dataesr/scientific-paragraphs-categorization huggingface dataset:
Flow cytometry analysis of Primary Dcs For flow cytometry analysis of primary myeloid DCs, cells were harvested from cultures and blocked with PBS/BSA 1% plus TruStain fcX anti-mouse CD16/32 (BioLegend) and stained using the following conjugated antibodies: DCs: anti-CD11c (APC), anti-CD11b (FITC), anti-MHC-II (Percp-Cy5. 5), anti-CD80 (PE-Cy7), and anti-CD86 (PE), all purchased from BioLegend. Stained cells were acquired using Attune™ NxT Acoustic Focusing Cytometer (Thermo Fisher). Data analysis was performed using FlowJo software (Tree Star, Ashland, OR, USA).
Peptide/Protein Binding Prediction The computational modeling platform PepSite 2. 0 (Russel-Lab)  (34)  was used to predict the binding probability of peptides to mouse MIF (PDB: 1MFI, chain B) and mouse CD74 (PDB: 1IIE, chain B) proteins. Results are displayed as p values, where p ≤ 0. 05 values are the statistically significant binding predictions. iCDR peptide was used as a negative peptide control. Binding probability was calculated using the interval 0. 01 < p < 0. 05, where p = 0. 01 represents 100% of binding probability and p > 0. 05 represents 0% of binding probability.
cD74 normal Mode calculations and generation of low-energy conformations The CD74 structure 1IIE  (35)  (residues from 118 to 176) was used to perform normal modes analysis using CHARMM c41b1 and CHARMM36 force filed using DIMB module. A distancedependent dielectric constant was employed to treat the electrostatic shielding from solvation. The five lowest-frequency normal modes were computed as directional constraint to generate lowenergy conformers along the mode trajectory using the VMOD algorithm in CHARMM, as previously described  (36, 37). The restraints were applied only on Cα atoms, and the energy was computed for all atoms. The structures were displaced from -3. 0 Å to +3. 0 Å using steps of 0. 1 Å, resulting in 61 intermediate energy relaxed structures along each mode.
A pilot study addressing the binding probability of C36L1 to MIF and its receptor CD74 was carried out using the computational modeling prediction of peptide-binding sites to protein surfaces and the PepSite 2. 0 algorithm  (34). This in silico approach predicted a statistically significant binding of C36L1 to mouse CD74 B chain (PDB: 1IIE) protein (p < 0. 001), and a potential binding to mouse MIF B chain (PDB: 1MFI) protein (p = 0. 04) (Figure  4A ). No interaction with either CD74 or MIF was predicted for an irrelevant control CDR peptide (iCDR-CE48-H2), which was previously observed to have no effect on metastatic melanoma proliferation in vitro and progression in vivo (25) (Figure  4A ; Figure  S7A  in Supplementary Material). We used the PepSite 2. 0 algorithm to identify the amino acid residues involved in the interaction of C36L1 to CD74 and found that the peptide is predicted to interact with Tyr (118), Arg (179), and His (180) residues from the B chain of the murine/human CD74 protein, highlighted in red (Figure  S7B  in Supplementary Material).
Introduction Scientific knowledge relating to our environment, human health, climate change and global ecosystems increasingly requires the creation and evaluation of diverse and varied datasets, warranting training to raise the data competencies of researchers and data providers  (Hampton et al. 2017 , Hampton et al. 2013 , McCulloch 2013). One of the important data sources for ecological data is from specimens found in natural history collections  (Hampton et al. 2013 , iDigBio 2016 , Lister 2011 , Otero-Ferrer et al. 2017 , Shaffer et al. 1998 , Arnaud et al. 2016 , McGeoch et al. 2016 , Krishtalka et al. 2016). Natural history collections, or biocollections as defined in this paper, include any specimenbased biological, zooarchaeological, or paleontological collections  (Lieberman and Kimmig 2018).
The Georeferencing for Research Use (GRU) workshop was developed jointly by Int egrated Digitized Biocollections (iDigBio), UC Santa Barbara Cheadle Center for Biodiversity and Ecological Restoration (CCBER), VertNet, Denver Botanic Gardens, Yale Peabody Museum, Stanford Earth and the GEOLocate project to provide an innovative, integrated forum for discussion between data researchers and data providers about the use and critical creation of geospatial coordinates from biocollection specimens, one aspect of biocollection data. This paper reports our findings and observations, and captures the participants' discussion around changing museum practices, use of error ranges in geospatial data, and needs for continued training and tool development.
e. , latitude, longitude) are retroactively applied to a text description of an organism's locality in a process referred to as "georeferencing. " Georeferenced locality data are widely made available through biodiversity data aggregators such as Atlas of Living Australia (ALA), the Global Biodiversity Information Facility (GBIF), Integrated Digitized Biocollections (iDigBio), and VertNet in a digital format. Digital data records for more than 115 million physical specimens have been shared (iDigBio 2018), leading to large amounts of digitally available locality data about organisms. Of those 115 million records, 59 million have coordinates. For example, the points on this map in Fig. 1  indicate geocoordinated specimen location data available through iDigBio for beetles in the genus Cicindela (Coleoptera) from California, USA, with each point representing one or more specimen records found in a collection (Fig.
