dataesr/scientific-paragraphs-categorization huggingface dataset:
INSPIRE only addresses authoritative datasets but archaeologists routinely create spatial data through a range of increasingly sophisticated digital fieldwork techniques. There is no requirement to collate spatial data from different archaeological projects into a single resource. For example, a map of the archaeological landscape may be created and published by applying consistent data standards to transcriptions of individual archaeological sites (Figure  1 ). This map may be used for internal management purposes, published online as part of a heritage portal  (Canmore) , and exposed through a metadata catalogue as WMS or WFS for others to access on their own GIS. Yet all too often data is created on a project by project basis following different conventions and formats, greatly restricting the ability to develop maps of the archaeological landscape.
Within Europeana (Europeana Foundation 2019), CARARE  (Carare 2019 ) acts as an aggregator for archaeological and architectural heritage but the emphasis is firmly placed on the cultural value of objects within virtual collections rather than the spatial content. More recently  data from multiple projects into organised datasets managed as part of core business applications and published online through web-GIS portals. For INSPIRE mandated datasets data is also available as WMS and WFS enabling other organisations, such as environmental consultancies, and researchers to work more efficiently by accessing information directly into their own systems. Although the ARIADNE Portal does not use dynamic WMS and WFS mandated by INSPIRE (in part the attribution is not rich enough), the portal could both signpost and consume those services to deliver truly spatial datasets within a dedicated heritage portal.
Archaeological spatial data tend not to conform to recognised ISO standards. The  ISO Standard ISO 19115-2014 (ISO 2014)  defines the schema for describing geographical information and associated services, including contents, spatial-temporal extents, data quality, access and rights to use  (Shaw et al. 2009: 8). Metadata should be discoverable online so that a user can understand the nature, content and extent of the data. Additionally, there should be enough information to allow the user to explore and assess if the data is fit for purpose. For complex datasets such as remote sensing data, this should include technical metadata documenting the instrumentation and resolution of the capture scale. Finally, exploitation metadata is essential to allow the user to access transfer and apply the data in their own systems both within the heritage sector and for wider environmental benefits.
g. cuts, cut by, etc. ) to another object (another archaeological feature). In fact, archaeological field data, in the relationships between archaeological contexts and other archaeological contexts, finds and methodological constructs, function very much like a complicated graph structure that is at odds with the relational database structures that we tend to use to describe these entities. Instead, the RDF structure of describing these entities in terms of their relationships to other entities allows for easier deep graph traversal and network analysis. The use of Linked Data technologies, however, is by no means simple. The problem of GI literacy may be reduced in the future by the rise of generations of archaeologists that know no other world than the digital, but the technical expertise required to express archaeological data as Linked Data is considerable.
The second area where semantic web technologies can contribute is in describing the semantics, using web addresses. The prevalent way to embed the semantics of data in the data itself is through Linked Data. RDF is often used as a standard to describe the properties and semantics of data in a way that is much more descriptive than just an ambiguous label that is often used in databases, such as "date". It is easy to imagine confusion over the property "date": is this the dating of some find, the time it was collected in the field, when it was recorded in the field recording system, when it was entered in the database, altered in the database or when it was deposited in an archive? Best practices in RDF require you to model your data in more specific terms than is customary in most current (spatial) database systems, allowing users to look up the exact intended use of an instance just by following the links that describe the types and predicates of the data.
Even if two data sets use a different vocabulary to describe the same types of entity, the classes used in these vocabularies can be (locally) equated to or subsumed, allowing the user to query across these data sets using one vocabulary. The development of CIDOC-CRM extensions CRMgeo and CRMarchaeo (ICOM CIDOC 2019c; Nicolucci 2017) is highly relevant here. After its CRM-EH predecessor (CRM-EH 2019), it may be the first internationally drafted metastandard for describing archaeological data using RDF. In the long term, it could go a long way towards solving problem statement no. 2 above: the lack of interpretable data. Also, if developed further, it could serve to help with problem statement 3: the lack in adoption of shared archaeological data standards. However, it is still in a very early stage, and, as part of the CIDOC-CRM initiative, is not a domain ontology, but a top ontology, intended to be implemented in domain ontologies.
The problem has been addressed through PeriodO (2019), cataloguing 'not global period concepts, but specific period definitions: authoritative assertions about the chronological and geographical coverage of period concepts, expressed using machine-readable coordinates (including start and end dates as well as geographic boundaries)' to 'facilitate the discovery of chronologically related data across heterogeneous digital resources'  (Rabinowitz et al. 2016: 44-45). The CIDOC-CRMgeo extension is an ontology extension modeling spatio-temporal aspects of heritage in general. It bridges the gap of cultural heritage modeling with geospatial and temporal properties, opening the possibility for integrating knowledge bases between heritage subdomains. CRMgeo interfaces with a geospatial extension called GeoSPARQL (OGC 2019b), that, in theory, facilitates spatial interoperability.
