dataesr/scientific-paragraphs-categorization huggingface dataset:
On the ID21 microscopy beamline At ID21, two core interfaces are provided to allow data acquisition and beamline control and alignment. The first, shown in Fig. 2 , uses the VLM component detailed above to allow the user to visualize the sample in real time via a live video stream (frames provided from a LIMA camera, interfaced via video-streamer-mpeg, see Section 4. 6), to navigate around, mark regions and collect 2D mXRF maps. To the right is the list of currently defined ROIs (in purple) and POIs (in turquoise), and below the data collections conducted and resulting maps on each of these regions. Further below is a general hardware monitoring section to change settings such as the zoom level and move to different sample stage locations. Once a region or point is selected a new data collection can be executed against this object.
By clicking the 'New' button on the Data Collections panel, daiquiri will allow the user to select what action to execute based on the available actors defined on the beamline. For example 2D mXRF maps and 2D multispectral mXRF maps can be collected against ROIs, whereas mXAS can be executed against POIs. An example of this dialogue is shown in Fig. 3  -this is generated automatically from the actor's validation schema. These actors are committed to the local beamline repository daiquiri-local, so can be readily modified and added by beamline staff as experiments evolve. These actions can be placed into daiquiri's queue for sequential execution, so for example a series of mXAS spectra can be collected overnight. Daiquiri has been available to users since the EBS restart (September 2020), and first-time synchrotron users were autonomous in less than an hour.
computer programs 4. 5. Containerization The entire project has been containerized using docker. The container includes a test BLISS session, LIMA simulator, Tango dummy device, daiquiri, and a production-ready minified version of daiquiri-ui. The docker container is built nightly and tested using dgoss. This allows daiquiri to be easily started for rapid development, demonstration and evaluation. Two images are available: the primary daiquiri container and a supporting prepopulated database daiquiri-testdb.
Supplementary packages In addition to daiquiri and daiquiri-ui, a number of additional packages are available to help stream video, create synoptic schematics and synchronize user office information: synoptic svg : Allows the generation of beamline schematic synoptics from a simple YAML configuration file. video-streamer-mpeg : Takes raw frames from a camera and streams them over websockets using MPEG encoding to provide low-latency real-time video with reasonable compression. The package currently supports LIMA cameras but the frame grabber is implemented via an interface and so is straightforward to extend to other control systems. The package internally uses FFmpeg (FFmpeg, 2000) and can  Acquisition interface on ID21, courtesy of H. Moreira. mXRF maps are superimposed on the visible-light image of the sample.
Third-party integration Internally daiquiri makes use of the some of the ISPyB database tables to handle its metadata; this means that if a beamline is making use of daiquiri it can also make use of ISPyB interfaces such as SynchWeb  (Fisher et al. , 2015)  to manage access rights, monitor beamlines in real time, evaluate beam time usage and review collected data. This also means that if ISPyB is used to barcode and ship samples to a facility, daiquiri can access the sample information and use this metadata during data collection. For example, chemical information about a sample could help to determine data collection parameters and guide data processing. Then ISPyB can be used to ship samples back to users, giving full traceability in a single Laboratory Information Management System (LIMS).
Acknowledgements We would like to thank the  ID21  staff, especially  Hiram Castillo  and  Murielle Salome , as well as all of the  BCU  staff members for internal testing and valuable feedback during the development process. We thank  Hugo Moreira ,  University of Montpellier , and his colleagues for testing Daiquiri as the first academic users at ID21 and for permission to use Fig. 1.
, 2018; Luking et al. , 2016; Stewart & May 2016). While previous ☆ All data is available upon request from the authors. This research was funded by the NIMH Intramural Research Program. This protocol, NCT00397111, was approved by the NIH Institutional Review Board. All participants provided written informed consent before participating in this study. The authors have no conflicts of interest. experiments suggest that oscillatory power differences measured in EEG may originate from the basal ganglia, the exact sources contributing to altered reward processing remain largely unknown  (Glazer et al. , 2018; Keren et al. , 2018). To fully understand the neural characteristics of these reward networks, it is important to simultaneously measure where and when activity in basal ganglia and cortical areas occurs throughout reward anticipation and processing.
