dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: We acknowledge the people past and present whose samples we analyzed in this study. We thank  Oliver Uberti  for the design of Figure 3 and the figure associated with the  One Page Summary. We thank the  Minusinsk Regional Museum  of  N. M. Martyanov  for sharing skeletal samples. We thank  Orazak Ismagulov ,  Ainagul Ismagulova , and  Akmatov Kunbolot Toktonsunovich  for facilitating access to skeletal material. We thank  Orazak Ismagulov ,  Ainagul Ismagulova , and  Akmatov Kunbolot Toktonsunovich  for facilitating access to skeletal material We thank the  Department of Archaeology and Museums, Government of Pakistan , the  Directorate of Archaeology and Museums, Government of Khyber-Pakhtunkhwa Province (Pakistan) , and the  Dipartimento di Biologia Ambientale, Sapienza Università (Rome)  for facilitating access to the materials from Swat excavated by the  Italian Archaeological Mission  (now ISMEO).
All newly reported sequencing data are available from the European Nucleotide Archive, accession number PRJEB32466, and the software for dating admixture events in ancient samples is available at https://zenodo. org/record/ 3263997#. XRnebJNKj6A (DOI: 10. 5281/zenodo. 3263997). The Online Data Visualizer is available at: https://public. tableau. com/ views/TheGenomicFormationofSouthandCentralAsia/Fig_1. Visualizer for exploring data: https://public. tableau. com/views/TheGenomicFormationofSouthandCentralAsia/Fig_1.
Contributors Sarah Bowen Savant is a Cultural Historian and Professor at the Aga Khan University-ISMC in London. She is the PI for the Arabic DH project KITAB (kitab-project. org) and co-PI for the Open Islamicate Texts Initiative (OpenITI, <http://iti-corpus. github. io/>), a corpus building project. She publishes on early Islamic history and historiography and Digital Humanities. Thibault Clérice is the head of the MA program "Digital Technologies Applied to History" (Technologies Numériques Appliquées à l'Histoire) at the École Nationale des Chartes (Paris, France). He is a Classicist who served as an engineer both at the Centre for eResearch (Kings College London, UK) and the Humboldt Chair for Digital Humanities  (Leipzig, Germany) , where he developed the data backbone for the future Perseus 5 (under the CapiTainS.
4  As he summarizes, "the cross between the two systems that governed previous reading material (the volumen and then the codex) produces, in fact, an entirely new relationship with the text. Thanks to these mutations, the electronic text is able to allow the realization of the never-ending dreams of conquering all knowledge that preceded it". 5  In this relationship to textuality, the rhythm of data production and publishing is reshaping Humanities research profoundly. The digital world allows data to be constantly updated, openly accessible online, and in various forms. Publishing formats can be short syntheses of datasets in blogs, videos, short posts or draft papers, social media, even before the research is completed and peer-reviewed. Peer-reviewed journals like New Testament Studies now allow authors to reference blogs of individual scholars.
2. 1 Infrastructural Challenges from Photography to Digital Database During our December 2011 season, the YMAP team conducted excavations in an upstairs space called the "Candle Room" within the monumental fifth-century church at the White Monastery. Located above and to the north of the sanctuary, the room had been used in recent times as a storage space for candles (hence the name). Along with fragments of candles, charcoal, floor plaster, brick, newspaper, cardboard, and modern prayer petitions, our excavations in the room yielded numerous small fragments of parchment and paper with Coptic or Arabic writing, along with pieces of textile and leather associated with book bindings. The following year, I oversaw the photo-documentation and cataloguing of the fragments on site. A co-authored article published in the Journal of Coptic Studies documented our archaeological methodology and presented an initial report on the contents of this find, including one identified fragment from the Canons of Shenoute, the fifth-century head of the community.
As a result, prior to our planned photo-documentation we were required to relocate our entire finds magazine to a building adjacent to the inspectorate and to perform all our subsequent finds analysis and photography in the inspectorate office space, where the lighting was poor and variable. At the time, these local dynamics made it uncertain what kind of access and authorization we might be granted in the future, and so we felt pressed to complete an entire set of photographs within the short span of that season, despite the suboptimal conditions. I completed this work with the assistance of three graduate students, Elizabeth  Davidson, Daniel Schriever, and Mary Farag. We faced a different set of infrastructural challenges in moving from photography to the development of an online database.