The GeoSPARQL standard is composed of a vocabulary part and a functional SPARQL extension. The GeoSPARQL vocabulary is highly useful in separating and describing spatial properties (geometries) from their objects (features), where in non-semantic data formats, these separate entities are often confused and concatenated. In GeoSPARQL, multiple geometries can describe the same object, at different functional levels: one can supply a centroid point coordinate for the use of labeling, and a complex multipolygon to describe its spatial extents. The extension links the GeoSPARQL vocabulary to CIDOC-CRM spatial and spatiotemporal concepts. At first sight this is of great value to incorporate cross-domain geospatial standards rather than to re-invent the wheel, but the practical value of both the CIDOC-CRM geospatial and archaeology extensions needs careful evaluation in future work.
The GeoSPARQL functions on the other hand offer little advantage over the range of functionality found in common spatial data infrastructures and GIS, except that it can be used in conjunction with Linked Data. For instance, common functions to calculate polygon areas, reproject to different coordinate systems or return the number of vertices in a geometry are lacking in the current version 1. 0  (Perry & Herring 2012). In considering the CRMarchao and CRMgeo extensions, there is still a large void between an abstract set of top concepts in CIDOC-CRM and their practical implementation as implementation documentation and manuals for the extensions appear to be unavailable.
2017). Other studies are underway, such as in the ArchAIDE project (ArchAIDE 2019), but publications in general on AI and archaeology are still few and far between. However, it is likely that machine learning may have a tremendous impact on almost every data heavy analysis in archaeology, in support systems for These goals are getting within reach using the current state of technology in machine learning. With regards to problem statement 4 -the time-consuming curation of archaeological information, and issues relating to the fragmentary nature of data (e. g. Löwenborg 2018) -the machine learning technologies are likely to be able to play a significant role but with so few practical results it is too early to tell.
Challenges Sustainability As highlighted earlier, efforts to guarantee the sustainable archiving of digital archaeological data have been partially successful at the local and national level, even when the spatial dimension of archaeological data has not been the primary focus. However, even well-established initiatives are still very vulnerable to changes in the political climate. Almost all organisations curating archaeological data do so on the basis of governmental funding, and changes in political priorities can easily lead to loss of datasets if curating these is not part of the legal obligations of administrative authorities at local, regional, national or transnational levels. The Dutch national digital archive DANS, for example, is currently financed by the Dutch government as a partnership between the Royal Dutch Academy of Sciences (KNAW) and the Netherlands Organisation for Scientific Research (NWO), as part of a larger effort to curate scientific research data.
However, there is no guarantee that this agreement will continue in the future, and if so, whether accessing data will remain free of charge, and if DANS will continue to receive enough funding to adapt its services to changing technological and research environments. And the Netherlands are ahead of many other countries in acknowledging that there is a problem that needs to be addressed at the national level. The funding for the German IANUS initiative (IANUS 2019), for example, was ended in 2017 without a follow-up strategy for implementing the project's results. Clearly, sustainability is not just a matter of developing exchange standards and creating repositories, but also of developing infrastructures that have a sound financial and legal basis.
An additional challenge for some spatial data is third party intellectual property rights and copyright for data sourced against national mapping agency or other vendor products. Going forward, openness needs to be built into data creation to ensure ease of re-use. Legacy data presents another challenge. Archive datasets, from paper records to most born digitally, were created without thought of transformation or re-use in a spatial data infrastructure. Traditional approaches persist with most spatial data consigned to an illustration in a project report, fossilising in print the spatial knowledge often acquired digitally in the field. Resolving copyright issues aside, much of this information cannot be easily or accurately geo-referenced and the sheer time and effort required to capture legacy data appears daunting.
However, a pragmatic approach might be to capture the data ' as required' to support other project work or through training programmes. Counterarguments to opening data are familiar. Publishing site locations raises the risk of looting through metal detecting and other activities. There will always be a hard core of determined looters, but risks can be mitigated through education and cooperation. At the same time promoting heritage amongst local communities often raises awareness in their surroundings empowering a sense of stewardship. A middle ground between those interests is often found by permitting access to data at various levels of detail for various user groups. The Portable Antiquities of the Netherlands project (PAN 2019), for example, provides searching and mapping of (metal) items at the municipality level, but will not provide exact coordinates unless the user has a login to the system.
