dataesr/scientific-paragraphs-categorization huggingface dataset:
Design of a modern simulation environment for synchrotron optics After the refactoring of SHADOW and the release of SHADOW3 in 2011, it was required to renew the old graphics tools and interfaces, based on XOP  (Sanchez del Rı ´o & Dejus, 2011). Our experience showed that the efficiency of SHADOW is supported by a well designed and user-friendly user interface. For about 20 years, more than 90% of the SHADOW calculations used the ShadowVUI interface available in XOP. Evolution of the hardware platforms, modernization of the software tools, access to the codes of a large number of young people and popularization of the open source software for scientific applications drove us to design a completely new graphical user interface for SHADOW3. The interaction with a large community of users permitted us to identify a list of requirements: (i) The interface should be easy and intuitive to use.
It is a very concise language with numerous libraries available. (ii) A high-level interface based on workflows. We selected Orange  (Orange, 2016) , which was customized into OASYS, our development platform. (iii) SHADOW3 was selected as the main calculation engine, because it is the most-used ray-tracing code in the community, and it has a Python API. SHADOW3 is used only via the simplified API interface, meaning that no system calls are performed as done in previous graphical interfaces for SHADOW which communicate only via files. All previous calculations required by SHADOW (e. g. the pre-processors for preparing the physical properties of materials) have been completely rewritten in Python, using the xraylib library  (Schoonjans et al. , 2011). Similarly, all the new post-processors are fully implemented in Python.
(iv) Graphics (one-dimensional, two-dimensional, histograms, etc. ) are presently using PyMca  (Soleet al. , 2007)  graphics. They will be soon replaced by silx  (Silx, 2016). (v) Widgets use the Qt library, via the pyqt binding. This is also the standard in Orange and silx. A block diagram of the modules that constitute ShadowOui is shown in Fig. 1.
Main use of ShadowOui From the user point of view, the ShadowOui package is presented with several parts (see Fig. 2 ): (i) Canvas: this is the space for working, to be populated with different elements (called widgets). (ii) ToolBox: the menu with the items that are used to populate the canvas with elements from different categories: sources, optical elements and accessory tools, etc. (iii) Connectors: the wires that connect widgets building the schema. The information (the beam, containing the rays and their history) travels through these wires. (iv) Widgets: the active elements. Double-clicking in a widget opens a window, containing the parameters to be defined and customized. An action 'run' must be started from source widgets. The signal (X-ray beam) is propagated automatically through all connected elements.
The two-dimensional mesh of the surface error profile to be added to the surface of the optical element, with the same format of the Waviness algorithm, can be generated using different algorithms: fractal or Gaussian distribution of errors, and also from a user-defined error profile from an external file, thus allowing the use of experimental or customized error profiles. Finally, the user can generate the two-dimensional mesh of the error profile using the DABAM Height Profile widget (see Fig. 4b ), accessing in an automatic and transparent way the DABAM online metrology database (Sanchez del  Rio et al. , 2016) , containing a collection of experimental error profiles, coming from a worldwide group of metrology laboratories. With the DABAM widget the user can analyse the behaviour of a beamline by simulating mirrors with experimental error profiles contained in the database, querying the database with geometrical parameters and manipulating the chosen profile in order to properly fit the simulated optical element, both in terms of dimension and in terms of final slope errors.
This may help not only to evaluate, tune and optimize the beamline parameters but also to compute instrumental functions that can be used in the data analysis. It can be certainly applied not only in diffraction but also in many X-ray techniques such as spectroscopy and imaging. The availability of our code in open source makes possible for interested users and developers to add their own models for photon-sample interaction and contribute with new widgets to the OASYS-ShadowOui project.
Soft X-ray beamline As an example of a soft X-ray beamline, we simulate UARPES (angle-resolved photoelectron spectroscopy) at the Polish synchrotron Solaris (Szamota-Leandersson, 2016). The parameters for the simulations are reported in Table  1 , and the schematic setup of the beamline is represented in Fig. 8 , together with the ShadowOui schema. For this example, experimental metrological data of all the four mirrors are available. Three complete error profiles have been submitted to the DABAM database (profiles numbers 27-29) and used in the simulation using the DABAM widget, while for the last a simulated profile with 1. 5 mrad slope error is created using the Height Error Profile widget. In Fig. 9  it is possible to see an example of one of the used DABAM heights profiles and the bidimensional plot of the intensity versus X-Z coordinates (spot) of the rays in the image plane, given by the Plot XY widget and corresponding to the sample position.
