dataesr/scientific-paragraphs-categorization huggingface dataset:
These types of studies have yielded important advances in our understanding of the anatomy of living and fossil organisms (e. g. [10, 16, 17] ), as well as fundamental aspects of their biology, from feeding mode  [18 -20]  to mobility  [21, 22] , development  [23, 24]  and physiology  [25] [26] [27] , as well as developments in taxonomic practice  [28, 29]. Barriers to data sharing and access to specimens can be eroded because data exist as digital files that can be easily copied and readily distributed, allowing simultaneous analysis by multiple researchers  [30]. These attributes should also enhance the verifiability and reproducibility of studies, facilitating the reuse of data and metadata, more in-depth interrogation of any given dataset, and broader-scale comparative analyses through the assembly of large datasets of multiple specimens or taxa.
However, authors of studies involving 3D digital datasets of biological and palaeontological specimens often do not publish their supporting data, meaning that results and conclusions cannot easily be verified or replicated, and that this potentially valuable source of novel data cannot be further explored  [30]. Ultimately, digital data collected but unpublished are likely to be lost to science  [2, 28]. This also represents a substantial waste of financial and other resources, and places vulnerable original specimens at greater risk of damage or loss, as the same specimens are likely to be reimaged repeatedly to enable different groups of workers to reproduce the data  [28, 31]. Consequently, the promise of 3D digital data has not yet been fully realized. This is not news  [2, 28, 30]. However, most national and international funders have imposed regulations on data access and sharing that are forcing researchers and institutions to finally confront this challenge  [32].
These regulations range from funder-mandated full release of all data  [32] , through declarations that the data are available from authors on request, to no release of supporting data  [32]. When data are released, they are deposited in a diversity of online databases (e. g. BIRN, Dataverse, Dryad, EOL, figshare, GigaDB, Github, MorphoBank, MorphoDBase, MorphoMuseuM, Mor-phoSource, Phenome10 K, Zenodo), institutional and funder repositories, physical museums, and research group websites. At least in part, this diversity of approaches reflects uncertainty about the available repositories for data deposition and the cost of storing the comparatively large files associated with digital imaging-based research. Researchers can also be reluctant to share data that remain part of an active research programme  [33] , or to share a subset of data that is part of a larger, unpublished package.
There is also a lack of consensus and widespread confusion over issues of data ownership and copyright, and conflict that emerges between institutional policies asserting copyright ownership (e. g. public museum or even private collections) and the regulations of funding bodies and publishers with regard to open data. Consequently, sharing or publishing supporting data is often a low priority and has effectively been considered optional when not prescribed by a journal. Partial datasets (e. g. low-resolution visualizations or external surfaces) can be insufficient for reproducibility or even verification. As digital morphology has evolved, most of us in the research community have failed to achieve what might now be considered best practice of open data. The academic world has already taken important steps towards overcoming some of these motivational and practical obstacles.
Platforms for both archiving and sharing data online are becoming more commonplace, and can rspb. royalsocietypublishing. org Proc. R. Soc. B 284: 20170194 handle large file sizes. The standard in molecular biology is GenBank (https://www. ncbi. nlm. nih. gov/genbank/), where sequence data underpinning studies are accessioned before publication. For other data formats, journals and publishers offer a mixed landscape of policies on data publishing that is in need of standardization  [34, 35] , but many not only mandate data deposition-some are even prepared to bear the associated costs, making data deposition easier and ultimately improving science, both in terms of practice and accessibility. There are also initiatives to integrate data submission with submissions to peer-reviewed journals, requiring (or at least allowing) the submission of data in the article submission process and enabling reviewers to examine supporting data as part of the review process  [36].
However, collectively, these initiatives have not been integrated  [34] , and they have not yet translated into common practice within many subdisciplines in biology, palaeontology and anthropology. If a consensus can be established among authors, repositories, journal editors, peer reviewers and funding agencies, there is the prospect of finally realizing the potential of digital morphology in the open-data era. Here, we make recommendations on the nature and extent of essential and recommended best practice datasets that should be made available to support scientific publications using 3D digital datasets across biological sciences (summarized in tables 1 and 2). We review the requirements of associated metadata, discuss the current range of repositories available for such studies and comment on issues affecting their utility.
Publishing tomographic data A range of methods exist for studying 3D specimens through the creation of two-dimensional (2D) image stacks (i. e. tomography), including X-ray CT (encompassing medical CT, micro-CT and synchrotron tomography), MRI, neutron tomography, optical tomography, histological microtomy and physical tomography  [1, 3, 4, 37, 38]. All of these techniques generate datasets consisting of up to several thousand parallel sections or slices (tomograms) through a specimen, with each tomogram represented by an image file. Various techniques exist for the construction of 3D digital models from sets of tomograms  [1].