Participants Twenty-five healthy volunteers with no personal or family history of psychiatric disorders (first-degree) participated in this study. Individuals completed diagnostic assessment via the Structured Clinical Interview for DSM-V-TR Disorders (SCID-NP) and the Family Interview for Genetics Studies (FIGS). Participants were enrolled in clinical trial NCT00397111 and provided written informed consent. This study included 21 females and 4 males with a mean age of 32 (SD = 9. 0). The age of inclusion was 18-65 years old. Participants additionally had no serious medical illnesses, were not pregnant or nursing, and had no MRI contraindications.
Gambling task The task was written with PsychoPy software (https://www. psych opy. org). Participants viewed stimuli while sitting upright in the MEG scanner. They received instructions that they would have the opportunity to gamble in each trial for either 5 or 25 cents (Fig. 1 ). The task consisted of 180 trials, split into 6 blocks of 30 trials each. At the beginning of each trial, participants saw two cards, one labeled 5 and one labeled 25. For each trial, there was a 50% chance of the 5 or 25 appearing on the left or right side. Participants were instructed to choose whether to "bet" 5 or 25 cents by left or right button press. After the cards were displayed for 2 s, the chosen card turned green (Win), red (Loss), or yellow (Boost) for 1 s. In expected reward trials, the participants won or lost the amount of money that they bet on.
MEG and MRI data acquisition and preprocessing MEG data were collected on a 275-channel CTF MEG system (CTF systems, Inc. , Coquitlam, British Columbia, Canada) located in a magnetically shielded room. Fiducial coils were placed on participants' preauricular points and nasion to localize the position of the head inside the MEG system at the beginning and end of every recording. Data were digitally sampled at 1200 Hz after application of a quarter Nyquist lowpass filter at 300 Hz. Participants with excessive artifacts in their data or head movement exceeding. 5 cm were excluded from the analysis. Line noise was removed from the data using a 3 Hz notch filter at 60 Hz and higher harmonics of 120, 180, and 240 Hz. Visual inspection in Data-Editor (CTF systems, Inc. , Coquitlam, British Columbia, Canada) was used to mark eye blink, muscle, heartbeat and other common artifacts, and trials contained artifacts were removed from subsequent analysis.
Additionally, remaining artifacts were reduced with SAM beamforming as these methods are designed to attenuate signal from outside the voxel  (Sekihara & Nagarajan, 2008). T1 weighted MRI images were acquired on a 3-T scanner (General Electric, Milwaukee, WI). Anatomical MRI images were co-registered with MEG data fiducial markers using Analysis of Functional Neuro-Images (AFNI) software  (Cox, 1996). The MRI was used to generate a multisphere head model for source estimation  (Huang et al. , 1999). Data was transformed into common (MNI) space in AFNI and retained to apply to beamformed MEG images. Data preprocessing and analysis were conducted using the computational resources of the NIH HPC Biowulf cluster (http://hpc. nih. gov).
MEG data analysis MEG data were localized to anatomical space using synthetic aperture magnetometry (SAM) beamforming on a 5 mm grid  (Vrba and Robinson, 2001). SAM was used to estimate source power within the beta (15-30 Hz), gamma (30-60 Hz), and high gamma (60-150 Hz) frequency bands. Activity was analyzed during the 1 s reward feedback period in three overlapping time windows from 0 to 0. 5s, 0. 25-0. 75s, and 0. 5-1s. Power was estimated separately for each window and normalized by noise estimates based on signal covariance. The covariance matrix was summed across both conditions in each contrast, then inverted, to generate the beamformer. No regularization was used. To analyze power differences during the unexpected reward condition with the smallest reward and greatest loss conditions, contrasts were calculated for each participant as the log power ratio between pairs of conditions in the same time window.
