dataesr/scientific-paragraphs-categorization huggingface dataset:
data visualization in IdR IDR integrates image data and metadata from several studies. The current IDR web user interface (WUI) is based on OMERO. web, an open-source application  17  , and is supplemented with a plugin allowing data sets to be viewed by study, genes, phenotypes, siRNAs, antibodies, compounds and organisms (Supplementary Note). This architecture makes the integrated data resource available for access and reuse in several ways (Supplementary Note). Image data are viewable as thumbnails for each study, and multidimensional images can be viewed and browsed. Tiled whole-slide images used in histopathology are also supported. Any regions of interest (ROIs) submitted with the image data are included and linked and, where possible, made available through the IDR WUI. IDR images, thumbnails and metadata are accessible through the IDR WUI and web-based API in JSON format (Supplementary Note).
Standardized interfaces for imaging metadata IDR integrates imaging data from many studies. These data were acquired by various imaging modalities, in the absence of overarching standards for experimental, imaging or analytic metadata. While efforts such as MIACA (http://miaca. sourceforge. net/), NeuroVault  18  , MULTIMOT  19  have proposed data standards in specific imaging subdomains, there is not yet a metadata standard that crosses all the imaging domains potentially served by IDR. We therefore sought to adopt lightweight methods from other communities that have had broad acceptance  20  and converted metadata submitted in custom formats-spreadsheets, PDFs, MySQL databases and Microsoft Word documents-into a consistent tabular format, inspired by the MAGE-TAB and ISA-TAB specifications  21, 22  , that could then be used for importing semistructured metadata such as gene and ontology identifiers into OMERO  23.
We also used the Bio-Formats software library RESOURCE to identify and convert well-defined, semantically typed elements that describe imaging metadata (for example, image pixel size) as specified in the OME Data Model  24, 25  , and we used the resulting translation scripts to integrate data sets into a single resource. The scripts are publicly available (Online Methods) and thus comprise a framework for recognizing and reading a range of metadata types across several imaging domains into a common, open specification.
Data in IDR show abnormal nuclear phenotypes in several tissues from Car4 -/-mice, including gastrointestinal (https://idr. openmicroscopy. org/webclient/?show=dataset-153), liver (https://idr. openmicroscopy. org/webclient/?show=image-1918940) and male reproductive tract (https://idr. openmicroscopy. org/webclient/?show=image-1918953). The human ortholog, CA4, is involved in certain forms of retinitis pigmentosa  31, 32. Data in IDR from the MitoCheck study show that siRNA-mediated depletion of CA4 in HeLa cells  4  also results in abnormally shaped nuclei (https://idr. openmicroscopy. org/webclient/?show=well-828419), consistent with a defect in some aspect of the cell division cycle. Phenotypes across distinct studies can also be used to build novel representations of gene networks. Figure  2a  shows the gene network created when knockouts or knockdowns that caused an elongated cell phenotype (CMPO_0000077) in Schizosaccharomyces pombe and human cells are linked by queries to String DB  33  and visualized in Cytoscape  34  (Supplementary Note and Supplementary Table  1 ).
data integration and access Like most modern online resources, IDR makes data available through a web user interface as well as a web-based JSON API. This encourages third parties to make use of IDR on their own sites. For example, image data in IDR have been linked to study data in BioStudies (for example, BioStudies S-EPMC4704494) and to PhenoImageShare  36  , an online phenotypic repository (http://www. phenoimageshare. org/search/ ?term=&hostName=Image+Data+Repository+(IDR)). To further extend the possibilities for reuse of IDR data, we are calculating comprehensive sets of feature vectors of IDR image data using the open-source tool WND-CHARM  37. To date, full WND-CHARM features have been calculated for images in idr0002-A, idr0005-A, idr0008-B, idr0009-A, idr0009-B and idr0012-A and for parts of idr0013-A and idr0013-B.