Data quality management Despite the fact that standards for data description and exchange are now well developed and increasingly being accepted within the archaeological community, there is still a lack of common concepts to be used for the evaluation of data quality. The most common quality aspects of archaeological data can be related to four main types of uncertainty: spatial precision, chronological certainty, interpretational accuracy and completeness of documentation  (Verhagen et al. 2016). However, methods to define levels of certainty and to compare datasets with different accuracies have hardly been a subject of debate within the archaeological community. For example, the practice of 'fuzzy' dating of archaeological objects and assemblages (see e. g. Green 2011; Crema 2012) does not fully solve the problem of understanding chronological uncertainties, since it is inevitably based on a subjective assessment of the accuracy of dating, unless absolute dating methods are available.
To rely on absolute chronology, however, seems essential, even when lots of dates are fuzzy. Different online gazetteers (e. g. PeriodO, Pleiades) and ontologies (e. g. OWL-Time) appear to offer good intermediate solutions. From a practical point of view, data quality assessment should start from the definition of an ideal dataset and listing its characteristics. For example, an 'ideal' field survey dataset should be based on dGPS-based coordinates, should follow a field walking strategy that is according to current scientific standards, and employ a standardized and accepted system for recording and describing finds. Already, such a seemingly simple data quality description will be quite complex to define, let alone 'mapping' existing field survey datasets onto it and evaluating data quality on this basis.
Towards realising the value of spatial data in archaeology Multiple agencies/actors create and use spatial data in archaeology, but the potential of that data remains unrealized despite initiatives in the wider geospatial community. This paper has outlined existing approaches/initiatives to delivering value from spatial data. The strengths and weaknesses of the different approaches are summarized in Table  1. The two key approaches outlined in this paper address different aspects of realizing the spatial value from archaeological data. Development of consistent spatial data standards, following the INSPIRE model, requires effective data management through the definition and implementation of appropriate data standards and technical specifications across a range of archaeological spatial datasets.
This model works well where there is a requirement to publish defined datasets, including spatial extents but does not address the richness and variety of the archaeological data behind the mapped content. Semantic approaches, based on conceptual reference models such as the CIDOC-CRM and extensions, address interoperability by defining an integrating framework for manipulating data from multiple projects. These approaches underpin research projects, including Pelagios (Pelagios Commons 2019) and ARIADNE, that aggregate data from multiple sources. Although very powerful, the voluntary nature of participation and incompleteness of the data means that data cannot be relied on for decision making purposes. Current initiatives downplay the value of the mapping element to location -the simple X and Y coordinates rather than the full geometries of the archaeology not required for their projects.
Quantitative indicators of data quality Large datasets inherently have their imperfections, due to the effects of combining various sources with different standards of data entry and data management practices (see e. g. Cooper & Green 2016). However, the fear of criticism of less than perfect data hinders some individuals and institutions from sharing their data. It is therefore important that tools for assessing data quality will not be used to shame and blame the original contributors but will act as an incentive to work towards improving data quality. After all, it is up to the future users of legacy datasets to decide whether the data is good enough for purpose, and if it is not, to develop strategies to enhance data quality. The good news is that good curation tools for digital data, such as graphs, are increasingly available, but we also need to find to ways to pay and credit the curators.
Sustainability Many old databases are stored in obsolete (or soon-to-be outdated) software formats, and only archived on media like CDs, zip-disks or even floppy disks. It is time to take action now to make sure that old data sets will not be lost forever within a few years' time. In order to guarantee sustainability of legacy data it is first of all important to have a good overview of what is there, but what isn't shared yet. Data owners need to join forces to investigate the size of the problem, and to develop strategies for dealing with it. The current shared data initiatives are the frontrunners, and we need to make sure that the practices developed there will be followed by data owners around the world in academia, government and private institutions. Publishing data papers and documenting good practices seem to the most effective way to achieve this, since topdown solutions tend to be very vulnerable to the financing models applied and are often not distributed more widely because of institutional barriers.
Involving the archaeological community Involving the wider archaeological community in working with linked data and machine learning requires showing that the advantages far outweigh the investment in understanding and applying the technologies involved. This proof should come from a careful comparative evaluation of these technologies in controlled experiments on a wide range of archaeological information types and archaeological projects, in a study that not only shows that the semantic models can correctly describe information, but that there is also a distinct advantage to this when compared to a baseline of common digital methods of representing archaeological information. Training can be organized through international initiatives, and should involve collaboration with computer scientists, as undertaken during the ARIADNE project.
