dataesr/scientific-paragraphs-categorization huggingface dataset:
Sometimes this material is given as a data annex after the text, where the PhD thesis functions a kind of -data vehicle‖. An example: a 2012 PhD thesis in law from the University of Milan contains two annexes, one with questionnaire raw data, and another with tables of survey results, on more than fifty pages. The thesis is available without any restriction in the Milan institutional repository AIR 12 , the datasets are recorded with a unique identifier (DOI) and harvested by the DataCite content service 13 but as they are part of the text file in PDF, they can not be reusedthey are open but not exploitable. Another example is a 2014 dissertation in architecture at the TU Delft, with one DOI attributed to different datasets (again, fifty pages of statistics, measures etc. ) which are part of the PDF text file 14.
Sometimes the text and data files are submitted separately, and the PhD thesis plays the role of a -gateway to data‖. This is the case of a 2009 PhD thesis at the University of Sheffield in media and communication sciences 15  -two files with research data have been deposited on Figshare 16 with attribution of a DOI which allows for retrieval with the DataCite content service 17. Here, the research resultsthousands of labels and values in ODS spreadsheet formatare not only openly available but can be reused, reanalyzed, merged with other data. In a more systematic way, the US Inter-university Consortium for Political and Social Research (ICPSR) at Ann Arbor, Mass. , started to link datasets to PhD theses from the ProQuest database  (Walker 2011). Often, ETD will be somewhere -in-between‖ with some data integrated in the text (tables, graphs, illustrations) and others published as annex.
One example among thousands: a 2010 Master thesis in physics from the University of Magdeburg and DESY, with several figures and tables in the text and an annex with a complete source codeall in PDF, open, with a DOI for datasets (for the code, not for the figures and tables) but not reusable 18. Text and data mining are necessary to identify and exploit all this information, and the dissertation is at the same time -data vehicle‖ and primary data. Obviously, electronic theses and dissertations can contribute to eScience in each way described above: they can be exploited as primary data source, they contain (potentially) interactive content, and they are linked to datasets and research results (see Figure  1 ). But linking does not necessarily mean providing access. Supplementary material from ETD does not often even meet the criteria of Berners-Lee's and Shotton's first level of openness, i.
CHALLENGES Up to now, these items appear more or less out of scope for eScience and open data strategies. Surveys on small data (see for instance  Simukovic et al. 2014)  do not mention the link with theses and dissertations. Scientists report that they produce research results through observations, experiments, simulations, surveys, interviews and so on, but they report this production as completely separated from their publishing activity. There is no reliable and representative empirical evidence of the volume, distribution and characteristics of datasets and other files submitted together with ETD, except for a recent study at the University of Illinois at Urbana-Champaign that evaluated the part of ETD with some form of supplementary file since 2010 as 3%, in particular in life and physical sciences, most often with data, protocols or codes in a wide variety of formats, such as PDF or Excel  (Shreeves 2013).
However, the study insists on the preliminary nature of these findings and concludes that more structured description of supplementary files are needed, together with more investigation into when supplementary files are included and when they are not. Providing access to research data related to digital PhD theses is a challenge for academic libraries and infrastructures. The following overview puts the focus on three crucial topics: which could or should be the information system that fits best? How to facilitate the retrieval of these datasets? And which are the legal conditions for their dissemination, access and reuse?.
WORKFLOW AND SYSTEM ARCHITECTURE Should ETD and related datasets be processed together or separately? Should they be disseminated on the same or on different repositories? Should they be preserved on the same or on different servers? How should they be linked? A workflow comparison of the French STAR and the UK EThOS infrastructures with ProQuest's global schema  (Walker 2011 ) and the TARDis project at the University of Southampton  (Simpson & Hey 2006 , Hey & Hey 2006 ) suggests that there may be no unique ideal solution but different options, depending on legal and technical conditions. For instance, research data can be handled and disseminated via centralized data management systems or decentralized collaborative systems (social networks) with reduced costs and customizable interfaces  (Wang & Liu 2009).
Data repositories can be institution-based (such as most ETD repositories) but also run by third-party service providers, such as Dryad, Zenodo or Figshare. One size does not fit all. This is the first point. The second point is that such heterogeneous datasets cannot be compared to the kind of big data produced by CERN and other large facilities but are more similar to personal data, even if the main challenges are roughly the same, covering issues of up-dates, enrichment and reuse, submission policy, handling of copyrighted material, standards, technical infrastructure or long-term preservation. The point is that the ideal system architecture should combine features of personal data stores (small data) with characteristics of institutional information systems (big data). For instance, how to decide on the inclusion and deposit of supplementary files? In big data repositories with automatic input, these decisions are taken ad hoc and upstream.