Features are stored in IDR using OMERO's HDF5-based data store and available through the OMERO API (Supplementary Note). The integration of image-based phenotypes and calculated features makes IDR an attractive candidate for computational reanalysis. To ease the access to IDR's TB-scale data sets, we have connected IDR to a Jupyter notebook-based computational resource (https://idr. openmicroscopy. org/jupyter) that exposes IDR data sets via an API (https://idr. openmicroscopy. org/about/ api. html). We include example notebooks that provide visualization of image features using PCA, access to images annotated with CMPO phenotypes, calculations of gene networks and WND-CHARM features for individual images and recreation of Figures  1  and 2  from IDR data. Users can also run their own analyses using notebooks stored in GitHub (https://github.
com/IDR/idrnotebooks). To allow reuse of IDR metadata locally, we have made all IDR databases, metadata and thumbnails available for download and have built Ansible scripts that automate deployment of the IDR software stack (original image data are not included; see Supplementary Note). dISCUSSIOn Making data public and available is a critical part of the scientific enterprise  38. To help facilitate the reuse and meta-analysis of image data sets, we have built IDR, a next-generation data technology that integrates and publishes image data and metadata from a wide range of imaging modalities and scales in a consistent format. IDR integrates experimental, imaging, phenotypic and analytic metadata from several independent studies into a single resource, allowing new modes of biological Big Data querying and analysis.
As more data sets are added to IDR, they will potentiate and catalyze the generation of new biological hypotheses and discoveries. In IDR, we have linked image metadata from several independent studies. Experimental, imaging phenotypic and analytic metadata are recorded in a consistent format. Rather than attempting b a Figure  2  | Network analysis of genes linked to the elongated cell phenotype in the IDR. (a) Protein-protein interaction network based on the genes linked to the elongated cell phenotype (CMPO_0000077) in three IDR studies. Genes from S. pombe (green, idr0001-A)  5  , HeLa cell morphology (blue, idr0012-A)  39  and HeLa Actinome (red, idr0008-B)  40  are displayed with linkages (gray) from StringDB 33. To enable comparisons in Cytoscape, the human orthologs of S. pombe genes are used for the genes identified in idr0001-A (Supplementary note).
RESOURCE to enforce a strict imaging data standard, IDR provides tools for supporting community formats and releases these as a framework that facilitates data reuse. We hope that the availability of this framework will provide incentives for others to structure metadata in shareable formats that can be read into IDR or other applications. In the future, we can imagine that these and other capabilities could be extended in IDR-or similar repositories that link to IDR-to enable systematic integration, visualization and analytics across imaging studies, thereby helping to harness and capitalize on the increasing amounts of bioimaging data that the community generates. As of this writing, IDR has published 35 reference image data sets grouped into 24 studies (Table  1 ) and, using EMBL-EBI's Embassy Cloud, has the capacity to receive and publish many more.
Authors can submit image data sets for publication in IDR using the metadata specifications and formats we have built (details about the submission process are available at https://idr. openmicroscopy. org/about/submission. html). Once published, the data sets can be browsed and viewed through IDR's WUI or queried and reanalyzed using the IDR computational resource. IDR software and technology is open source, so it can be accessed and built into other systems for image data publication. This supports the building of technology and installations that integrate and publish bioimaging data for the scientific community. IDR therefore functions both as a resource for image data publication and as a technology platform that supports online scientific image databases and services. In the future, those databases and services may amalgamate to form resources analogous to the genomic resources that are the foundation of much of modern biology.
We thank all the study authors who submitted data sets for inclusion in the IDR for their contributions and help in incorporating their data sets. We also thank the systems support team at  EMBL-EBI , in particular  R. Boyce ,  D. Ocana ,  C. Short  and  A. Cafferkey  for their support of the project's use of the Embassy Cloud. We are particularly grateful to  S. Jupp  for help with adding and defining new ontology terms. The  IDR  project was funded by the  BBSRC  ( BB/M018423/1  to J. R. S. , A. B. and  R. E. C. S. )  and  Horizon 2020 Framework Programme  of the  European Union  under grant agreement  688945  ( Euro-BioImaging Prep Phase II  to J. R. S. and A. B. ). Updates to OMERO and Bio-Formats were supported by awards to J. R. S. from the  Wellcome Trust  ( 095931/Z/11/Z ) and  Horizon 2020 Framework Programme  of the  European Union  under grant agreement  634107  (MULTIMOT).