(a) Data essential for scientific verification (i) The image stack Image stacks are the starting point for most tomographic studies. These provide immediate insight into internal and external features, and form the basis for any subsequent construction of 3D models. Image stacks exist in a range of non-proprietary file formats, but the most common include DICOM, TIFF, JPEG, PNG, VOL, RAW and BMP  [39]. All such files can be opened and viewed in free software such as IMAGEJ, DRISHTI, SPIERS, HOROS and 3D SLICER  [40] , and can be converted into different formats, although this can be more difficult with DICOM files, which exist in a multitude of sub-formats, not all of which can be handled by all software. For most purposes, TIFFs (16-or 8-bit) provide the best balance of accessibility, file size and data quality (lossless compression), but any lossless, standard image file-types are sufficient.
(ii) Metadata An image stack alone will not contain all the information necessary to make full use of the data. For example, scale is only preserved if the resolution (e. g. voxel size or slice spacing) is encoded in the files, and for some datasets slice spacing is not constant and requires per-slice documentation. In the case of DICOMs, this information is typically retained within the file or can be added to the file with a header tag editor (e. g. IMAGEJ). Otherwise, a text file detailing the voxel or pixel size and slice spacing is the minimum necessary information that must accompany publication of any image stacks. Additionally, metadata information should include full details of how the images were acquired (including scan settings), and further information on data copyright, repository and accession of specimens scanned and, if appropriate, comments on preparation or specimen storage for biological specimens (table  1 ).
(b) Additional data required for best practice (i) Prepared datasets While some tomographic datasets are reconstructed as 3D models without any modification or markup, this is unusual. Most datasets are subjected at least to segmentation, the semiautomated or manual differentiation of voxels (3D pixels) into distinct regions-of-interest (using, for example, 'label fields' in AVIZO or 'masks' in SPIERS). Some datasets also require semi-automated or manual modification of the data (e. g. through brightness modifications) to better separate specimen from background (we term this 'editing'). These processes involve a degree of subjective interpretation; this is especially true for palaeontological datasets, which are often very noisy and can require extensive manual intervention to extract maximal information from the original data.
Thus, publication of the original tomographic dataset and final 3D model may not be sufficient to enable other researchers to assess the association between the two. Segmenting and/or editing a tomographic dataset can be very timeconsuming and therefore difficult to reproduce in practice; without access to prepared datasets, most secondary users would not be able to fully interrogate the data underlying a Table  1. Summary table of recommendations for types of data files that should be published in support of published articles. Everything in the 'essential' column must be provided to enable reproduction of the study (assuming the information about how the 3D model was produced is sufficiently detailed). By contrast, the 'recommended' column represents our suggestions for improving the transparency of the process and should be provided where possible (i.
The most critical data are the final 3D surface files, which may be fused from the original component meshes (e. g. in STL, PLY or OBJ formats)  [39]. In cases where the surface texture (i. e. colour information) is directly relevant to the outcomes of a study, data publication -all the data required to replicate and verify a published study must be made available immediately upon publication -published data must include original image stacks (for tomography), final 3D models (for tomography and surface-based methods), landmark data (for morphometrics), and files containing details of the analysis set-up and parameters (for functional analysis); metadata outlining how these data were collected and processed, together with information on copyright and details of the original specimens under study, must also be provided -additionally, as best practice, original capture data (for surface-based methods), unregistered images (for optical and physical tomography), prepared datasets (for tomography) and results files (for functional analysis) should be provided -data files should ideally be published in widely accessible standard formats, such as TIFF for image stacks, STL or PLY for 3D models, and TXT for metadata; however, where no standard format exists (e.
g. digital watermarking) could be implemented -data producers should devise a strategy for meeting the costs of long-term data storage (e. g. applications for external funding) at an early stage in their research; in some cases, costs may be minimized by reducing file sizes using lossless data compression data reuse -data producers should provide a statement of intent outlining how they intend to exploit their published dataset over a short specified time frame (e. g. six months to 1 year); other researchers are free to reuse these data for other purposes immediately following publication and for any purpose (within the restrictions of the copyright licence) after the conclusion of this stated time frame -data users should contact data producers to discuss research plans in case of overlapping interests; where appropriate, this may include collaborative projects leading to joint outputs (e.
g. publications) -data users must credit the original published dataset upon reuse; journal editors and reviewers should ensure that this practice is correctly followed in all relevant publications (ii) Original capture data The photographs or data captured by the scanner or the 3D data generated by the photogrammetry software allow verification of the processes used to generate the model and should be included as best practice. For 3D scanning, in some cases it may only be feasible to release the raw data in proprietary formats but, where possible, widely compatible (e. g. STL) surfaces should be exported. For methods that involve the digital alignment of different aspects of a specimen, or significant manual intervention in the model construction, the unfused data should be released as the accuracy of the original alignment may be of variable quality.
