dataesr/scientific-paragraphs-categorization huggingface dataset:
Use-Cases for VIS30K We envision the following use-cases for VIN and VIS30K: 1. Identifying Related Work. Typically, when researchers search for related work, they either rely on text search in digital libraries or they manually follow trails of citations from one paper to the next. In addition to offering a text-based search in paper metadata, VIN offers a focused, visual way to quickly browse related work that is impossible with other research databases or generic online image search tools. This visual search can complement the traditional related work search and enable researchers to stumble upon papers with similar layouts, data representations, or interfaces that may not show up in text searches. Image overviews also help them to see and describe differences in data visualization styles spanning multiple years that may be more difficult to grasp from images confined to individual papers.
2. Teaching and Communication. Our image database and VIN can also be used to quickly find images for teaching and communication. By filtering out later years of the conference, for example, historic examples from the community can be retrieved and compared to the current state-of-the-art. Browsing the most recent years reveals new contributions and the latest advances. Our images are stored in a lossless format at a resolution that supports their use for teaching and communication. In addition, extracting paper references is made simple through the VIN interface (Fig. 6(c) ). Further, there may be users outside the community who are interested in the types of representations published by the community but lack easy access to papers or are not accustomed to reading scientific content. For these groups of users, VIN can serve as an entry point to research in the visualization community and spark interest in exploring its work further.
Thus, VIN also serves as a bridge to other communities. 3. Understanding VIS. Both the Visualization community itself and external researchers interested in the history of visual data analysis may be curious about the evolution of the field. Past efforts on understanding practices in the community were listed in Sec. 2. Complementing this past work, our data and tool now offers overviews of our community's visual output both in the form of both figures and tables. Researchers can either engage assess and analyze the data qualitatively using VIN or download it to build additional tools that use their own metrics to support quantitative study of the image content. 4. Tool Building and Testing. Our database can be used by others to extend VIN or build novel tools for other types of image analysis tasks.
For example, future projects could build a dedicated image similarity search tool on top of VIN, use the database as training data for machine learning algorithms, or look into visualizing image content (e. g. visual question answering and visualization re-targeting). The database can also be used for computer vision projects. Our results demonstrate that the state-of-the-art CNN solutions and figure and table extractions do not achieve human-level accuracy. This finding suggests that our VIS30K dataset could present a grand challenge for future benchmarking of machine learning research.
DISCUSSION AND CONCLUSION We introduce VIS30K, a curated and complete dataset of all figures and tables used in IEEE VIS conference publications over its 30year history. We also provide a data exploration tool, VIN, that facilitates interactive exploration of this scholarly resource as well as a collection of the relevant metadata. For the first time, our VIN tool enables researchers and practitioners to search for approaches related to their own or solutions for their data analysis problems in a visual way-after all, most of us remember images we have seen in the past much better than the specific names of the relevant papers. Our search also enables researchers to quickly find related work they may not even be aware of, without requiring them to read and download several possibly related papers from digital libraries.
In addition to these immediate benefits of our interactive search, our dataset will allow us to explore a number of interesting research questions in the future. For example, how has visual encoding been used in the past, and has this changed over time? Do the three conference tracks use specific forms of encoding in a similar way or are there differences? How can we create a visualization with a similar style? Our work also has implications that arise from our specific extraction approach. We used CNNs to extract the image and table locations via generated pseudo papers, followed by a manual cleaning step to ensure quality. Without CNNs, a huge amount of manual work would have been needed. Without our manual cleaning, similarly, we could not have ensured our high data quality. While we worked on published papers, our hybrid CNNmanual approach is not limited to such documents: it could well be applied more broadly, e.
g. , to XML-based solutions such as GROBID  [29]. We also anticipate that DeepPaperComposer  [27] , a newer model for non-textual content extraction can provide a scalable solution for information extraction from future VIS publications. Our constructive experience could inspire future work on pipelines that seek to extract images and tables from documents. Naturally, our work is not without limitations. Our dataset does not represent all of visualization scholarship. We examined papers in only a single venue and did not collect scholarly figures presented at other venues, e. g. , EuroVis, PacificVis, CHI, and other related conferences. We also did not collect visualization-related journal articles in the IEEE Transactions on Visualization and Computer Graphics and in IEEE Computer Graphics and Applications.
As the visualization field in itself is cross-disciplinary, we also did not examine domain-specific journals that provide applications and real-world impact (see an excellent review of visualization uses in studying the human connectome by Margulies et al. [30] ). Reproducibility. We have released three data collections and our CNN models. The main contribution in this work is the VIS30K image collection and ground-truth bounding box types and locations of all images released through the IEEE dataport at DOI 10. 21227/4hy6-vh52. Metadata for our VIS30K dataset is accessible via a public Google spreadsheet (go. osu. edu/vis30k). The 13K training and validation data of the synthetic pages and their ground-truth and the text and image corpora are accessible via go. osu. edu/vis30ktrainingdata.