org/webclient/?show=image-648950 idr0010-doil-dnadamage https://idr. openmicroscopy. org/webclient/?show=image-3063667 idr0011-ledesmafernandez-dad4 https://idr. openmicroscopy. org/webclient/?show=image-2849866 idr0012-fuchs-cellmorph https://idr. openmicroscopy. org/webclient/?show=image-1821818 idr0013-neumann-mitocheck https://idr. openmicroscopy. org/webclient/?show=image-1636543 idr0015-UNKNOWN-taraoceans https://idr. openmicroscopy. org/webclient/?show=well-1056578 idr0016-wawer-bioactivecompoundprofiling https://idr. openmicroscopy. org/webclient/?show=well-1029401 idr0017-breinig-drugscreen https://idr. openmicroscopy. org/webclient/?show=well-1046336 idr0018-neff-histopathology https://idr. openmicroscopy. org/webclient/?show=dataset-369 idr0019-sero-nfkappab https://idr. openmicroscopy.
org/webclient/?show=well-1024671 idr0020-barr-chtog https://idr. openmicroscopy. org/webclient/?show=well-1030579 idr0021-lawo-pericentriolarmaterial https://idr. openmicroscopy. org/webclient/?show=dataset-51 idr0023-szymborska-nuclearpore https://idr. openmicroscopy. org/webclient/?show=dataset-61 idr0027-dickerson-chromatin https://idr. openmicroscopy. org/webclient/?show=image-2858266 idr0028-pascualvargas-rhogtpases https://idr. openmicroscopy. org/webclient/?show=image-2895051 idr0032-yang-meristem https://idr. openmicroscopy. org/webclient/?show=image-3125776 Data availability. All data sets described in this paper are available at https://idr. openmicroscopy. org.
Big Data and corpora From a technical perspective, Big Data often refers to datasets that are large enough to require significant computing power in their analysis. Due to their huge size, complexity or speed at which they grow, Big Data are sometimes differentiated from so-called 'small data' by their 'volume, velocity and variety'  (Laney, 2001: 1). However, as boyd and Crawford (2012: 663)  observe, what actually sets Big Data approaches apart is the 'capacity to search, aggregate, and cross-reference large data sets'. In 2008, the editor-in-chief of Wired Magazine at the time argued that the apparently comprehensive nature of these datasets replaces analysis and theory: This is a world where massive amounts of data and applied mathematics replace every other tool that might be brought to bear.
Out with every theory of human behaviour, from linguistics to sociology. Who knows why people do what they do? The point is they do it. [. ]  With enough data, the numbers speak for themselves. (Anderson, 2008)  Some critical scholars have reacted to this view of Big Data, pointing out the continued need for acknowledging 'the situatedness, positionality and politics of the social science being conducted'  (Kitchin, 2014: 10). Numbers rarely, if ever, speak for themselves in straightforward ways. Meanwhile, others have observed that most people do not directly access or encounter data in their original forms anyway. Instead, this access is often mediated through platforms, search engines or corporate interests  (Couldry and Turow, 2014). Also, large datasets present issues of provenance and validity, particularly in the case of social media data  (Driscoll and Walker, 2014).
Since then, the development and availability of large reference corpora drawn from diverse sources exemplifies how digital access to more textual data has transformed contemporary study of language. Meanwhile, techniques like webscraping and data mining have enabled innovative study of online language that relies upon corpora containing blogs, tweets, comments or forum postings  (Hardaker, 2010). And these textual datasets can be linked with other forms of data -like those held in Geographic Information Systems (GIS) -to extend linguistic knowledge even further  (Gregory and Hardie, 2011). The central ideas of 'volume, variety, and velocity' are just as relevant for lexicographers and linguists who build, maintain and share corpora, especially in cases where corpora are either derived from online sources or archives, or are linked with other kinds of databases.