Summary and future work We have presented a new graphical application ShadowOui that allows optical systems to be simulated using the SHADOW engine. This tool has been designed to help the user to make simulations in a very intuitive and simple way. The power of SHADOW is complemented and extended with other tools that make it possible, for instance, to quickly integrate slope errors in mirrors or estimate the effect of coherent diffraction in the simulations. The ShadowOui tool has been designed with the idea of being combined with other packages, for example, for quick calculations of characteristics of optical elements (like XOP) or for wave optics simulations. These tools will be developed in the near future, and integrated into the main OASYS platform. OASYS is now using ShadowOui as its only package (add-on) but accepts other packages that will be presented to the user in the same environment, allowing simulating beamlines with different tools without multiplying the beamline definitions.
Acknowledgements We warmly thank  Janez Dems  ˇar and all the  Orange team at the University of Ljubljana  for their support and help to customize Orange for OASYS. We thank all the comments and suggestions from  Edoardo Busetto ,  Anna Bianco ,  Ruben Reininger  and  Xianbo Shi. We also thank all our beta testers and, in particular,  Ferenc Borodnics ,  Giovanni Birarda ,  Giorgio Bais ,  Nicola Demitri  and  Yajung Tong.
HomeBank is introduced here. It is a public, permanent, extensible, online database of daylong audio recorded in naturalistic environments. HomeBank serves two primary purposes. First, it is a repository for raw audio and associated files: one database requires special permissions, and another redacted database allows unrestricted public access. Associated files include metadata such as participant demographics and clinical diagnostics, automated annotations, and humangenerated transcriptions and annotations. Many recordings use the child-perspective LENA recorders (LENA Research Foundation, Boulder, Colorado, United States), but various recordings and metadata can be accommodated. The HomeBank database can have both vetted and unvetted recordings, with different levels of accessibility. Additionally, HomeBank is an open repository for processing and analysis tools for HomeBank or similar data sets.
However, this approach has not been entirely successful in natural (i. e. , ecologically valid) settings with highly variable vocalizations, overlapping conversations, and various environmental sounds. Thus postprocessing currently focuses on a simpler, though still challenging, automatic segmentation and labeling task: that of breaking up the continuous acoustic signal into segments that are labeled by the primary sound source. This can be done using custom-written scripts, but many LENA users opt to employ the LENA software's automatic labeling algorithms. The LENA algorithms return an audio recording broken down into segments that are assigned a segment label. The labels are organized into around a dozen higher-level categories including talker identity (child wearing the recorder, any other child, adult female, adult male, and human speech overlapped with other speech or nonspeech noise), other sound sources (TV or other electronic sounds, noise), and silence.
g. , speaking, babble, singing) versus non-speech-related child vocalizations (e. g. , crying, laughing, burping), and whether there are sequences of segments where the key child and an adult alternate (which can be automatically labeled as "conversational turns"). Finally, the LENA system in particular draws from a standardized database to provide even more information by comparing the acoustic features of the child's speech against norms from the Natural Language Study. 39 This allows the LENA software to provide users with an automatic vocalization assessment that aims to provide information about the child's developmental level with regard to speech production ability. 40 The automatic vocalization assessment necessarily relies on normed data, but all other data relies on standard speech technology methods.
2] [43]  It uses the open source Sphinx software (CMU Sphinx, Carnegie Melon University, Pittsburgh, PA) as inputs in estimating number of adult words and in producing the child vocalization assessment. 39, 44, 45 7] [48]  Several of these researchers have also created practical software tools ranging from database and file management to algorithms for acoustic analyses. The tools are undoubtedly useful to the researchers and their teams, but there was, prior to HomeBank, no central repository that would benefit other researchers or those interested in becoming familiar with the technology. A comprehensive database of external software tools would greatly benefit the research community by reducing the cost of entry and increasing the accessibility of and proficiency with the data. This would especially benefit students and those researchers desiring to take advantage of the LENA system but not currently active.
As a result, most of the collected data-estimated to be in the tens of thousands of daylong audio recordings-remains sequestered in individual laboratories. Indeed, some researchers may be required (e. g. , by their institutional review board [IRB]) to delete the underlying audio data, not having a system at their disposal to properly vet, store, or secure sensitive recordings. The development of a system for sharing these rich data collections offers a benefit to the fields of child language acquisition and automatic speech processing. Such a database supports the sharing and development of resources for basic research as well as for educational and clinical work. Facilitating the development of a system for sharing this valuable data as well as improvements on and extensions to the existing analysis tools are the key motivations for the HomeBank project.