D. ADDITIONAL USE-CASES In the paper we present three interfaces in an interactive webbased tool, VIN, that allows the general public to perform their own analyses on the entire 30 years of IEEE visualization image datasets. We show in this part example questions the VIN tool can help answer. What are the illustrative visualization techniques in literature? Looking at images allows scholars to "see" the techniques invented over the years and obtain a gist of the development of techniques over time. Fig. 8  shows an exploratory process for someone, here Jerry, who has taken a visualization class in graduate school to explore techniques related to "illustrative visualization". Jerry puts "illustrative visualization" in the query by author keywords and ses that the tool returned 243 figures and 13 table images ranging from see-through views to distorted rendering to depth-based techniques.
Jerry first observes that most of the early techniques were about transfer functions and then newer techniques address interactive exploration and augmented depth perception (Fig. 8 (a)-(b)). The most recent work in 2019 is different enough to catch Jerry's attention in that only one paper at InfoVis was about non-spatial data (Fig. 8(a) ). Wondering what that paper is about, Jerry switches to the paper-centric view to learn that it was about setting parameters in a high-dimensional space. Apparently this paper has a novel use of terminology compared to other papers, which largely focused on spatial data representation. Curious about why he did not see Ebert and Rheingans' work on shading-based illustrative visualization, Jerry tries a similar term 'non-photorealistic rendering' and added 'Penny Rheingans' to the author's name field; now it returned several of Rheingans' and her students' work (Fig.
Carpendale published several focus+context applications in the area of biology. Interested, André decides that he should read these papers to learn about the details. What visualization applications center around quantum physics? For Emma, a scholar whose goals are related to examine complex structures in quantum physics data, our VIN tool is a resource for research techniques she could adapt or reuse rather than reinventing the wheel. Emma knows that she could query "quantum" from the authors' keywords or more specific terms in abstract and title that target specific design choices (e. g. , showing the data with line or volume rendering) or stylistic decisions (e. g. , color space and line styles) (Fig. 10 ). She is interested in the most frequent visual encodings (e. g. , what data attributes are mapped to which marks and channels) and best practices (e.
[23]paper DOI as a unique identifier to cross-link to other databases such as VisPubData[15], KeyVis[16], and the Practice of Evaluating Visualization[17],[23]. B A thumbnail of each image, which is a low-resolution version of each image extracted from the paper. This provides a gateway to data analyses, e. g. , through Google CoLab. C The image type: either figure (F) or table (T). D An image link that points to its web storage address where a full-resolution version is accessible through IEEE DataPort. Please note that all image files are copyrighted, and for most the copyright is owned by IEEE. Some have creative commons licenses or are in the public domain. Yet other images are subject to different, specific copyrights as indicated in the figure caption in the paper.
ACKNOWLEDGMENTS We thank  Roger Crawfis  for his hard copies of early conference proceedings and  David H. Laidlaw  for conference proceedings CDs. This work was partly supported by  NSF   OAC-1945347 ,  NIST MSE-10NANB12H181 ,  NSF   CNS-1531491 ,  NSF IIS-1302755  and the  FFG ICT of the Future program  via the  ViSciPub  project (no. 867378 ).
[23]paper DOI as a unique identifier to cross-link to other databases such as VisPubData[15], KeyVis[16], and the Practice of Evaluating Visualization[17],[23]. B A thumbnail of each image, which is a low-resolution version of each image extracted from the paper. This provides a gateway to data analyses, e. g. , through Google CoLab. C The image type: either figure (F) or table (T). D An image link that points to its web storage address where a full-resolution version is accessible through IEEE DataPort. Please note that all image files are copyrighted, and for most the copyright is owned by IEEE. Some have creative commons licenses or are in the public domain. Yet other images are subject to different, specific copyrights as indicated in the figure caption in the paper. Additional material A.
We apply probabilistic methods that extract information directly from the images at long wavelengths, exploiting the prior information available at shorter wavelengths and providing full posterior distributions rather than maximum likelihood estimates and associated uncertainties as in traditional catalogues. With this project definition paper we provide full access to the first data release of HELP; Data Release 1 (DR1), including a monolithic map of the largest SPIRE extragalactic field at 385 deg 2 and 18 million measurements of PACS and SPIRE fluxes. We also provide tools to access and analyse the full HELP database. This new data set includes far-infrared photometry, photometric redshifts, and derived physical properties estimated from modelling the spectral energy distributions over the full HELP sky.
Open Science The project has been implemented using open science frameworks with the following general principles: • All code is publicly available through a version controlled git repository. • Production code is embedded in extensively annotated Jupyter Notebooks with integrated diagnostic plots. • Every version of each data product is associated with the git commit code for any code used at the time of production These key principles enable rerunning of any section of the pipeline in order to facilitate both verification and extension of the work by external researchers. By using Jupyter notebooks to document all the processing on GitHub, all the information about data quality is readily available and the code can be rerun with future additional survey data.