Rationale: Raising data competencies for researchers and data providers The data captured from biocollection specimens are typically managed by collections staff who care for specimens and digitize the data associated with those specimens, ultimately making the data available for research. This creates a scenario where the people creating the datasets are often not the same people who use the data in research, which creates a need to provide venues of communication and foster understanding between these stakeholders about the implications of each other's' methods on research products  (Tenopir et al. 2015 ,Zimmermann 2008). Geospatial and biocollections communities benefit from mutual conversations that inform the work of both biocollections staff and the research community, and through exposure to computational technologies, data, and software for working with spatial data.
History: Background and provenance of recent efforts in biocollection digitization Recognizing the value of biocollections for research, education, and society, a diverse group of scientists outlined a coordinated effort they envisioned as a Networked Integrated Biocollections Alliance (NIBA), which resulted in strategic and implementation plans for the digitization of US collections information  (Hanken 2013, Chapman and Wieczorek 2006). In response, in 2011 the US National Science Foundation (NSF) instituted the program Advancing the Digitization of Biological Collections (ADBC). As part of the ADBC, NSF funded the Integrated Digitized Biocollections (iDigBio) 10 year project as the hub for US non-federal collection digitization efforts. iDigBio's missions are to support efforts by biocollections to digitize, mobilize, aggregate, and provide access to biological specimen data for biodiversity research and education globally.
Museums holding biocollections collaborate to form Thematic Collection Networks (TCNs) and seek NSF ADBC funding to support digitization of select collections to meet targeted, specific research needs and share this mobilized data via iDigBio. As part of this initiative, the iDigBio Georeferencing Working Group (GWG) was formed to support the biocollections community to implement best practices in the improvement and maintenance of critical location data. The GWG benefits from the contributions from many, including GEOLocate, VertNet, TCNs, and earlier projects (e. g. , Georeferencing. org, MaNI S, FishNet2, ORNIS and HerpNET). In response to biocollection georeferencing needs, the GWG offered two Train-the-Tainers workshops in 2012 and 2013. These workshops were aimed at training biodiversity and collections professionals to use best practices for georeferencing  (Bloom et al.
2017) , to mobilize biocollection occurrence data, and to encourage those participants to share what they learn with others for the benefit of the collections community as a whole. The primary audience for these workshops included the collaborative museum and herbaria ADBC TCNs staff, collection managers, and curators engaged in active transcription and georeferencing of collections data. An additional workshop, Field to Database (F2DB) (2015), targeted researchers and the collection of new, future biocollections data. F2DB incorporated current best practices for creation and sharing of georeferences and locality information to be born digital, that is mapped to data standards, if possible, in electronic format and georeferenced at the time of collection (Online Computer Library Center (OCLC) 2018).
This process results in higher quality data, mobilized more efficiently, and avoids adding to the legacy pile of text locality strings to be data-entered and georeferenced. The Biocode Field Information Management System (Deck and Ewing 2016) is an example project supporting the generation of born digital biodiversity data. Other workshops, such as the joint Synthesys-iDigBio: Digitization Software Training Workshop, have similarly shared tools for biocollection data generation and standardization. Training provided by the iDigBio project initially focused on community-derived digitization best practice discovery and documentation but has increasingly incorporated innovative use of scientific collections data for research into workshops and symposia design. The iDigBio Georeferencing Working Group (GWG) saw the need to offer a georeferencing workshop that would combine best practices for historical and new data with new lessons on evaluating the resulting biocollections data for their fitness for research and other downstream use.
Georeferencing for Research Use (GRU) Workshop Design and Implementation To design the Georeferencing for Research Use workshop, the GWG first reviewed materials used in prior workshops (e. g. , Train-the-Trainers, Field to Database) to determine what lessons about the evaluation of geospatial data should be integrated into the course curriculum. Additional content was then included by the course instructors from Biodiversity Informatics Training Curriculum (BITC), Cheadle Center for Biodiversity and Ecological Restoration (CCBER), and University of California, Santa Barbara National Center for Ecological Analysis and Synthesis (NCEAS) and iDigBio. A specimen dataset downloaded directly from iDigBio was used as the example dataset (Suppl. material 1). The dataset contained 25,429 records of ground beetles (Family: Carabidae) found in California.