INTEGRATION OF HOMEBANK INTO TALKBANK The system described in this report, HomeBank (available at homebank. talkbank. org/), was designed to be a public repository for daylong family audio recordings, associated files and processing output, and software or algorithm tools for processing data. HomeBank is integrated into the existing framework of the Talk-Bank databases (which include, for example, the CHILDES database) and tools entailed there. HomeBank thus leverages the massive data and advantages of automatic processing described previously with an existing infrastructure and long history of success of the TalkBank project. Before describing the HomeBank project in detail, CHILDES and TalkBank will be briefly introduced. CHILDES is a Web-based system for sharing and analyzing child language transcript data.
53 he database at childes. talkbank. org includes 50 million words of transcript data, much of it linked on the sentence level to digitized video or audio recordings that can be played back directly over the Web. Over 95% of the data in CHILDES are publicly available for downloading and analysis without a password. CHILDES is one component of the larger TalkBank system (talkbank. org) that includes additional databases for the study of aphasia, traumatic brain injury, second language acquisition, dementia, conversation analysis, meetings, and other language areas. 55 ILDES began in 1984 with support from the MacArthur Foundation and has received continuous support from the National Institutes of Health since 1987 and from the National Science Foundation between 1999 and 2004 and between 2015 and 2019.
HOMEBANK HomeBank (homebank. talkbank. org) was conceived to offer researchers greater access to raw data and tools associated with the rapidly growing amount of daylong audio files being collected by a wide range of researchers. Home-Bank is an effort to address the issue of providing a centralized database or repository for daylong audio files, the associated data and metadata with those files, and software tools for researchers. We expect that the database will be useful to a wide range of users, including those interested in language and child development in the social sciences and those interested in automatic speech processing. HomeBank consists of a recording database and a code repository. The recording database consists of vetted and unvetted day-long audio recordings. In the vetted section, original daylong recordings and their associated metadata were vetted by experienced, trained listeners to ensure that recordings contained no private or personally identifying information.
For example, if a parent recited her name and address on a recording, that audio portion is redacted and inaccessible to the user. The vetted database is unrestricted and open to the public for download, playback, or analysis. However, because the vetted database requires a trained person to listen to the audio in its entirety, and requires that individuals on the recording agree to a more open distribution, this is a relatively modest-sized database. The other part of the recording database is a restricted database requiring special permissions to access. This part of the database contains unvetted audio recordings and their associated metadata, and recordings for which public distribution has not been agreed to by the participants. Because much of the audio has not been vetted and cleaned by trained listeners, this can be a much larger database than the unvetted database.
However, because personally identifying details may be contained on the recordings, several tight user restrictions have been implemented to safeguard the participant families. These recordings are stored in a password-protected computer space only available to registered HomeBank members who have agreed to confidentiality in writing and have passed recognized ethical training on dealing with human data. HomeBank is intended to be accessible and open to the research community while balancing the collective obligation to treating participants ethically. Additional details can be viewed on the HomeBank Web site or by direct email inquiry (contact information at homeBank. talkbank. org). Metadata are an important aspect of the database that greatly benefits from the extant tools developed from the TalkBank project.
In addition to the source audio files and results of the LENA speech processing algorithms, data for each recording generally includes child age, child sex, information about whether the child is typically developing or is a member of a specific clinical population (e. g. , having hearing impairment, language delay, autism spectrum disorder, and so on), education level of the child's primary caregivers, country the child was recorded in, dominant ambient language, scores on language or other developmental tests or questionnaires, and comments on the recording. The metadata protocol is designed to be flexible and extensible within the database. For example, certain characteristics of children with autism spectrum disorder may warrant several data fields unique to that population, or children with hearing loss may have audiological data such as audiograms or details of the hearing aid itself.
Thus, the transcriptions are an especially valuable aspect of the database specifically for an important expected user base. In addition, human transcriptions are the gold standard by which machine algorithms are both evaluated and trained, and so are essential to the development of new and improved speech recognition technologies in this domain. We expect that because these gold standard, human-generated transcriptions exist, researchers interested in automatic speech processing will be attracted to the database. The second part of HomeBank is an open source code repository, HomeBankCode, hosted at GitHub (github. com/HomeBank-Code). GitHub provides free storage and version history. It also has the advantage of being extremely widely used across academia, industry, and hobbyists, making it likely that many potential contributors are already familiar with how to use Git and GitHub and increasing the chances that other users will discover the resource.