(a) Data essential for verification (i) Morphometric data For morphometric approaches, the original landmark coordinates and the rules defining landmark location should be provided as these constitute the raw data for the morphometric analyses. For 2D landmark data, a TPS file or similar format links landmarks to their constituent images. Where 3D landmark data points are collected via a 3D digitizer, it is common practice to tabulate the specimen number of the digitized specimen. Where the analyses are based on 3D surfaces or digital models, it is desirable that the models (surface or volume) used in the analysis should be published in an accessible format (following the guidelines outlined above). (ii) Downstream functional data Functional analyses typically convert 3D digital datasets into proprietary formats for specific methodologies, such as FEA, CFD and MDA.
(iii) Project files or metadata Specialist software has the disadvantage that it outputs data in proprietary file formats that may not be widely accessible to many potential users. For morphometrics, a text file detailing any corrections or transformations applied to the data and an explanation of the analyses should be published. If the morphometric analysis is conducted in the R environment, an annotated R script is a convenient solution. For 3D functional analyses, the (usually proprietary) files containing the analysis set-up and parameters, either with or without the results files, are required for model verification. This addition enables a user with access to the appropriate software to replicate the analyses. Full metadata should be provided with details of processing techniques used to generate the final model, as well as a description of any parameters specified by the user in the analysis (table  1 ).
(b) Data required for best practice (i) Project and results files Analytical techniques used to investigate the function and biomechanical performance of 3D modelled taxa will produce a range of additional digital data, which should also be made available in order to replicate studies. In the case of FEA, programs use volumetric meshes consisting of a finite number of elements. For MDA and CFD, formats such as the parasolid standard are often essential to perform the analyses. Further parameters and boundary conditions are then defined in specialist software (e. g. ABAQUS, ANSYS, STRAND 7, ADAMS, OPEN-SIM, GAITSYM, COMSOL). Ideally, both the model set-up as well as the result files would be published alongside a study. For commercial packages, viewing software is sometimes available which allows the display of models and results files, but no additional analyses.
Data repositories Researchers have a responsibility to ensure that all of the data necessary to reproduce a published study are made rspb. royalsocietypublishing. org Proc. R. Soc. B 284: 20170194 available. As explained above, for 3D digital datasets these data may include original 2D images, prepared/segmented 3D images, 3D geometries and relevant metadata. These datasets can be, in toto, very large by today's standards; over 100 GB per specimen is possible in some scenarios, and there may be some instances where single publications utilize huge numbers of specimens, the storage of which is in itself a project. Publishers and other institutions hosting repositories must manage and facilitate access to the data they host, with these obligations persisting into the future, ideally indefinitely.
Digimorph. org pioneered the curation of digital morphological data for in-house scans generated by the University of Texas High-Resolution CT Facility (UTCT), and there are now a number of general and specialist repositories facilitating the publication and dissemination of supporting data at a variety of scales (electronic supplementary material, table  S1 ). Many journals have agreements with such repositories and will cover charges, even for relatively large datasets. In addition, many funding agencies cover the costs of longterm data storage, and many institutions have developed their own data repositories to manage research data generated by their own researchers. Out-moded promises to make data 'available on request' should give way to permanent URL links to 3D image data in biology, anthropology and palaeontology (cf.
(a) Available data repositories A range of repositories are available that cater for 3D digital datasets arising from research in biological sciences (electronic supplementary material, table  S1 ). These can vary greatly in terms of the size and types of data they are willing to accept, as well as the cost of storage. In some cases, the choice of repository may be prescribed by the funding body or journal, but this decision will most often be made by the researcher. Modern facilities for publicly sharing datasets include national data centres (typically supported by a research funding body; e. g. RCUK data centres), multidisciplinary (e. g. Dryad, datadryad. org; figshare, figshare. com; MorphoMuseuM, morphomuseum. com; MorphoSource, morphosource. org; Phenome10 K, phenome10 k. org; Zenodo, zenodo.
org) or discipline-specific (e. g. XROMM, xromm. org) repositories, and institutional repositories for data produced in-house (e. g. Bristol University's Research Data Repository, data. bris. ac. uk/data; Natural History Museum London's Data Portal, http://data. nhm. ac. uk). It is not entirely clear that all of these are sustainable in the long term. Traditional repositories of physical specimens can also store and disseminate data, and many are moving towards online access to their digital collections.