Because of the link to copyright protected documents, and because of the personal nature of these research results, the same strategy cannot be applied here, and decisions have to be taken on a different level, probably case by case. But on which criteria? Third point: Because of the specific nature of data and supplementary files (see above), it appears appropriate not to store text and data files in the same repository but to distinguish between document server and data repository and to deposit text and data files on different platforms, or at least to separate them on an early stage of the workflow and to handle them in different information system environments (see Figure  2 ). For instance, Sun et al. (2011) developed a database and associated computational infrastructure for datasets with different metadata submission forms for different topics.
Supplementary material should not only be available as appendix or illustration to the related ETD but also extractable and reusable without link to the thesis, as an independent dataset and interconnected to other data. In the Berlin survey cited above, scientists seem to prefer a local data repository (department, laboratory) to other solutions which means that they are realistic enough to require a combined institutional and disciplinary environment for their data  (Simukovic et al. 2014). At the first EUDAT conference, Laure & Livenson (2012) presented a draft service for small datasets called -Simple Store‖, based on the CERN Invenio technology and designed for the -long tail of small data and the researchers/citizen scientists creating/manipulating them (with) YouTube or DropBox like functionality (and) inclusion of data presentation and discussion layer (players, feedback, etc.
To be fully accessible, sharable and reusable over the time, research data need curation and management throughout their whole life cycle  (Neuroth et al. 2013 , Schubert et al. , 2013). Formats: Because of their specificity and because of their different service environment, datasets and ETD should be described by different metadata sets. In particular, metadata formats on datasets should be adapted to the specific needs of the ETD environment. One part of both metadata sets will be shared, and these metadata should be generated whenever possible from the ETD metadata, instead of creating new data. CERIF, the European standard format of current research information systems (CRIS) can be helpful as exchange format Data Data Data.
Data repository between data and text repositories, facilitating incidentally the integration of ETD and related datasets into research evaluation  (Schöpfel et al. 2014)   19. Standards: Standards are necessary for interconnections and interoperability. In the particular case of ETD and related data, this means standardized metadata for both. Qualified Dublin Core metadata is the minimum for ETD (see for instance  Park & Richard 2011) ; more detailed and appropriate are standard formats like TEI, MODS (Library of Congress), the UK ETD Metadata Core Set, TEF (France ETD infrastructure STAR) and above all ETD-MS (NDLTD). Each format allows for some more or less controlled and detailed description of accompanying material. Concerning supplementary material, the exploration of standard metadata for datasets and other types of files should build on the ongoing work on metadata for datasets federated by EUDAT (de Witt 2012) or DANS 20.
Adapting those models will probably face one major problem, i. e. the great variety of these small data. Along with research data, these metadata must be documented (perhaps even standardized) and stored with the description of the technical requirements (e. g. to document or even archive hardware and software frameworks). Identifiers: While unique identifiers for ETD already exist and are widely used, especially in national and international ETD infrastructures, the lack of permanent names for archived data and of the attribution of persistent and unique object identifiers to scientific datasets such as an URN, handle or digital object identifiers (DOI) are still a problem. The international project DataCite, member of the Research Data Alliance (RDA), already attributed more than three millions DOI to research data 21.
At present, datasets linked to PhD theses represent less than 1% of the DataCite content service. The International Standard Randomised Controlled Trial Number Register (ISRCTN) recently decided to introduce DOI to all records in the Current Controlled Trials database and to link datasets to publications. So far, no major institution or network appears to have taken the same kind of decision for digital PhD theses. Linking ETD to data: Datasets should be connected to the text via the ETD metadata, not only by attribution of the unique ETD identifier or via an URL address but by means of their own persistent identifier. ETD should mention the existence of related datasets as a specific element of their metadata set, as detailed and controlled as possible. Linking online ETD to their underlying datasets should also be considered in the emerging context of semantic publishing  (Rinaldi 2010 ) and rich Internet publications  (Breure et al.
2011 ). Formats, standards, identifiers and appropriate linking are necessary conditions for data retrieval but they do not guarantee accessibility and reusability. Non-adapted or missing discovery services and functionalities can be bottlenecks on the way to open access to ETD and supplementary data files. Data integration and handling need specific service and workflow management. -Creating a production grid environment poses several significant technological problems related to security, accounting, information provisioning, resource brokering, and data management‖  (Dooley et al 2006). If hosting institutions or service providers want to increase usage and impact of open repositories, they should develop a user-centred process of service development prioritization  (Halbert 2007). According to  Morris et al.
