dataesr/scientific-paragraphs-categorization huggingface dataset:
Licencing The success of an archaeological SDI depends on dismantling the barriers to sharing data between different branches of the profession. Building on the principles of Open Access publications, archaeologists should adopt a culture of Open Data so that data created by one part of the profession can be reused without undue restriction by others. However, retention of sensitive data because of concerns over commercial sensitivity, ongoing research or embargoed data must be addressed through robust access rights management. Spatial data may include third-party content, typically from national mapping agencies, whose intellectual property rights may restrict reuse and needs to be acknowledged in any licencing.
Formats As discussed above, archaeologists still insist on condemning rich data formats to raster formats, which severely limits interoperability  [86]. An archaeological SDI should promote the use of appropriate open data formats. Data captured or created in a vector format should be available for reuse but despite the flexibility of vector digital formats, the final outputs are often visual renderings of the data as images that lack intelligence. These formats restrict reuse with the redigitisation of plans and rekeying of gazetteers being both inefficient and inaccurate. Providing data in reusable formats referencing open data standards would be far more efficient for data curators charged with integrating results into record systems, and make the data more accessible to researchers. In 2013, the G8 summit published The Open Data Charter which recognised that although governments and businesses collect a wide range of data, they do not always share it in ways that are easily discoverable, useable, or understandable by the public  [87].
Acknowledging the missed opportunities caused by data retention in institutional silos, the Charter calls for greater transparency through Open Data to increase awareness, innovation and efficiencies. However, data can take many formats, as illustrated by the incremental Five Star schema for Linked Open Data defined by Tim Berners Lee (Figure  5 )  [88]. This schema is usually expressed for textual data where One Star data is simply data available on the web but with an Open Licence. Two Star data is available as machine-readable structured text in proprietary formats (e. g. , Excel), with Three Star data held in non-proprietary formats (csv). Four Star data conforms to open standards from WC3 (e. g. , Resource Description Frameworks) and SPARQL to identify things so that people can point to your resources, and Five Star data helps you link your data to other people's data to provide content.
Data Transfer An efficient workflow to streamline reporting from field to record is key to delivering an archaeological SDI. As discussed above, online applications already exist in some countries to manage the transfer of data, including spatial extents, on project completion. However, data standards and harmonisation are often only enforced for Protected Sites and other 'managed' datasets created, maintained and published by public sector organisations. The relevant data standards are often not promoted to those creating data through primary research. Nor is there a requirement to collate data from separate projects or organisations into datasets, available as view or download services (Figure Promoting relevant data standards, such as controlled terminologies, during the reporting stage would help improve the quality and consistency of the data and ease the task of aggregating data from multiple contributors.
Approaches to data access range from full open access datasets available online and for download to freemium or tiered data models. For instance, the Norwegian Cultural Heritage Database provides tiered access to data from their national inventory  [92]. Alternatively, web-GIS portals may restrict the level of detail users can zoom into or the accuracy of the published coordinates. Both approaches are adopted by CSIC-The Spanish Superior Council of Scientific Investigations-in their IDEArq Archaeological Research SDI for Levantine rock art, radiocarbon dates and isotope analysis data  [93]. Many public portals only display the location rather than the spatial extent of records. Point data is useful for navigation and generalised distribution maps but does not present a true representation of our knowledge  [56].
Recording Systems As previously discussed, approaches to documenting fieldwork vary from technique to technique and, depending on the organisational structure of field archaeology, country by country. High-level attribution common to all datasets must document what took place where and when, and be supported by more detailed technique-specific attribution. For more technical datasets, such as geophysical surveys and laser scanning  [58, 59] , metadata requirements are already identified for archival purposes, but the simple step of joining the descriptive with the spatial is overlooked. For invasive techniques such as test pit surveys, watching briefs and excavations, separate metadata is required to document the project extents and trenches (typically who, what and when) from that needed to document each feature or context revealed.