Activity in the Boost condition was separately contrasted with the Win5 and Lose25 conditions. To assess neural differences dependent on magnitude of reward valence (win or loss) the Lose5 condition was contrasted with activity in the Lose25 condition, and the Win25 condition was contrasted with the Win5 condition. Comparisons between the Boost condition and both Win25 and the Lose25 conditions were not performed as analyses were limited to contrasts with the largest differences in reward magnitude. Results for each subject were transformed to common (MNI) space. We performed whole-brain group one-sample t-tests for each contrast using the AFNI program 3dttest++ to assess overall differences between reward trial types. We report results significant at q < 0. 05, where q is the p-value corrected for false discovery rate (FDR).
A growing number of papers are published in the area of superconducting materials science. However, novel text and data mining (TDM) processes are still needed to efficiently access and exploit this accumulated knowledge, paving the way towards data-driven materials design. Herein, we present SuperMat (Superconductor Materials), an annotated corpus of linked data derived from scientific publications on superconductors, which comprises 142 articles, 16052 entities, and 1398 links that are characterised into six categories: the names, classes, and properties of materials; links to their respective superconducting critical temperature (Tc); and parametric conditions such as applied pressure or measurement methods. The construction of SuperMat resulted from a fruitful collaboration between computer scientists and material scientists, and its high quality is ensured through validation by domain experts.
In chemistry, it was used for the disambiguation of chemical compounds names, synthesis extraction, and retrieval  [10]. In both domains, the application of TDM was based on manually curated datasets (corpora) that functioned as infrastructures. Examples are the BioCreative IV CHEMDNER corpus  [11]  in chemistry, and Genia  [12]  and GENETAG  [13, 14]  in biology. Such datasets are crucial for developing, training, and evaluating TDM systems. In comparison, such resources in the materials science domain are rather limited. Reported cases include NaDev  [15]  on nanocrystal devices research, a corpus for extracting synthesis recipes  [16] , and ChemDataExtractor  [17]  which focuses only on chemical entities. In the superconductors domain, we could identify MagDb  [18]  focusing on magnetic materials with limited information categories.
Another project is SC-CoMIcs  [19]. SuperMat is different from SC-CoMIcs based on the following reasons: (a) it provides full papers instead of abstracts which contain more detailed information about the research on superconducting materials, and (b) it contains linked entities. To address this shortage of infrastructure, experimental data is extracted manually  [20] , or ab-initio calculations are used  [21]  but they might not accurately describe the real system. Several challenges still hinder the data-driven exploration of materials (also called Materials Informatics (MI)), namely: the lack of data standard, infant stage of the data-driven culture, a wide variety of conflicting stakeholders, and missing incentives for researchers to contribute to large collaborative initiatives  [22]. To bridge these gaps, it is necessary to create infrastructural resources to support TDM processes in materials science through the automatic construction of databases for materials and their properties.
Such application can minimise the need for humans to read the new papers and extract the key information therein. Equally importantly, it enables scientists to focus and leverage computing power and human resources to find deeper relationships between superficially unrelated information. Other applications include providing semantically enriched search engines that accept fine-grain queries  [23]  to reduce the time needed to access specific information. These processes cannot be established without essential resources such as dictionaries, lexicons, and datasets. Research on superconducting materials has been growing rapidly towards both fundamental science as well as practical applications. Superconductors display many intriguing phenomena including zero-resistivity, the ability to host a high magnetic field, quantisation of the magnetic flux, and vortex pinning.