Tools The HELP philosophy is that astronomers can easily carry out their scientific investigations without a high degree of instrument or survey specific expertise. We have defined some specific scientific use-cases which should be achievable at the end of the project. Our target is that these recipes could be used by a postgraduate student to produce meaningful scientific results. Our intention is that all scientific results from the team are easily reproduced using these tools. Some of these tools are database operations. Our database is VO enabled with ADQL interfaces. Some tools are traditional client/server interfaces. Other tools are developed to provide containers (e. g. Docker) that the user can download and run on their own CPU resources. We supply extensive examples and documentation to aid the uptake of all the tools developed and presented here.
2012 and HerS). This image was built for HELP from the processed SPIRE time-lines using the HerMES SMAP processing. production of the master list are presented in  Shirley et al. (2019)  and through the code itself on Github. The master list is central to the HELP pipeline and data products. As the master list progresses through the pipeline, some fraction of objects satisfy the criteria required for the next additional processing stage. For instance, if there are not sufficient optical photometry points then the photometric redshift (photo-z) calculation is likely to fail and the object will not have a photo-z, and so cannot be used for SED fitting. Likewise, when XID+ is run, some objects will have low signal to noise so will not be 'detected' and will not be used for SED fitting. We aim to model each of these selection effects so that the full selection function can be understood as accurately as possible.
HELP XID+ pipeline HELP uses XID+ to carry out forced photometry on the Spitzer MIPS and the Herschel PACS and SPIRE images to produce catalogue fluxes for the HELP database. Our prior source list for these images are constructed using two different pipelines, which we describe in detail in the following paragraphs. For flux priors, we use uninformative flux priors (i. e. uniform flux prior bounded with reasonable limits derived locally from the image) to enable an unrestricted range of analysis with the HELP data products. More informative prior information would be preferable for specific science projects and is a powerful approach to extract more information out of the data (e. g. Pearson et al. 2017b Pearson et al. , 2018)) , however their use must be fully understood and taken into account, such that they are more suited for bespoke projects than for a data product.
Merging of spectroscopic redshift catalogues To merge the individual catalogues, we match each catalogue sequentially by using STILTS  (Taylor 2006a ) to perform a sky match in a radius of 1-2 and checking up to 5 , where the radius is chosen to give the optimum matches. By performing a manual check of objects close to the matching radius the code can be modified so that any above/below the matching threshold should be merged/split. For any redshifts that have been found to be a match between the merged catalogue and the new individual catalogue the redshift with the higher Q flag is kept. For the case where both Q flags are ≥ 3 a check is performed to see if the redshifts have a ∆z < 0. 01 and < 5% (for lower redshifts). If the two reliable redshifts disagree, a manual choice is made to decide the best redshift based on available information (i.
HELP Derived Photometric Redshifts Photo-zs for the prime HELP optical datasets are estimated based on the method presented by  Duncan et al. (2018a)  and  Duncan et al. (2018b). The approach combines multiple templates and machine-learning estimates to produce a hybrid consensus photo-z estimate with accurately calibrated uncertainties. This method is only possible on fields with sufficient spectroscopic redshift samples as described below. We refer the reader to the original papers for a detailed discussion on the motivation and implementation. Below we summarise the implementation of this method for HELP. As in  Duncan et al. (2018a) , three different templatebased estimations are calculated using the EAZY software  (Brammer et al. 2008 ) with three different template sets: one set of stellar-only templates, the EAZY default library  (Brammer et al.
int/web/xmm-newton/xmmsl2-ug z 1,median or z 2,median ). To present a measure of redshift uncertainty within the HELP catalogues we also then present the lower and upper boundaries of the 80% HPD CI peaks (i. e. where the P (z) crosses the threshold). In the HELP catalogues, database and the subsequent analysis (e. g. Section 4. 7), photo-zs values are taken to be z 1,median.
Future HELP Photo-z Plans The photo-z estimation methodology applied to majority of HELP fields and sources (i. e. Duncan et al. 2018a; Duncan et al. 2018b ) was developed with the aim of providing the optimum photo-z estimate given the available data, regardless of source type. However, the requirement for template fitting (and machine learning), hierarchical Bayesian combination and detailed posterior. This difficulty is particularly acute in the largest and most complicated datasets (e. g. Herschel Stripe 82) where significant high-performance computing resources were required for the runs. Going forward, we will therefore aim to move to a more scaleable and automated approach that can exploit the infrastructure provided by HELP. The ingestion of the homogenised optical datasets into the HELP virtual observatory now opens up possibilities for optimised machinelearning based photo-zs that combine all spectroscopic redshifts available for a given combination of optical to near-IR photometry, regardless of field.