Standardising recording practices for excavations delivers a range of benefits organisationally. Aside from developing a flexible, skilled workforce, consistent data standards enable efficient data management and intra-and inter-site analysis. Systems may be standardised on an organisational basis (e. g. , INRAP) or use an available software package. Options for the latter include INTRASIS  [94] , a commercially available software package used in much of Scandinavia and by Historic England, or ARK, a bespoke open source, standards-compliant, web-based toolkit for the collection, storage and dissemination of archaeological data  [95]. However, many smaller organisations and projects have standalone bespoke recording systems.
Vocabularies Formalised vocabularies, or thesauri, are now in common usage in archaeological inventories. They provide a structure to the data and encourage a uniform use of vocabularies to improve querying and information retrieval  [96]. Originally developed for internal databases, many organisations have now published their key vocabularies online in SKOS Linked Data formats. Examples include a range of vocabularies from Britain, published though Heritagedata. org [97]  under the auspices of the Forum for Information Standards in Heritage (FISH)  [98] , from The Flanders Heritage Agency [99], and from PACTOLS in France  [43, 44]. The challenge of multilingual thesauri is being addressed through ARIADNE Plus  [81] , which is mapping resources published in their native language to the hub vocabulary, the Getty Art and Architectural Thesaurus [100], and developing the tools to cross-search multiple datasets in the ARIADNE Portal  [47, 101].
The application of controlled vocabularies to spatial datasets from primary research is less mature than in online databases. The exciting potential generated by new technologies and the democratisation of data creation has resulted in a wealth of data but little adherence to consistency in interoperability, presentation or reuse. Consequently, there is little control or standardisation over terminologies used, many of which have not been formally defined or are incomplete. Quality is further compromised through the level of interpretation applied or the granularity of the terminology used, with more generic or broader terms competing alongside narrower interpretations, occasionally in the same dataset.
If that data is invisible or inaccessible within an archive in the wrong format, it has no value in the emerging geospatial economy. Developing consistent archaeological datasets not only makes managing that data more efficient, but also unlocks economic value through saving time and money. Archaeological data is routinely used in the stewardship of the historic environment through curated spatial datasets which inform a wide range of planning activities. A recent study of the impact of local authority archaeologists in Britain  [105]  estimated that archaeology in development management returned £15 for every £1 spent on planning archaeologists, and contributed £218 million to local economies from commercial archaeology. Up to £1. 3 billion was saved in delay and emergency excavation costs.
The potential for archaeology to contribute to the long term monitoring and measurement of coastal change may be illustrated by two projects. Funded through the European Regional Development Fund Ireland-Wales Programme 2014-2020, Climate Change and Coastal Heritage (CHERISH) project, aims to raise awareness and understanding of the past, present and near-future impacts of climate change, storminess and extreme weather events on the rich cultural heritage of the Irish and Welsh regional seas and coast. The project is developing methodologies and skills through shared experiences and knowledge exchange between the two countries and the wider expert community of geographers and geologists  [118]. Historic Environment Scotland uses a range of spatial data to monitor the condition of and risk to properties in its care.
Mandate INSPIRE mandates the release of Protected Sites data as discovery metadata, View and Download services. Through the European Convention on the Protection of the Archaeological Heritage (revised) (1992) (the Valletta Convention)  [7] , archaeologists have a duty to develop and maintain inventories of archaeological sites and to promote public awareness. Inventory systems have been transformed from paper records to an online database and Web-GIS. The INSPIRE Protected Sites theme makes inventory data (Valletta Convention Article 2i  [7] ) available as spatial View and Download Services but primary research data-Article 7i of the Convention  [7] -remains largely inaccessible. The potential for using emerging digital technologies to share, connect and provide access to archaeological information is recognised by The European Archaeological Consilium's (EAC) Amersfoort Agenda  [123].