Due in part to the suboptimal photographic conditions on site, the images revealed certain variations in quality, lightness/contrast, and proportion. Thus, to prepare the composites required a multi-step approach: we adjusted lightness and contrast, and then resized and reconfigured images to achieve uniformity across the entire collection. Once the composites were prepared, the resulting images were optimized in Photoshop. Finally, the high-resolution images were reduced to about ten percent of original size, without reducing image quality for online viewing. In preparation for making the images available to the general public, we ran tests and decided on a simple yet advanced "slide-show" gallery presentation using an application called SimpleViewer-Pro. 3 This software had a pleasing interface, allowed for Universal Playback, and provided a considerable amount of control and flexibility.
He also revealed that he had been approached in the recent past by another North American academic institution interested in entering into a contract with the monastery to digitize the collection. But the terms of the proposed contract caused him considerable discomfort, primarily because of the way copyright control was construed. While the American institution offered to provide equipment, and to undertake all the digitization work itself, it wanted to charge a fee to anyone requesting use of the images. This fee would be payable not to the monastery but to the partner library in the United States. Father Bigoul did not feel comfortable with this kind of financial structure: instead, he preferred to facilitate free access. The proposed contract also stipulated that copyright ownership would be shared between the American institution and the monastery and that each would possess a copy of the digital database, but Father Bigoul was concerned that in practice the agency of the monastery would be trumped by the American institution if there was a disagreement on terms of permission.
As a result, Father Bigoul had come away with significant reservations about entering such partnerships. In this context, I want to draw on some helpful insights from scholars working in the area of digitization and cultural heritage studies. As I alluded to earlier, Father Bigoul's reservations are in fact deeply embedded in cultural memories of "epistemic violence" and "the loss of Indigenous control over use and access. "9 As Joshua Bell, Kimberly Christen, and Mark Turin note in their introduction to a volume on "Digital Repatriation and the Circulation of Indigenous Knowledge," it is "precisely because [digital] materials can exist in multiple locations" that there has been a shift of emphasis from "issues of access to access and control. " It is because "digital objects can co-exist in Indigenous archives and websites as well as in institutional databases (online or off)" that communities like the Monastery of the Syrians raise valid "concerns over who makes decisions about how materials are accessed, circulated, and understood across multiple settings.
In their study, they then tested the results using crowdsourcing techniques, asking the crowdsourcers to choose the outsider and then measuring how often that outsider was the same as the one chosen by the algorithm. Gap Score presents a way to perform this task without crowdsourcing if one has an external categorization of semantically related words for a language or a specific corpus. This study is broken down into two parts. The first part introduces the Gap Score metric and applies it to the results produced on the Greek Biblical corpus by the Word2Vec machine learning algorithm. In this study, we produced several language models based on different parameters using Word2Vec as implemented in the Python Gensim package and evaluated each of these using both simple distance measures and Gap Score.
However, the results suggest that one can get just as good a language model without such pre-training. The Gap Score table still shows a marked preference for fewer neurons, with 9 out of the 10 having only 30. The Gensim table, however, prefers larger networks, with only 5 of the ten having 30 neurons, 3 having 50, and 2 having 80. This suggests that the doesnt_match function requires a more complex representation of the corpus in order to produce good results. Finally, the Gap Score results show more preference for the larger, 10-word context window than did the previous two tables, 4 of 10 depending on this window size. The Gensim results also showed a slightly higher preference than the previous two, with 3 of 10, but still less so than Gap Score. This last observation about the preference for larger context windows for the New Testament probably comes from the corpus size of the New Testament.
Within computer science, the term Natural Language Processing (NLP) refers to way computers are programmed to understand natural languages. At a basic level, NLP involves three steps -lexical analysis, syntax analysis, and semantic analysis. The complexity of each of these steps is perhaps best illustrated through looking at how three well-known programs incorporate NLP; namely, Microsoft Word, the Google search engine, and Apple's Siri. If you were to type (or copy and paste) the following string -"Can I be worn jeens to church?" -into Microsoft Word then it will perform simple lexical analysis by grouping the characters into tokens (i. e. words) using the whitespace and punctuation as separators. Having done this, the program will then consult its dictionary and recognize that "jeens" is not a valid entry.