Involving data owners and curators Existing approaches to data collection and curation are illsuited to the digital age. Compartmentalised roles of data creator and curator and archivist hinder maximising the potential of born digital data in general and the need to collate spatial data from multiple sources is recognised. The bigger map of the archaeological landscape has been lost to the immediacy of project-led archaeology lacking consistent data standards. Harnessing the potential of spatial data requires a collaborative approach through developing networks across the profession to help exploit the full potential of digital databases so that the data can be shared with other policy domains and linked to societal challenges -all sub-themes within the Amersfoort Agenda (Europae Archaeologiae Consilium 2015: 15-23).
Authoritative datasets, such as designations, tend to follow well-defined schemas within national jurisdictions but are not easily interoperable across boundaries. Primary data collection from fieldwork is generally not standardised and requires harmonising across contributors. This raises the question of whose role is it to pull together these data which may eventually be deposited in a monument record or an archive. Organisational structures of archives adopt a hierarchical approach to tidying specific projects into folders within specific collections and are not open to reengineering and amalgamating data into datasets let alone publishing that data online through a range of web services. Delivery requires co-ordination through a thematic Spatial Data Infrastructure setting out the framework, policies, standards etc.
Conclusion As this paper shows, the potential value of spatial data in heritage is currently not being realised. Beyond the fundamental issue of ensuring the long-term preservation of digital data in general, there is a lack of recognition of the value and potential of spatial data held in reports and datasets, notwithstand-ing the obvious benefit of standardising and sharing spatial data for research and to inform environmental policies and activities that may impact the cultural heritage. The technical solutions exist from the Discovery Metadata, Web Map Services and Web Feature Services mandated by the INSPIRE Directive or through Linked Data approaches. However, solutions require capital investment in delivering change and a commitment to maintain services well into the future. With Linked Data, technical barriers constrain their implementation outside research institutes.
The lack of coordination in creating a sustainable future for spatial data -actual or legacy -is a major challenge for archaeology information management. The datasharing envisaged by the Valletta convention remains limited mostly due to fear of looting, intellectual property issues and client confidentiality. Contrast the lack of coordination in archaeology with the approach adopted by geological sciences where the need for, and value of, a standardized approach to spatial data was recognized and addressed from the early 2000s  (Jackson 2007). Now over ten years old, the One Geology portal (One Geology 2019) provides access to standardized spatial data from 113 countries. Multi-national data sharing initiatives, including Pelagios, ARIADNE and ArkeoGIS, are very much the exception in archaeology.
The INSPIRE Directive should form a blueprint for developing an SDI for cultural heritage datasets to demonstrate the need for and potential of a coordinated approach not only for archaeologists but the wider society. This will inform the evidence base to develop a manifesto for implementing a spatial data strategy for archaeological datasets. One channel for preparing a manifesto and strategy would be establishing a working group on spatial data under the auspices of the EAC, ideally jointly with the EAA and Computer Applications and Quantitative Methods in Archaeology.
Acknowledgements This paper developed from presentations held by the authors at the EAA2017 conference in Maastricht (30 August -2 September 2017) in the session " Mapping our heritage. Towards a sustainable future for digital spatial information and technologies in archaeological heritage management ", organized by  Philip Verhagen ,  Niels van Manen ,  Loup Bernard  and  Isto Huvila. Some  of the presentations are available online (EAA 2019). Verhagen and  Van Manen  gratefully acknowledge the support of CLUE+, the  Research Institute for Culture, Cognition and Heritage at Vrije Universiteit Amsterdam  in organising this session. This paper was further made possible through the support of the  COST Action  ARKWORK (2019). Huvila's work was partially supported by the  Archaeological Information in the Digital Society project (ARKDIS)  project funded by the  Swedish Research Council  Grant  340-2012-5751.
There appeared to be a link between absence of jaw pain and a marked reduction in the heart rate as reported on the SatScreen studies. After 6-8 weeks and once patients reached the identified horizontal treatment range of 5-7 mm, SatScreen software (Patient Safety, Inc. , Columbus, OH, USA) was employed with a Minolta 300i HRPO equipped with the SR-5C finger clip probe (Konica Minolta Sensing, Inc. , Osaka, Japan) for three nights of a Minolta 300i/SatScreen (300i/SS) study with the MAD in place (Figure  3 ). The 300i and SR-5C can store up to 300 h of data, peripheral capillary oxygen saturation (SpO2) memory resolution is 0. 1%, 1 s interval measurements are recorded, and are user friendly, not requiring a high level of skill to operate. SatScreen reports provide estimates of the respiratory disturbance index (RDI); SpO2 recorded to 0.