The Amersfoort Agenda acknowledges that this will require improved collaboration and development of, and participation in, European networks. The Agenda also promotes the greatest possible access to digital archaeological resources for various user groups, and the exploitation of digital databases to their full potential, including reaching the wider public. Without dedicated working groups or resources at a European level through the EAC or a relevant Community of Interest in the European Archaeological Association, there will be no concerted impetus to help deliver this vision. In 2015, the Horizon 2020 Expert Group on Cultural Heritage  [124]  found that spatial data about the historic environment should be at the heart of good decision making but is noticeable by its absence. The group recognised the need to shift from "an object-orientated approach towards a spatial approach in heritage planning", and to consider "cultural landscapes early as part of land use and spatial planning processes".
-It should be possible to seamlessly combine data from different sources and share it between many users and applications (Interoperability). -Data should be openly shared so that users can discover which spatial data is available, evaluate its fitness for purpose and understand the conditions for reuse (Findability). There is also a need to: build capacity within the profession to use and share spatial data; -ensure that datasets are routinely and promptly updated; -promote best practice; -make data discoverable, available as View and Download Services, preferably under an Open Licence. INSPIRE technical specifications already promote good data stewardship, encapsulated by the FAIR Data principles  [126, 127]  for scientific data. In particular, metadata should facilitate interoperability and reuse  [128] : -enable users to discover what the dataset is about.
Promoting Open Licencing Unlike INSPIRE, which promotes the sharing of data across the public sector, archaeological data is created through research, commercial archaeology and community projects. With multiple data creators, there is no consistent approach to the reuse of spatial data. Where datasets are available, the licencing conditions are often not apparent, or reuse may be constrained by funding conditions. Open licences must be promoted within the profession to ensure that spatial data, essential for project discovery, is freely accessible.
Findability-The Need for a Spatial Approach Considerable investment has been made in developing digital infrastructures for object (Europeana  [80] ) and project (ARIADNE  [47] ) data. To date, these portals remain object-or site-focused, whereas engaging with the wider geospatial community requires a landscape approach to aid and signpost available View and Download Services to the full range of archaeological spatial datasets, and relevant related resources such as historical mapping and LiDAR datasets. One option would be development of the ARIADNE service page to collate and publish spatial datasets and portals  [129]. The wealth of GIS technologies in cultural heritage has long been recognised  [130]  if not realised. An ongoing review of services  [131]  demonstrates the considerable potential for coordinating a formal metadata catalogue as part of a wider archaeological digital infrastructure.
Many of the building blocks that would help archaeologists move from established processes to the maturing geospatial approaches for societal benefit already exist. INSPIRE provides the template and foundations for sharing a range of datasets including Protected Sites data. SEADDA  [27]  is addressing accessibility and reuse of digital data in archives. Through ARIADNE  [47, 81] , there is an emerging research data infrastructure, and more and more vocabularies are available in SKOS formats. Many countries are developing digital processes and incorporating data standards to manage the data flow from field to record. With standardised reporting, it is a short step to routinely collate and publish spatial data from primary archaeological research. It is a case of bringing together these building blocks but rather than developing new solutions we should build on the emerging digital infrastructure that is ARIADNE.
Material This research brings together a comprehensive database of all human dental collections from Java, whose specimens are housed in 7 different institutions in Indonesia and Europe: The Sangiran Museum, Balai Arkeologi Yogyakarta, Geological Museum Bandung, Gadjah Mada University Yogyakarta, Institute of Technology Bandung, Naturmuseum Senckenberg Frankfurt, and National Museum of Natural History Leiden. These specimens include 17 maxilla and 19 mandibles (mostly fragmentary) as well as 62 isolated teeth. They come from the following Pleistocene sites (Fig. 1 ): Sangiran and Patiayam (Central Java), Trinil and Wajak (East Java), Lida Ajer (West Sumatra). For comparative purpose the study will also include Sinanthropus teeth, which are the Pleistocene hominins from the Zhoukoudian Lower Cave (China), the nearest Mainland Asia region.