(2005) , -biomedical computing will permit scientists to extract biologically meaningful information from datasets of ever-increasing size, heterogeneity and complexity‖, a prognosis that can be extrapolated to eScience in general and ETD in particular but is conditioned by appropriate metadata, retrieval tools and other data-related services. When asked to indicate services they would like to have for their research data, scientists rank first secured and backed-up storage, followed by advice and guidance on legal issues (e. g. access restrictions, sensible data, licensing) and on technical issues (e. g. metadata, standards, long-term preservation)  (Simukovic et al. 2014). Options for copyright clearance, such as dissemination under a Creative Commons or an Open License can be part of these added value services upstream of the repository.
In order to exploit and explore datasets, scientists will need -the assistance of specialized search engines and powerful data mining tools‖  (Hey & Hey 2006). Open and institutional repositories develop a large spectrum of added value services such as training events, guidelines and assistance, mailing lists, different entry points and separate indexes, several browsing and search options, customisation, reference management such as export of bibliographic listings in different formats, social media tools, multi-dimensional data processing, virtual organizations, location based service, usage statistics, videos or print on demand (see for instance  Bester 2010). Some of them may be relevant for data repositories. In a general way, the specifications for services and functionalities must allow the linking of documents and datasets, while facilitating the independent use and reuse of each item and their exploitation together with other research data (-cross-domain data-mashup‖), including harvesting by other service providers.
This means that the data deposited in the form of supplementary files should be reusable independently of the related thesis (yet clearly linked to it). Another related issue may be the service, support and incentives for authors to publish their data together with their thesis (see  Costello 2009). All these services and functionalities are designed to increase the availability, searchability and retrieval of research results. Some of these developments are possible even with a -very modest programming staff available to deploy on ad hoc projects‖  (Halbert 2007)  while others ask for more investment and resources. On a more general level, the repository services for the dissemination of ETD should be flexible, with a capacity for rapid adaptation; the software should be user-friendly and reliable, perhaps also open to other service providers and integrated in different service environments, such as extended (distant) learning.
LICENSING AND REUSE RIGHTS Recently, a librarian asked the following question on the international ETD listserv: -Suppose that at your publicly-funded university, a student completes a full-length documentary film as part of a PhD in Gender Relations and Social Justice. (…) Does your university allow the student to refuse to put the film in your library, and to retain full and exclusive rights to its distribution?‖ Applying copyright to PhD theses is already rather complicated  (Schöpfel & Lipinski 2013). The legal environment of data and other supplementary materials is not the same as for ETD. Both must be considered independently, even if related and interconnected. Non-adapted licensing or (over) protection by copyright can be legal barriers to their deposit, dissemination and reuse. Linking datasets to the copyright protection of ETD creates a potential conflict with open data policy.
The European Commission and several national governments promote the dissemination of datasets under the minimalist open licence, limited to the attribution of authorship (CC-BY). On the other side, authors and service providers of ETD often adopt a more restrictive sharing policies that prohibit modifications and for-profit use, apply the full protection of the intellectual property law or limit the dissemination to campus-wide access  (Schöpfel & Prost 2013). This is too restrictive to realize the potential for reuse of data and to be in conformity with the wish of the European Commission to make it -a general rule that all documents made accessible by public sector bodies can be re-used for any purpose, commercial or non-commercial, unless protected by third party copyright. ‖ 22 The legal regime for datasets should not be the same as for the dissemination of theses and dissertations.
Murray-Rust (2008)  suggested for open data the following categories:  scientific data deemed to belong to the commons (e. g. the human genome)  infrastructural data essential for scientific endeavour (e. g. in Geographic information systems)  data published in scientific articles which are factual and therefore not copyrightable  data as opposed to software and therefore not covered by Open Source licenses and so potentially capable of being misappropriated.  maps and other artifacts required for communal infrastructure. Some content may be protected by privacy or confidentiality concerns, for instance personal (human) data and sensitive or strategic information, including professional secrecy. Other research results may be subject to specific sui generis database property rights, and sometimes open access policy may be in conflict with legitimate interests (publishing for scientific career) and fear of plagiarism.
In any case, author and institution must reconsider the legal condition of the deposit and dissemination of datasets and other material, but they should do so applying a policy of open data allowing for a maximum of reuse and exploitation. Unlike ETD, datasets should not only be free (in terms of the open access movement) but also -libre‖, i. e. reusable. Because -restrictions in the use of research data directly affect research data curation (they) must (…) be taken into account right from the beginning (in matters such as policies, technology, etc. )‖  (Neuroth et al. 2013).