Current applications of superconductors include medical instruments, high-speed trains, quantum computers, and the Linear Hadron Collider (LHC)  [24] [25] [26]. However, discovering a new superconductor is a challenging task  [27]. For example, in a previous work  [28]  out of ∼1000 studied materials, only 3% were found to be superconducting. The National Institute for Materials Science (NIMS) in Japan has been manually constructing databases to support material research, and SuperCon (http://supercon. nims. go. jp) is a manually curated data source for the superconductor domain. These databases would help researchers design new superconducting materials with a higher superconducting critical temperature (T c ) (ideally up to room temperature)  [29, 30]. However, the current resources are very limited and not dynamic enough to incorporate the information from new publications in a timely manner.
In this paper, we present SuperMat (Superconductors Materials), an annotated linked corpus for superconducting material information. This dataset contains 142 documents with 16052 (7166 unique) entities, and 1398 links that can serve as an infrastructural data for TDM processes in the domain of superconducting materials. We also describe the construction guidelines for SuperMat, in the hope of supporting researchers to systematically create annotated data. Furthermore, the unique feature of links between entities in SuperMat will allow the development of more precise methodologies to associate a particular material with its properties.
Content acquisition SuperMat originates from PDF documents of scientific articles related to superconductor research. The PDF format is the most widely used format for scientific publications  [31]. The original documents were collected from the following sources: (a) the Open Access (OA) version of peer-reviewed articles referenced in the SuperCon database records; (b) articles provided by domain experts containing suitable items and potential links of material names, T c values, measurement methods, and pressures; (c) articles from "condensed matter" category of arXiv (https://arxiv. org/archive/cond-mat) selected using the search terms of "superconductor", "critical temperature", and "superconductivity". Pre-print versions of peer-reviewed articles were obtained using a lookup service for bibliographic data called biblio-glutton (https://github.
com/kermitt2/ biblio-glutton) that aggregates data from various sources: the Crossref (https: //www. crossref. org/) bibliographic database, the unPaywall (http://unpaywall. org) service, the PubMed Central repository (https://pubmed. ncbi. nlm. nih. gov/), and mappings to other databases. We queried biblio-glutton using the bibliographic data of each article referenced in Supercon; subsequently, we downloaded the pre-print article associated with the retrieved record, if available. Although the published version may be different from the pre-print version of a document, the differences measured by comparing pre-print and peer-reviewed articles in biology  [32]  measured objective differences to be around 5%.
Annotation guidelines Annotation guidelines include the principles and the rules that describe what constitutes as desired information for the SuperMat dataset and how to annotate it. They include detailed description of the specific rules that have been defined for each type of information to be annotated, with one or more definitions and examples illustrating what to annotate in different cases, exceptions, and references. We used an online system to track the discussions and decisions when a question or a comment was raised, and provided a link to such issues in the respective description or example. In addition, the guidelines include linking rules that provide information on how to correctly connect the entities in a relationship. The guidelines were built using a dynamic markup language (called RestructuredText) and stored in a git (https://git-scm.
Web-based collaborative annotation tool: INCEpTION The annotation tool is the platform used for creating, correcting and linking annotations. After evaluating several tools, we selected INCEpTION  [35, 36] , a web-based multi-user platform for machine-assisted rapid dataset annotation construction. IN-CEpTION provides supportive functionalities that include: • Multi-layer annotation sheets allow different annotation schemas over the same documents, • Two annotation steps: annotation consists of manually correcting pre-imported documents, while curation allows another user to validate the annotations (Figure  5 ). • On-the-fly automatic suggestions based on active learning and string matching (Figure  5 ), • Bulk annotation corrections, and • Being open-source (Apache 2. 0 license), and under active development at the time of this paper (https://inception-project.
Automatic corpus analysis Automatic corpus analysis is a set of scripts designed to run after the validation step. These scripts automatically find inconsistencies in the links and entities, while extracting the statistics of the corpus. We calculated the inconsistencies by examining every annotated entity and computing the frequency of the same text being annotated with different labels. The script outputs a summary table by visualising each annotation value, as well as their labels and frequencies. We visually inspected this table, because the reported inconsistencies can be either obvious mistakes (Table  3 ) or arise from ambiguities (Table  2 ); therefore their context should be verified. Although the links are conceptually non-directed, we have defined a practical convention to maintain their consistency.