Conclusion Teeth are the most abundant anatomical parts represented among hominin fossils collections, especially from Indonesia. Some fossils were found in situ in archaeological or palaeontological excavations, others have a more or less reliable stratigraphical context, and others are surface finds. That means that the identification of the material and the documentation of their chrono-stratigraphical context is not always possible. It has to be taken into account in any study attempt. We tried to compile quite scattered data and to build an updated comprehensive reference collection: some of the analyzed teeth have been subject to detailed previous studies, some were only partly or not studied; a large part of the Homo sapiens collection included in this study were never studied in detail before.
Remerciements The authors would like to acknowledge colleagues and friends who have supported this study;  Marie-Antoinette de Lumley  and  Anne-Marie Moigne  at the  Muséum national d'Histoire naturelle, Paris, Yves Coppens at the Collège de France, Paris ,  Jeffrey H. Schwartz  at the  University of Pittsburgh ,  Pennsylvania ,  John de Vos  and  Natasja den Ouden  at the  Naturalis Biodiversity Center, Leiden ,  Friedemann Schrenk  and  Christine Hertler  at the the  Naturmuseum Senckenberg, Frankfurt ,  José María Bermúdez de Castro  and  María Martinón-Torres , at the  Centro Nacional de Investigación sobre la Evolución Humana, Burgos ,  Fachroel Aziz ,  Unggul Prasetyo Wibowo , and  Halmi Insani at the Museum Geology, Bandung ,  Mika Puspaningrum  and  Johan Arif  at the  Faculty of Earth Sciences and Technology, the Institute of Technology Bandung, Neni Rahmawati at the Laboratory of Bioanthropology and Paleoanthropology, Faculty of Medicine, Public Health and Nursing, Gadjah Mada University, Yogyakarta, Sukronedi  and  M.
Financements This research funded by  Erasmus Mundus International Doctoral in Quaternary and Prehistory, European Union , and supported by  UMR 7194 Histoire Naturelle de l'Homme Préhistorique , ED 227  Sorbonne Université  and the  Muséum national de Histoire naturelle, Paris, France , also  Area de Prehistoria, Universitat Rovira i Virgili , and  Institut Català de Paleoecologia Humana i Evolució Social, Tarragona, Spain.
We investigated the interactions of air and snow over one entire winter accumulation period as well as the importance of chemical markers in a pristine free-tropospheric environment to explain variation in a microbiological dataset. To overcome the limitations of short term bioaerosol sampling, we sampled the atmosphere continuously onto quartzfiber air filters using a DIGITEL high volume PM10 sampler. The bacterial and fungal communities, sequenced using Illumina MiSeq, as well as the chemical components of the atmosphere were compared to those of a late season snow profile. Results reveal strong dynamics in the composition of bacterial and fungal communities in air and snow. In fall the two compartments were similar, suggesting a strong interaction between them. The overlap diminished as the season progressed due to an evolution within the snowpack throughout winter and spring.
Bioinformatics and Statistics For 16S sequences, the base quality of the reads 1 and 2 was controlled using the functions fastx_quality_stats and fastq_quality_boxplot_graph of the FASTX-Toolkit 1. PANDAseq 1 http://hannonlab. cshl. edu/fastx_toolkit/ (Masella et al. , 2012)  was used to assemble read 1 and 2 using the RDP algorithm, the minimum and maximum length of the resulting sequences for 16S were 410 bp and 500 bp, and 390 bp and 500 bp for ITS, with a minimum and maximum overlap length of 20 bp and 100 bp, respectively. The resulting sequences were stripped of their primers and annotated to the genus or family level by RDP Classifier  (Wang et al. , 2007)  using the RDP 16srRNA and fungallsu databases. Singletons were removed. Negative control OTUs were subtracted for all samples (Supplementary Figure  S6  and Supplementary Table  S1  for rarefaction curves and sequence statistics).