The repository is publicly available and adheres to the open source philosophy. Code, pseudocode, and stand-alone algorithms are posted by users, to be modified, improved, changed, or used as the basis for new code by other users. For example, users have developed tools to process LENA daylong audio files, including tools for applying acoustic analyses in batch, performing data cleaning, using transcription functions within CLAN, and deidentifying Interpreted Time Segments (ITS) files. These scripts have been shared through HomeBank for public use and extension. This is the primary source of user-created, postprocessing tools for use on the daylong recordings. Additionally, the HomeBank website also maintains contact information, an overview of the project, links to related resources on the Web, and samples of related documents such as help with construction of IRB and consent forms for prospective researchers and how to organize metadata.
Anyone with access to the World Wide Web has unrestricted access to the vetted public database and HomeBankCode repository via the homebank. talkbank. org Web site. Before users can gain access to the larger protected database, they provide written evidence of ethical training (e. g. , Collaborative Institutional Training Initiative (CITI) certificate), and oral and written agreement of data use and confidentiality, obtained through a HomeBank staff person. Upon registration of membership, the member and supervisees, such as students or laboratory staff registered as working under that member, gain access to the database containing more restricted and unvetted files. Finally, the HomeBank database is committed to participant respect and beneficence. 59 We have constructed consent form templates that allow parents to opt in to sharing their data in HomeBank.
Permission to post the recording is requested at the time of each recording, or some researchers may opt to procure retroactive participant consent to post extant recordings in support of HomeBank. For data to be contributed to HomeBank, consent forms should ask parents to indicate that they approve that their child's daylong audio recordings and metadata are included in a shared database and that they are comfortable with the public being able to listen to their recordings. Participants could alternatively indicate that their data are to be made available only in the restricted data set, for download by more thoroughly vetted researchers only. The consent forms can also provide contact information should they decide at a later date that they wish to revoke access to their data.
WHAT HOMEBANK BRINGS TO THE COMMUNITY HomeBank provides contributors (of recordings, other data, or code) with a way of augmenting their impact in the research community. Following the model of the CHILDES database, all users of data from either the public or the restricted data sets will be provided with the appropriate citations and be required to cite them in any publications that utilize the data sets. CHILDES has placed consistent emphasis on the importance of citing original sources, with excellent results. CHILDES also maintains methods for citing corpora as publications through assignment of ISBN codes and Handle System identifiers; this policy is extended to HomeBank data as well. Code in the HomeBankCode GitHub repositories is licensed according to the contributor's preferences; for example, contributions to date use the GNU General Public License, version 2, requiring that any derivative work also make source code freely and publicly available.
The data in HomeBank provide an important resource to child development researchers. In contrast to in-laboratory experiments or short home recordings, daylong recordings of children and their caregivers in their natural environments provide more holistic information about child development. They provide a window into all types of children's daily experiences, as well as the ability to better estimate the frequency of different types of events and experiences over the course of a child's day. Many researchers, including student researchers, may have questions that can be addressed by human or automated coding of these daylong naturalistic samples, but lack the time, training, or funds to obtain a large number of original LENA recordings. Even if they do have the resources, those resources may not best be spent on replicating data that has already been collected and archived in the HomeBank database.
Furthermore, a very large database may allow researchers to ask questions of the data that would not otherwise be possible in a smaller database. For instance, users may train listeners working under their supervision to find segments of the recording in which events of a particular type (e. g. , singing or book reading) are present and manually transcribe those events, or perform acoustic analyses on selected segments or in certain contexts. Alternatively, users may apply their own automated data analysis methods to a large number of the recordings without necessarily listening to the recordings except to establish reliability. Another advantage of daylong recording is that, assuming there is a way to efficiently process the large quantity of data, it is possible to accumulate data on relatively rare events.
In all of these cases, the presence of a well-populated Home-Bank database ensures that hard-earned day-long recording data, generously contributed by parents and children for the purposes of increasing our understanding of child development, is put to maximum use. The raw WAV files included in HomeBank also provide an ample, large data set for input to supervised or unsupervised machine learning systems. For example, unsupervised deep learning neural network algorithms are increasingly being recognized as powerful methods for extracting acoustic features for automatic speech analysis. 60 Such approaches greatly benefit from more and more naturalistic data, which is exactly what HomeBank can provide.
FUTURE DIRECTIONS FOR HOMEBANK HomeBank was launched in 2015 with support from the National Science Foundation through 2019. TalkBank has agreed to partner with and host HomeBank in perpetuity. The key developments required to maximize HomeBank include increasing the size and variety of the database and making researchers aware of the tool. There are several developments that will make HomeBank even more useful. First, we may add a third section in the recordings database for daylong recordings that were collected under maximally restrictive sharing conditions, whereby no users outside of the initial laboratory where collection occurred are allowed to listen to them. This might happen, for instance, if they involve families who are particularly concerned with privacy or populations at risk.