3 The UK-based think-tank Demos also features a 'Centre for the Analysis of Social Media' that links quantitative textual analysis with ongoing political and policy debates. And the Leveson Report in the UK, produced after a major public investigation into British press practices, considered several pieces of linguistic research as evidence for inaccurate, biased and unfair reporting on immigration  (Leveson, 2012: 670-1). They were introduced by representatives of ENGAGE and the Federation of Muslim Organisations, two examples of civil society and community-based groups. So, whether through intermediaries or more direct engagement with non-academic groups, people working with text as data are continuing to take their findings outside the academy. But knowledge exchange is not straightforward in practice: it is contingent on the contexts in which it eventually happens  (Sebba, 2013).
Data and methods To address these questions, I draw upon two research projects conducted by the Migration Observatory (MigObs), an organisation that transmits academic research on migration to non-academic stakeholders including journalists, non-governmental organisations (NGOs), civil society organisations and Parliamentarians. Other academics, educators and students also use MigObs resources for research, teaching or self-study. These projects aim to analyse how the British national press has portrayed migration issues across many publications and several years, and then relate these to changes in how the public perceive immigration issues. They developed from a straightforward rationale: before making assertions about how or why the press should cover immigration in particular ways, it is necessary to understand as fully as possible what the press has actually said in the first place.
Project 1: Migration in the News, 2010-2012 Since MigObs did not have prior experience in conducting corpus analysis, the first task was to pilot the data collection, methods and analysis. This took the form of a study that was limited in scope to three years. It aimed to answer two questions: (1) what kinds of language have different sub-sections of the The project aimed to collect, as far as possible, all items in all UK national newspapers that mentioned immigrants, migrants, asylum seekers or refugees from 1 January 2010 to 31 December 2012. This three-year period covered an important time in British politics and migration policy change, including a General Election. Nexis UK, a database service that archives many kinds of international periodicals, was queried using the following search string: (refugee! OR asylum! OR deport! OR immigr! OR emigr! OR migrant! OR illegal alien! OR illegal entry OR leave to remain NOT deportivo NOT deportment).
In total, the dataset contained 58,351 items comprising 43,966,872 words. This study used collocational analysis based on a combination of Mutual Information and log-likelihood tests to identify the kinds of modifiers used to describe immigrants, migrants, asylum seekers and refugees. By focussing attention on those words that immediately appeared before mentions of each migrant group in the L1 position as well as within five words to the left or right, the study identified the main descriptors used to reference different migrant groups. Although this is not a perfect rule -some modifiers may appear outside the L1 position, as in 'an IMMIGRANT from BULGARIA' -it does reflect a tendency in written English to use adjectives immediately before the nouns they modify. However, newspaper coverage can be episodic: some events may generate a great deal of unique coverage that does not reappear.
Project 2: An Expanded View of British Newspaper Coverage, 2006-2013 After completing that pilot study, MigObs wanted to expand its view of the British newsprint media to a wider time period. Accompanying this aim was a secondary objective of improving its understanding and use of data visualisation to share results with end-users. As a research intermediary, MigObs actively promotes its analysis and research among a host of nonacademic users. For reasons outlined in Section 2. 2, visualisation was an important mode of communication that demanded greater attention. So, in Project 2, MigObs extended the previous corpus of newspaper items to include items published between January 2006 and December 2013. Derived from the same search string, this second corpus eventually contained about 90 million words from twenty-one titles -the additional publication being the News of the World.
Also, a major difference was the manner in which the corpus was stored and analysed. This time, MigObs used Sketch W. Allen Engine  (Kilgarriff et al. , 2014)  to conduct more detailed frequency and collocational analysis similar in type to, but in greater depth than, the pilot study. Then, as part of a larger project called Seeing Data, 8  MigObs visualised this corpus. The project team consisted of Helen Kennedy, Rosemary Lucy Hill, Andy Kirk and myself. We enlisted Clever Franke, a leading European design firm based in the Netherlands, to build a visualisation based on the textual dataset. The resulting interactive visualisation, which is still a work-in-progress as analysis of the expanded corpus continues, combined different kinds of charts to show four key analyses: word frequencies of immigrant, migrant, asylum seeker and refugee; collocations of each of these words; frequencies of people or organisations mentioned; and co-occurrences of people or groups in the items.