PERSPECTIVES Open, digital science is work in progress. Along with documents and publications, research data become an essential part of scientific information. Electronic theses and dissertations have the potential to contribute to the emerging landscape of eScience, as -data vehicles‖ as well as -gateways to data‖. Higher Education and research organizations invested into infrastructures, repositories and library systems in order to facilitate the transition from print to digital theses. Today, new investment is needed for the curation of research data produced and deposited with PhD theses. The development of ETD infrastructures, open repositories and eScience makes it possible to find an appropriate solution for the management and reuse of small data produced along with PhD theses. Our paper provides an overview on characteristics, bottlenecks and perspectives of these resources.
Curation, retrieval and reuse would be largely facilitated if this material would be separated from the PhD text files and handled in a different way. This means, PhD theses should be valorised as -gateways to data‖ which implies incentives for the deposit of related datasets and other supplementary files and specific and innovative procedures and workflows in graduate schools and academic libraries. Furthermore, service functionalities from institutional repositories and data stores should be adapted to these specific items, with a flexible, user-centred approach. To increase accessibility and reuse and to avoid isolated data silos with multiple metadata entries, all developments should be as standardized as possible and with maximal interconnectivity, based on the OAI protocol. This means also small data repositories should be, whenever possible, integrated in CRIS environments.
g. urban concentration due to local amenities). In order to be confident in its pertinence for the century to come, NEDUM has been validated on the past 100 years  (Viguié et al. , 2014). The model is based on three key mechanisms: (1) Households choose their accommodation location and size by making a trade-off between transport time to work and the price of real estate. (2) Real estate developers optimize profit by constructing tall buildings where land is expensive and houses further from the city centre. (3) Variable time scales for different processes, for example rents adjust quickly but building construction is slower to external forces. Numerous databases that describe the actual city are needed for the simulation including the transport network and some economic data (e. g. per capita income).
e. it does not require any calibration steps but depends strongly on user (urban planning preferences) defined parameters. It now takes a given amount of urbanization (that is provided by NEDUM every 10 years) into account and relocates it by considering geographical amenities (slopes) and urban planning preferences. The latter includes whether construction is to occur near already built-up areas and roads or disseminated in the countryside or what parts of the landscape are to be protected from urban development. The allocation of new urban areas is also influenced by integrating a map generated by NEDUM that describes the attractiveness of the area under study based on the cost of transport and the price of land. NEDUM and SLEUTH ⁄ were coupled using PALM software 7  and applied to the city of Toulouse.
To derive these data, an analysis of the existing 3D building databases was made in order to express the real city in the form of a map of block-scale LCZs (cf Fig. 3 ). The evolution of these LCZs in the future is driven by the flux of construction of floor area (m2) in each block at ten year intervals. The way the indicators vary as a function of the others is shown in Fig. 4. Note that some information on urban planning scenarios can also influence the future urban forms in the city so that, for a given built floor density, one type of building or another that typify an LCZ type may be favoured.
This is done following the methodology described in  Hidalgo and Masson (2013). This is the approach taken in this paper. Now that our atmospheric models have the adequate physics, the final task is to link these to the city characteristics (as described by the block-scale LCZs), which are generated by the urban development models. This is mainly done through the GENIUS architectural model that links the LCZ parameters to the TEB model. Some of the important data (such as geometry and land cover) are directly associated with each grid cell and its LCZ characteristics. Other data is acquired from architectural expertise that connects built parameters to LCZ class based on current building type, date of construction and likely renovation and future building norms. For example, zinc roofs are associated with old buildings (construction date before 1900) in Paris and non-industrial brick walls are associated with old buildings in Toulouse.
Other scenarios are then defined as variations from this one. The model assessment proved the ability of SLEUTH ⁄ to handle the simulation of several urban structures that reflect urban planning strategies. The model could then be used for forecasting. NEDUM and SLEUTH coupled together were intensively tested on the city of Toulouse. Sensitivity tests assessed the capacity of each of the models to integrate the temporary data produced by the other at an annual time step. Fig. 5  and Table  2  show an example of coupled NEDUM-SLEUTH model performance in simulating a plausible urban pattern compared to 2006. Verifying the ability of the architectural model GENIUS to reproduce the evolution of urban forms at block scale was more difficult because of the lack of building data over a lengthy period (50 years or longer).