Raw sequences were stored at ftp://ftp-adn. ec-lyon. fr/ Els_2020_amplicon_sequencing_air_snow/. Statistical analyses were done in R (R Core Team, 2015) using the phyloseq  (McMurdie and Holmes, 2013) , vegan  (Oksanen et al. , 2018) , and ggplot  (Wickham, 2009)  packages. Mean raw bacterial and fungal read counts per sample were 20153 (+ -11280) and 20899 (+ -21582), respectively. The whole dataset comprised 1566 bacterial and 3127 fungal genera, which accounted for 1058 bacterial and 1902 fungal genera after removal of blanks. The statistical analyses were run both rarefied and untreated on the dataset, which did not show differences, but removed rare genera. To include otherwise removed genera and to present the data characteristics appropriately, relative abundance analysis was carried out on non-filtered data  (Weiss et al.
, 2017). Bray-Curtis distances for bacteria and fungi were calculated on datasets normalized to relative abundance  (Weiss et al. , 2017)  and ordinated with principal coordinate analysis (PCoA). Pairwise PERMANOVA (permutational multivariate analysis of variance) was conducted using the package "pairwiseAdonis" (Martinez Arbizu, 2017), ANOSIM, ADONIS, Chao1, and Shannon indices were calculated using the package "phyloseq"  (McMurdie and Holmes, 2013). Bonferroni p-value correction (i. e. , p. adj) was applied as default for multiple corrections. Statistical parameters of ANOSIM (999 permutations), ADONIS (999 permutations), PERMANOVA and wilcoxon test statistics are reported in the Supplementary Tables  S2-S6. The significance level was set to alpha = 0. 05.
PM10 Quartz Fiber Filter Chemistry Quartz fiber filters were analyzed for different compounds. The carbonaceous fraction of organic, elemental and total carbon (OC, EC, and TC, respectively), were thermal-optically measured. Therefore, a filter punch with 10 mm in diameter was used for analysis with the OC-EC analyzer (Sunset Lab), using the EUSAAR2 reference temperature program and automatic split point setting as performed by the evaluation software. Anions (Cl -, NO 3 -, and SO 4 2-) and cations (Na + , K + , NH 4 + , Ca 2+ , Mg 2+ ) were determined using ion chromatography with the same setup as for the snow samples. Levoglucosan was also measured via ion chromatography (Dionex ICS-3000) but without using a suppressor. For the measurements of the cations a punch of 12 mm in diameter was placed in a polypropylene test tube and 3 mL of 38 mmol MSA (Methanesulfonic acid) were added.
Canonical Analysis of Principal Components and Stepwise Regression To evaluate the importance of environmental parameters on the variability of the microbial community a stepwise regression with backward elimination was conducted with "ordistep" in vegan  (Oksanen et al. , 2018)  and visualized with a canonical analysis of principal components (CAP) based on Bray-Curtis distance on normalized community data. All biological, chemical and physical environmental parameters for CAP analysis and stepwise regression were z-standardized within the air and snow dataset, respectively to account for different scales of unit. See Table  2  for an overview of all parameters used for the CAP analysis.
Based on these results, the macroscopic parameters of thermal conductivity and viscosity that are needed to compute the conservation equations are calculated. In our simulation, the absolute snow height was used to calculate new snow sums. Neither the snow surface temperature nor the reflected short-wave radiation were measured, so for the upper boundary condition, we used the incoming long wave radiation and parameterized the albedo. This implies working with fluxes rather than temperatures, i. e. , Neumann boundary conditions. Radiation data from Mt. Sonnblick used as input was obtained within the ARAD network  (Olefs et al. , 2016). Snow height was obtained from an automatic TAWES laser snow gauge.
Density x Analyzed samples for the respective environmental compartment indicated by "x". next to the snow pit sampling site, data validation was carried out using data from daily manual snow height measurements and via quality control algorithms. Wind speed and direction, temperature, pressure and humidity were obtained from the meteorological measurements at the Sonnblick Observatory. Details on calculation and model settings and further references are available at https://gitlab. com/lwd. met/snowpack/sonnblick_ 2016/wikis/home. The results of the simulated profile were compared with the measured grain type, grain size and snow density for the respective layers during field sampling (Supplementary Figures  S1, S2 ). The end-result is the temporal evolution of the snow cover as a time series of modeled snow profiles at our experiment site (Figure  3A ).