(b) Necessary standards for data repositories Digital repositories should have the same qualities as repositories of physical specimens, in that they should ensure the long-term persistence and preservation of datasets in their published form, provide expert curation and stable identifiers for submitted datasets, and facilitate public access to data without unnecessary restrictions. However, by their very nature, they should also ensure that the data are discoverable online, provided with unique, permanent and citable reference codes (e. g. DOIs), associated with relevant metadata (e. g. readme text file), and have links to relevant publications and funding bodies  [2, 28]. The specific licence used by the repository should be considered. Many facilities currently use the CC-BY-NC licence, which disallows reuse for commercial activities.
This may be desirable where there are concerns over activities such as selling 3D prints of museum specimens with no benefit to the institutions charged with maintaining those collections. Some data repositories (e. g. MorphoSource) allow users to specify the most appropriate licence for their data. Authors may prefer to choose the CC-BY licence, which is among the most open creative common licences available and has become the standard for open access publication of journal articles. This licence lets others distribute, edit and build upon the original data, even commercially, as long as they credit the original creator. The CC-0 licence (Dryad default) goes further and allows copyright owners to waive all rights. CC-BY-ND is less attractive, as it allows sharing but does not allow the end user to publish derivatives of the data.
3D digital datasets associated with published studies should be verifiable and fully traceable from production to publication, and later republication. One option is digital watermarking, which provides a means of achieving verification of the authenticity and integrity of data, and is imperceptible to the human eye, but also durable in both digital and printed forms, surviving most image edits, file format conversions, data compression, filtering, partial data removal and smoothing. Another option would be to require users to register with the repository before data can be downloaded and used, a practice already imposed by some repositories (e. g. Dryad, MorphoSource). Registration is usually free and open to everyone, but allows the repository to track data access.
(c) Costs When publishing large (e. g. more than 10 GB) 3D digital datasets, it is vital to consider the financial costs, which are typically proportional to the amount of data being stored. Some repositories do not currently charge for accessions (e. g. MorphoSource), but for some, accession charges are not insignificant. The popular online digital repository Dryad (datadryad. org) currently charges $120 per data package of 20 GB plus $50 for each additional 10 GB. Datasets based on synchrotron tomography supporting a single publication can easily run to 100 GB for a relatively small number of scans of individual specimens, and it is possible to envisage future projects, especially synthetic papers and large-scale comparative analyses, generating datasets that are orders of magnitude greater in size.
Publishing such datasets can quickly become prohibitively expensive; many journals offer to fully or rspb. royalsocietypublishing. org Proc. R. Soc. B 284: 20170194 partially cover the costs of depositing digital datasets, but do not have a clear policy for datasets that are hundreds of GB to TB in size. Applications for research funding are increasingly budgeting for data storage costs, but this does not assist projects making use of pre-existing data, or those where funds for data publication are not available. One way of minimizing costs is by reducing the total size of data published without compromising the quality. Cropping of redundant space around a volume representing the specimen is an obvious first step. Lossless compression of individual image files is an excellent route to reduce data storage for image stacks in certain formats.
For example, LZW compression, both lossless and fully reversible, can provide upwards of 40% reduction in file size on eight-bit TIFFs with no evident effect on data quality, but it is not routinely applied. The PNG image format provides a similar level of lossless compression. Most of the JPEG image formats enforce lossy compression that degrades data, and should not be used despite appealingly high compression ratios. Placing files into ZIP archives (e. g. one ZIP file per image stack) also reduces disc space through lossless compression and is more convenient for downloading. However, ZIP and VOL archives are less secure for long-term storage since, if the single file containing a dataset becomes corrupted, the entire dataset will be lost. Corruption of single files within a large dataset is less serious, and at least some repositories have procedures in place to detect and remediate bitrot  [31].
We recommend that unarchived copies of the original data are stored and made available where possible. In our enthusiasm for recycling 3D digital data and easing reproducibility of morphological studies based on them, the environmental costs of storage should be considered. Most datasets will be accessed infrequently and so there is no need or justification for their storage on spinning discs. Many repositories make use of automated tape storage which is stable and comparatively low in direct costs for the same reasons that make it environmentally low-cost.