In this case, it is likely to be a rule that looks like this: <question> = <adverb> <subject> <verb> <object> <prep phrase> <question mark> It might then change the sentence for you in order to conform it to this closest rule, as follows: Can I wear jeans to church? As complicated as this might sound, the next step, semantic analysis, is even harder. Such analysis would involve the program trying to understand the meaning of the sentence. Subsequently, the program might be intelligent enough in order to suggest a more formal alternative, such as: What type of clothing is appropriate for church? While Microsoft Word is not yet able to perform such complicated analysis, the Google search engine attempts to do this when it responds to a user's request. For example, if you were to copy and paste the string "Can I wear jeans to church?" into this search engine, it will attempt to find the answer to this question by searching for web pages that might be relevant.
While Microsoft Word and the Google search engine perform NLP on written (or typed) text, the latest generation natural language processors are able operate on spoken text. 4 These include programs like Apple's Siri,5 as well as Amazon Alexa6 Google Assistant,7 and Microsoft Cortana. 8  The reason why such technology is so popular is that it has the potential to answer almost every question and perform almost every request. That is, not just existing questions, but new ones as well! Not only can you ask, "How far is it to the moon?" but you also say, "Please order me a pizza. " This is because Siri is not simply looking at web postings on the Internet (though it can do this) but it is asking other computer programs to perform actions on its behalf. These programs can either be running on the same device/computer or they can be running on other platforms across the Internet.
2 The Variety of Reference Forms The onset of the digital age has brought with it the potential to automate the search for textual references, thereby allowing large databases of source texts to be quickly scanned. However, in order to find as many references as possible, it is important to know exactly what to look for. Therefore, this present section catalogues some of the different reference forms (i. e. the various ways that words are borrowed) in ancient literature. It has been said that one of the characteristic features of the work of Clement of Alexandria is "the presence of borrowed material […] taken more or less accurately from other authors' and "culled from every nook and cranny of the nearly thousand-year span of Greek literature. "9 The difficulty of identifying this "borrowed material" is compounded by the fact that he rarely acknowledges his sources; instead "most of the time Clement connects a thought from outside by no more than a single word, a brief formula, a hidden allusion or a mere hint.
Combining Modern Computers and Ancient Texts In the past, the task of identifying textual references was the domain of individual scholars who each manually searched the set of source texts that he/she was familiar with. However, the onset of the digital age has meant that searches can now be performed on any source text, whether familiar or not, at the click of a button. Event-driven software programs, like Accordance and Logos, and Internet search tools, like Bible Gateway and the Thesaurus Linguae Graecae (TLG), enable biblical scholars to perform word searches at a much faster rate than the traditional paper-based approach. For a complex query with multiple search words, it usually takes more time to type in the search string than for the computer to return the result. This difference becomes even more pronounced when large numbers of searches are involved, such as when searching for allusions and influences across an entire document.
Hence the algorithm may not always achieve the best solution (since not all permutations are considered), but it finds a good result in a shorter timeframe. The Greedy String-Tiling algorithm is used in the METER project at the University of Sheffield25 and is most effective when the words are borrowed in sequences (such as in quotations). The third type of algorithm, Sequence Alignment, divides the source and target strings into overlapping sets of consecutive words. These sets are called "shingles"or "n-grams", where n is the specified number of words in each set 20 The hashing function converts each word into a number, or index, which is then used to order the words. An example of a simple hashing function is to sum the unicode of each letter in the word. 21 The algorithm can also be applied to a sentence or a verse, or even to a whole document.
g. one or two). This flexibility has contributed to its use in several recent studies, including being used to search for text re-use across the TLG database. 28 In summary, there have been a number of recent projects in the Digital Humanities that have sought to detect textual references across a particular set of source texts. In essence, each of these projects is a natural language processor that automates the first two steps of NLP, lexical and syntax analysis. Due to the complexity of NLP, these projects involve written (rather than spoken) texts where the lexical analysis can be largely pre-determined. Each of these projects has a slightly different form of syntax analysis (i. e. determining which combination of words could be valid reference) that is an adaptation of three types of algorithms (as mentioned above), all of which are aimed at detecting verbal similarity.