Furthermore, this value demands identifying where gaps in evidence and data exist. Finally, 'engagement' involves enabling users to probe and explore the complexities of immigration in as self-directed a manner as possible. It also means introducing previously unfamiliar topics in accessible language. These values guide MigObs as it seeks to achieve its three main objectives: first, to provide independent and evidence-based analysis of available migration data; second, to use these analyses to inform public debate as it happens in media and policy; and, third, to produce its own high-quality research on migration and policy issues. In Sections 5. 1 to 5. 3, the practical discussion draws upon observations of how these values and objectives informed decisions that occurred throughout the processes of making the two visualisations.
Lessons for corpus linguists Visualisations play important roles as modes of communicating and facilitating analyses  (Kirk, 2016). Within corpus linguistics, they make patterns in texts visible in ways that go beyond conventional techniques like concordances  (Culy and Lyding, 2010). From the perspective of MigObs, visualisations are especially useful for two reasons. First, they enable users who may possess different expertise or skills to interact with an analysis and generate insights that are useful to them. Second, they can display key findings in ways that may be more accessible than conventional written outputs like reports or briefings. Since the point of the original study in Project 1 was to shed light on the actual language that the British national press had used to describe different migrant groups, and given organisational values such as engagement and clarity as discussed in Section 4.
Develop clear communication among all team members who have different areas of expertise Assessing these values and aims is one of several important steps. In a context where multiple team members contribute to the development and design of a visualisation, it is vital to ensure that all participants understand why these values are so important for the project. Also, designers, academic researchers and intermediary organisations possess different kinds of expertise and skills. While this may appear to be a source of strength, in practice it requires time and energy to ensure that diversity of perspectives does not lead to divergence of paths. Three examples of collaboration among the Seeing Data research team, MigObs, and Clever Franke while developing the corpus visualisation illustrate this point well.
Second, the visualisations handled word frequencies differently. The first output did not consider how mentions of each target word varied over time because its research design treated the entire 2010 to 2012 period as a unit: it was concerned with consistent portrayals, not variation. In contrast, the second output took advantage of its diachronic corpus and showed how mentions of each target group changed on a monthly basis. As shown in Figure  3 , it also included annotations indicating when important events in British policy or politics occurred, particularly elections. These annotations were seen as important markers of context that different audiences -perhaps 10 Details of how this score is calculated can be found in  Rychlý (2008). students, civil society organisations or members of the press -would be able to use as they navigated the dataset.
Third, the visualisations aimed to enable users to access different levels of analysis, but without necessarily prioritising one over the others. This constraint related to MigObs' organisational values of independence and engagement. In the first example, buttons along the right-hand side allowed users to toggle quickly among two kinds of collocational analysis, three publication types and four target words. Hovering over each square also revealed specific frequencies in both raw and normalised forms. These features were included for two reasons: an aim of being transparent with results for the benefit of users, and for consistency with other charts MigObs had already created with the same Tableau software. This was handled differently in the second visualisation. Although users could customise their view based on publication type or target word, the output did not include the ability to read off individual values.
Also, the mix of available skills and capacities held by MigObs meant that Tableau Public presented an achievable solution. In contrast, the bespoke visualisation produced by Clever Franke as part of the Seeing Data project was intended to show changes in linguistic features over time. It also aimed to take advantage of the greater range of skills available through the collaboration with design professionals by using more advanced software and libraries to express differences through colour and layout, and not just size and reading values. These decisions were made through iterative discussion involving the designers, researchers and MigObs staff. Therefore, the value of building time for communication (Section 5. 2) remains important here, too.