Physical Modelling Physical modelling of master list sources with both a redshift estimate and FIR data was carried out using Code Investigating GALaxy Emission 7  (CIGALE,  Boquien et al. 2019). We refer to  Burgarella et al. (2005) ;  Noll et al. (2009) ;  Boquien et al. (2019)  for a detailed description of the code, and  Ma lek et al. (2018)  for a detailed description of the range of parameters used within the HELP fits but describe the key features here. CIGALE conserves the energy balance between dust absorption in the UV to near infrared domain and emission in the mid and far IR when generating SEDs. The stellar emission is constructed from composite stellar populations from simple stellar populations (SSP) combined with flexible star formation histories (SFH). Attenuation curves are then applied to estimate the fraction of energy from stars and gas absorbed and re-emitted by the dust using a dust emission template.
CIGALE is a highly flexible code, containing multiple different modules for SSP, SFH, dust attenuation, dust emission and AGN component for users to apply or they can add their own modules. For the HELP project we selected low resolution  Bruzual & Charlot (2003)  single stellar population models, assuming a Chabrier (  2003 ) initial mass function. SFHs are chosen to have the so-called delayed τ parametrisation (∝ t e -t/τ ) form, with additional bursts to allow the SFH to model starburst populations on top of older (potentially quiescent) populations. The SFH is defined as: SFR(t) ∝ SF R delayed (t) if t < t0. SF R delayed (t) + SF R burst (t), if t ≥ t0. (5) with t0 being the age of onset of the second episode of star formation. We perform SED-fitting runs with Charlot & Fall (2000) model as the dust attenuation recipe.
Constraining power from multiple measurements at similar passbands will dominate over other bands. When multiple measurements are available in similar passbands we take the deepest only as determined by the signal to noise ratio. On average, based on the selection described above, we estimated physical properties of 1. 7 million galaxies or 1% off all objects in the master list. Table  2  shows the summary of the catalogues in each field, including the number of sources for which Mstar, L dust and SFR were estimated. To make HELP's SED fitting easily reproducible, we published a dedicated version of CIGALE, cigalon 8  which contains the modules and parameters used to fit HELP's galaxies. This version can be found on the main CIGALE page. As it was shown in Ma lek et al. (  2018 ), CIGALE's implementation of the energy balance enables predictions of L dust for standard IR galaxies, which preserve energy budget, based on the UV to near infrared data only.
Here we would like to stress that CIGALE cannot estimate monochromatic fluxes with reliable uncertainties, but only the total value of L dust. We used the predictions as priors for the IR extraction pipeline XID+. A similar method was used by  Pearson et al. (2018)  for the star forming galaxies beyond the Herschel confusion limit. To predict the L dust for galaxies without IR measurements, we run cigalon using the same parameters and methodology as described above but without the AGN module as without mid-IR data we are not able to constrain a reliable AGN component.
Database structure and access HELP data is distributed through the Herschel Database in Marseille 9 (HeDaM) in addition to the Virtual Observatory at susseX 10  (VOX). The former allows access to all raw data for reprocessing or direct handling. The latter permits fast queries over the full HELP area to generate samples for scientific analysis. Data accessed by code on GitHub can be found via its relative link on HeDaM such that the user can download the entire database and perform a full rerun. We also include meta data files in the Git repository with links to the corresponding data files.
HeDaM catalogues and images On HeDaM each data product is organised by field. For each field there is a final catalogue containing all the information from the optical fluxes to the infrared, the redshifts and the physical parameters derived with SED fitting. The HELP Herschel SPIRE and PACS images are also present in addition to the blind sources associated with each. The database is designed to run across the entire HELP sky. Many scientific users will be interested in an individual field of interest. We have therefore provided overviews of each field to help a new user become familiar with the data presented there. HeDaM also provides everything to reprocess the data exactly as described here. This facilitates rerunning the HELP work while changing some parameters or, for instance, adding a new optical catalogue to the process.
Virtual Observatory The Virtual Observatory at susseX (VOX) is a virtual observatory server built using the German Astrophysical Virtual Observatory (GAVO) DaCHS software: Data Center Helper Suite 11   (Demleitner et al. 2014; Demleitner 2018 ). VOX contains both the images and the catalogue data. Images are available through the Simple Image Access Protocol (SIAP). In particular, VOX makes is possible to get image cutouts at a given position. The catalogue data is gathered into a single table across all the coverage that users can query using the Table Access Protocol (TAP) with compliant software like TOPCAT  (Taylor 2005) , STILTS  (Taylor 2006b)  or PyVO 12. This allows users to make sophisticated queries or to remotely crossmatch their catalogues with HELP data. The total catalogue containing all photometry measurements across all fields has around 500 columns where each source may only have flux information in a small subset of these bands.