Accessed on 29 August 2016, this dataset was used in the data access demonstration, visualizations, data cleaning and QGIS tutorials during the workshop. Geospatial expertise outside the existing GWG group was recruited to develop a new Quantum Geographic Information System (QGIS) tutorial for biocollections data. The resulting tutorial content is now available on GitHub as a series of QGIS Natural History Collection Lessons. The final course outline can be found on the iDigBio Georeferencing for Research Use (GRU) Wiki. Topic choice and time spent on specialized topics was in part guided by the applicants' expectations of the course. Using an online form that called for participation and evaluated expectations of participants (Suppl. material 2), applicants were asked to describe: 1) the reason(s) for their interest in the course, 2) any current and/or future projects they were involved with that would benefit from their receiving training, 3) knowledge of, and experience with, georeferencing, and 4) their ideal syllabus for a 4-day georeferencing workshop.
Results and Outcomes In total, 42 applications were received in response to the open call for participants. Twentythree participants were accepted, with priority given to persons who had not participated in prior iDigBio workshops, and whose expectations best matched the proposed workshop goals and scope. Participants primarily self-identified as researchers or research students (14 participants) who use biocollections spatial data in their research projects, or as data/ collection managers (9 participants). Several participants also self-identified as having multiple career needs for participating in the training, for example, a researcher who is also managing a biocollection, or a data manager who is also a student. The combined audience fostered collaboration and understanding across these different domains.
Conversations around GIS, QGIS, and other tools Workshop participants self-identified as a researcher, data manager, GIS expert, or some combination of identities. The needs of each of these groups for data management and georeferencing skills were similar, yet each group had particular goals. For example, researchers were interested in the functionality of OpenRefine software for standardizing biodiversity data sets, new tools for efficient field data collection, such as the Biocode Field Information Management System, and efficient georeferencing of large datasets with highthroughput analyses of specimen coordinate accuracy. There was a strong desire for efficient and accurate georeferencing of biocollections to create "research ready" data that is available for complex, data-driven analyses  (Seltmann et al.
2017). Developing the skills available through OpenRefine for data cleaning was expressed to be of great benefit for basic cleaning of collections datasets prior to publication and ingestion by data aggregators such as iDigBio and GBIF. Data visualization tools, such as QGIS, for determining the accuracy of geopoints were perceived to be of great benefit to researchers and data/ collections personnel alike. Researchers were more interested in more advanced applications, such as the use of R and Python scripting due to the desired downstream applications and analyses. Workshop participants with a strong GIS background were primarily interested in learning more about the current practices of researchers and data managers in data cleaning, georeferencing, and spatial analysis to identify areas of opportunity for improving the ability of domain experts to assess data quality and gain new insights to visualize their data spatially.
3 ). In the geospatial community, bounding boxes are often used to delineate the extent of resources rather than points with a given radius. Additionally, many field record entries reference named places which can be reconciled against a gazetteer. Participants learned how this presents an opportunity for exploiting existing geometries to use as a record boundary from existing sources, such as OpenStreetMap's Application Program Interface (API), or shape files for publicly available datasets. Researchers and data managers were taught how to import existing georeferenced collections into software such as QGIS. Once spatially referenced, it is possible to couple the georeferenced data with existing layers of spatial information that can supplement assessment and analysis. Some layers of interest included administrative boundaries (for checking centroids, or center points of polygons, against existing records), ecoregions, cultivated gardens and zoos, and parks and protected areas.
Other researchers expressed interest in acquiring and incorporating data about elevation, federally managed sites, and climate data. A conversation about where to find data resources, like local municipality GIS office websites and ArcGIS Online's Living Atlas of the World, proved helpful. On the first day of the QGIS tutorial, the topics covered included importing tutorial data obtained from iDigBio, adding additional layers, and saving a map project. The following day included advanced topics, such as performing a spatial join across layers to query attributes, sub-setting the dataset based on spatial selections and intersection, changing the symbology of the data, and performing summary statistics on the attributes. Additionally, visualizing the data over time and producing heat maps of observation locations provided new views on trends within the dataset that would be impossible to detect by simply viewing the records in a table.
Reactions from the data managers and researchers about the QGIS tutorial was positive. Many were excited about the prospect of incorporating QGIS and other spatial analysis techniques into their georeferencing workflows. While fewer participants indicated interest in using QGIS for data cleaning, many expressed interest in using the techniques for data exploration and public communication. Participants who brought their own datasets to the workshop spent time on the last day running similar analyses against their collections in QGIS and were interested in learning how to perform specific GIS operations to spatialize their research questions. For example, several participants were interested in assessing the spatial distribution of specimens in their study area to find a region with a greater significant number occurences.
