. Finally, an overall rating across all the measurement properties for each instrument was proposed by the working group, evaluated by the TAG and finally brought to a broader group of the OMERACT community for final approval of our proposed level of endorsement.
In the web-based vote among the working group (n = 26; 22 clinicians, 19 researchers), domain match was accepted for AUSCAN (81% green, 15% amber) and MHQ (65% green, 31% amber), but concern was raised for FIHOA (62% amber, 12% red), mainly because voters felt the instrument includes some outdated, culturally challenging and gender-specific questions that are not acceptable in this day and age. Feasibility of MHQ was accepted (46% green, 46% amber), though important caveats are that the questionnaire is long, calculation of the scores is not straightforward, and that it should be translated to more languages. Feasibility of FIHOA was also accepted (81% green, 15% amber), the only concern being whether all translations are fully validated. Feasibility of AUSCAN was problematic (58% amber, 19% red), because the questionnaire is copyrighted and not freely available.
3  presents the SOMP table of AUSCAN (A) and MHQ (B). The combined rating of the evidence was supportive of a provisional endorsement of both MHQ subscales as core outcome measurement instrument (Fig. 3B ). The working group noted the  The color represents the good methods assessment (green, good; amber, noncritical limitation; red, unsuitable evidence). The sign represents the adequacy of the data in support of the instrument (+, positive support; §, ambivalent support; À, does not reach performance standards for metric property). For MHQ, superscript letters denote whether the evidence in that cell pertains to the overall function a or the activities of daily living b subscale. AUSCAN, Australian/Canadian Hand Osteoarthritis Index; MHQ, Michigan Hand Outcomes Questionnaire. need to re-assess clinical trial discrimination in future clinical trials on their research agenda.
Besides, their objective was to identify and appraise instruments for a specific application (general population surveys in primary care setting), while our work expands to instruments suitable for studies in all settings. Moreover, their search was performed until 2002, they used the old OMERACT framework (Filter 1. 0), did not critically appraise the quality of the studies providing data on the measurement properties of instruments, and no voting in the OMERACT community was done or level of endorsement presented. A recent narrative review by van de Stadt et al. provides an overview of all available instruments to measure hand function, including an overview of evidence for their measurement properties, without focusing on hand osteoarthritis specifically  [3]. Physical function is the first core domain from the OMERACT hand OA core outcome set for which core outcome measurement instruments have been endorsed.
Acknowledgments We would like to thank the patient research partners for their valuable contributions. We thank the other OMERACT hand OA working group members who completed the survey for domain match and feasibility ( R. Altman ,  M. Boers ,  M. Englund ,  H. Jonsson ,  T. Karjalainen ,  I. Kjeken ,  M. Kortekaas ,  M. Marshall ,  B. F. Leeb ,  Y-Y. Leung ,  P. Steen Pettersen ,  R. Ramonda ,  Z. Szekanecz ,  A-L Tan ,  P. Taylor ,  T. Woodworth ). We are grateful for the OMERACT hand OA working group members who translated the patient research partner questionnaire to the local language.
Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. KD is part funded by the  National Institute for Health Research (NIHR) Applied Research center (ARC) West Midlands. KD was also part funded by an  NIHR   Knowledge mobilisation Research Fellowship  ( KMRF-2014-03-002 ) and is an  NIHR Senior Investigator. The views expressed are those of the author(s) and not necessarily those of the  NIHR  or the  Department of Health and Social Care. PGC is supported in part by the  UK National Institute for Health Research (NIHR) Leeds Biomedical Research center. The views expressed are those of the author(s) and not necessarily those of the  NHS , the  NIHR , or the  Department of Health.