Data transformation There are two processes of data transformation (Figure  3 ): (a) from the source document (PDF) to the dataset format representation (XML-based), and (b) from the dataset format representation to the annotation tool exchange formats (https://inception-project. github. io/releases/0. 16. 1/docs/ user-guide. html#sect_formats) and vice-versa. • PDF to XML-based: This step converts the PDF source document to the dataset format representation in XML following the Text Encoding Initiative (TEI, https://tei-c. org/) format guidelines. Such transformation is performed by leveraging the functionalities provided by GROBID (https://github. com/ kermitt2/grobid). We developed a customised process for collecting a subset of information from the source PDF document. The process extracts the title, keywords, and abstract from the header; and paragraphs, sections.
and figure and table captions from the body. All the callouts to references, tables, and figures are ignored. The resulting structured document is then encoded in XML as will be described below. • XML to the annotation tool exchange formats: We transform our XMLformatted data into an INCEpTIONS compatible import format, such as the Webanno TSV 3. 2 (https://inception-project. github. io/releases/0. 17. 0/docs/user-guide. html#sect_formats_webannotsv3), and vice-versa using a set of Python scripts. The Webanno TSV 3. 2 format is an extension of the CONLL (https://www. signll. org/conll/) format, with additions of the header and column representation.
Data Record The dataset is composed of 142 PDF documents, of which 92% (130) are OA (Figure  4a ). To comply with copyright restriction, few articles from our dataset are not publicly available in our repository. The top three publishers represented in the corpus are American Physical Society (APS), Elsevier, and IOP Publishing (Figure  4b ). Figure  4c  illustrate the distribution by publication date. We summarise SuperMat's content in Table  4 , with the statistics of documents, entities, and links given separately. In particular, this dataset contains 16052 (7166 unique) entities spread over six labels and 1398 links. Each document is encoded according to the XML TEI guidelines, which is a rich format for document representation. We have carried out no specific customisation, in order to remain fully compliant with the general TEI schema.
Applications SuperMat is constructed as a resource for TDM applications in superconducting materials. It can be used as data source in several complementary tasks: (1) creation of an automatic information extraction system for dataset creation, (2) articles classification, (3) named entity extraction (for example, automatic dictionary construction), (4) clustering and document synthesis, (  5 ) training of machine learning (ML) algorithms, (6) evaluation of rule-based or ML-based algorithms, and (7) development of downstream processes, such as material name parser, or quantity normalisation.
Practical applications Such a dataset may benefit several types of possible applications: • Evaluation tasks: This corpus can be used for evaluation tasks on automatic extraction. In particular, we can envision two popular tasks in superconducting materials science, namely: (a) NER and (b) EL methods. EL techniques have been mainly designed and studied using text from Wikipedia and newswires services which represent most of the available data. To the best of our knowledge, however, there is no application within materials science. • Automatic information extraction for superconducting materials: This dataset can be used as training data for such a purpose. Automatic information extraction using ML and text mining techniques can accelerate the construction of databases for superconducting materials.
Technical Validation The following measures were employed to ensure the creation of a high-quality dataset: • Each document was revised and validated by domain experts, • The workflow begins by assigning machine-based annotated data. This has demonstrated to improve the annotation task over several aspects, namely: time consumption, error rate, and annotation agreement  [37] [38] [39]. • On-the-fly automatic annotation recommendations, which provide fresh suggestions based on online decisions made by the annotators. • The annotators have rapid access to changes in the annotation guidelines. • The discussions were documented and linked in the guidelines. • Reviews are discussed and approved collaboratively between domain experts and other annotators. These guidelines are a vital piece of this work since they contain knowledge accumulated from these activities.