Others were interested in testing predictions about regions where the specimens were not likely to occur (e. g. , studying absence) and using other spatial information to account for the trends in distribution. Desired skills to develop included coupling QGIS with Python scripting to enable researchers and data managers to work with larger datasets. Researchers who also wanted to incorporate QGIS into georeferencing workflows were interested in plugins such as QGIS Gazetteer Plugin that would allow them to cross-reference observations and flag records for removal from further analysis. Best practices for editing CSV files in QGIS were also desired, and a lack of current consensus surrounding when to remove a point from a dataset resulted in inconsistent practices surrounding editing of data using QGIS.
For example, once a point has been flagged because it corresponds too closely to a county centroid, should it be removed from the dataset or can its position be rectified? Should the record be noted but not included in analysis? Ideally, the modified and annotated research dataset would be published with original identifiers to enable linking of the data to the original data record and the physical specimen. Many researchers and collections managers expressed interest in a follow-up tutorial on more advanced techniques in QGIS and were highly motivated to incorporate QGIS into their workflows upon completion of the tutorial. Interest in using QGIS for georeferencing tasks, data exploration, and public communication indicated that planned future tutorials could help users apply more advanced techniques to their existing research or collections datasets.
Participant surveys and evaluations We gathered data from the participants using pre, post and follow-up surveys. Results from these were used to gauge enthusiasm from participants for the workshop and the direction participants thought future training should progress. The pre-workshop data was collected online through the workshop application using a google forms (Suppl. material 2). The post workshop (Suppl. material 3) and follow-up surveys (Suppl. material 4) were distributed electronically via Qualtrics Survey software licensed to the University of Florida. The application form, post-workshop surveys, and follow-up survey questions and protocols were reviewed and approved by the University of Florida Human Subjects Committee Institutional Review Board (IRB) (University of Florida IRB201601849).
Pre-and post-workshop surveys Post workshop surveys were given to participants at the end of each day. All participants recorded their knowledge of georeferencing best practices and resources, and their skills for creating and using spatial data were higher after the workshop, with 63-68% of participants indicating a much higher knowledge level after the workshop. The post-workshop surveys show that more than 50% of participants rated all but two topics covered in the workshop as "most valued. " Comments on the survey suggest that the two, less-highly rated topics Good-Bad Localities and Getting Datasets, were seen as review rather than introduction of new information. Participants also responded that the most useful topics provided during this workshop were OpenRefine and QGIS, the advanced features of GEOLocate, and how to incorperate available APIs into workflows for both basic georeferencing and research.
Follow-up survey Fifty-nine percent of the participants responded to the follow-up survey distributed three months following the workshop (Suppl. material 4,Suppl. material 5). Some of the results are featured in Fig. 4. All participants indicated an increased use of OpenRefine, 77% indicated an increased use of GEOLocate, including one participant who indicated the use of R for GEOLocate, and 54% indicated an increased use of QGIS. About half of the participants had increased their use of documentation relating to georeferencing best practices and the Georeferencing Calculator relative to before the GRU workshop. The majority of participants indicated that into the future, their use of GEOLocate, OpenRefine, QGIS, and georeferencing best practices will continue to increase. QGIS was primarily being used for data visualization, but also for spatial analysis; OpenRefine was being used primarily for data cleaning, error detection, and data reconciliation.
Almost all participants that responded provided one-on-one training to institutional colleagues, gave a group presentation, or posted information via social media or blog post. Several participants also reported sharing the knowledge they gained with colleagues outside their institution, and with students, and a publication was inspired by the workshop (Park and Davis 2017). Such sharing of information is critical to helping train the biodiversity workforce (e. g. , Hampton et al. 2017, Biodiversity Literacy for Undergraduate Training (BLUE), TDWG Biodiversity Informatics Curriculum Interest Group). All participants that responded desired advanced training on the software and tools (primarily GEOLocate, QGIS and OpenRefine) introduced during the GRU workshop. Participants all indicated an increase in confidence in the use of GEOLocate, QGIS, and OpenRefine, regardless of their initial knowledge of the software prior to training.
Topics for future workshops A complete list of topics and new tools that participants identified during the workshop as something they wanted to learn more about is available in the supplementary files (Suppl. material 8). In general, the topics of interest trended toward automation, analysis, and data complexity. 32 items were added to the list during the workshop discussion, and out of those, 11 have been addressed at previous iDigBio workshops, and 19 directly involve software training.
Discussion Digitization of biocollections is somewhat unique in the biological and paleontological data community because the major funding sources that create the data often do not include financial support for research using the resulting digitized data products. This produces large-scale data capture projects that do not participate in research efforts using those data, and these data providers (both collections and aggregators) need input from data users to improve their data products. One of the expectations between data providers and data users involves the quality of the georeferenced coordinates. Data providers have a mandate to deliver data efficiently, and often data users require data with a quality than may not be cost-efficient or feasible to produce. Other times, locations are deliberately obfuscated (see dwc:informationWithheld) in public databases due to biosecurity, privacy, sensitive species, and other legal reasons.