Only potential references with a minimum level of verbal similarity are passed on for semantic analysis (which is done manually on account of the complexity of this task). This approach is inclined to prefer quotations and paraphrases as opposed to single keywords. The syntax analysis can be configured to detect single word matches but that would mean all such matches would need to be passed to the manual semantic analysis phase, which is not practical for large databases of texts. Furthermore, these programs assume that the syntax of references (i. e. the arrangement of the borrowed words) is static. However, the form of textual references in poetry, for example, need not be the same as those in prose. Likewise, the way that the Early Church Fathers make reference to the New 26 So for example, the string "Shakespeare wrote many plays about love" would be divided into four trigrams: "Shakespeare-wrote-many", "wrote-many-plays", "many-plays-about" and "plays-about-love".
Testing the Algorithm This paper presents a generic NLP algorithm for detecting potential textual references. The algorithm itself is not particularly novel since it is in essence just the three steps of NLP. It was developed and tested during the research phase of a PhD in the field of biblical studies. The research looked at how the Pastoral Epistles (i. e. Titus and 1 & 2 Timothy) might have been influenced by the Septuagint and Jewish Pseudepigrapha. Implementing the algorithm as a computer program was not possible (nor necessary) in this context because a database of these texts was not available in the public domain. Since the semantic analysis (the most complicated aspect of NLP) needed to be manually performed, the lexical and syntax analysis was also simulated manually. This was possible because the algorithm is essentially very simple.
This data can then be used in further analysis, such as tracking the usage of a text before and after certain major events, like wars and revivals, or even to study which texts have been the most influential in legal decisions. Consequently, the ability to record metadata provides a wealth of possibilities for more scholars to investigate further. In summary, this new algorithm offers three significant benefits to the study of the humanities. The foremost of these is that it can detect any reference form, including those with low levels of verbal similarity. Furthermore, by varying the set of source texts to search, it can simulate different intertextual frameworks, thereby allowing it to highlight potential references in large databases of source texts, even those involving in less familiar texts.
Given that the same project may treat certain categories of manuscripts differently, such as preserving all abbreviations in majuscule manuscripts but expanding them in minuscules, the structured provision of this information means that it is recorded on a case-by-case basis and offers a clear guide to the principles and limitations of the present transcription. This information 37 This is exemplified by  Krans, "Codex Boreelianus". 38  For Diktyon numbers, see <http://pinakes. irht. cnrs. fr/>; the proposal for an ISMSN was put forward by the Biblissima project at a conference in Paris in April 2017. The current TEI header for the IGNTP includes a field for the identifiers in Trismegistos and the Leuven Database of Ancient Books. would also be helpful for the later enhancement of transcriptions, when features not recorded by the original transcriber can be systematically added.
Following the original presentation of this chapter, a proposal was tabled that the IGNTP and other creators of electronic transcriptions should follow INTF's lead of removing the non-commercial stipulation from their licences and also dispense with the share-alike requirement, in order to allow for the widest possible re-use of this data. This was unanimously approved by the IGNTP committee in November 2017 and applied retrospectively with the release of 350 New Testament transcriptions under a Creative Commons 4. 0 Attribution licence. In reality, it is often practical measures for making transcriptions publicly available which can prove the stumbling block to their re-use. Earlier digital editions relied on a publishing model which served transcriptions as HTML generated from a database and provided no access to the original files: this is the case with editions of New Testament writings created with the Anastasia software as well as the transcription display in the Digital Codex Sinaiticus project, although the latter has the whole transcription file available as a separate download.
A researcher may use the dynamic light source and special filters within RTI viewer software to reveal details of an inscription that are not visible in images wherein an object is lit only from a single direction. Just as technology has provided scholars with more precise images of inscriptions, it has also provided them with more options, and arguably more efficient ones, for using these images in order to obtain as much data from them as possible. One of the most commonly used software programs for working with digital images is Adobe Photoshop. 27 27 See Adobe Photoshop, <http://www. photoshop. com/products/photoshop>, accessed on 10. 04. 19. The Photoshop "invert" tool (Figs. 9. [4] [5]  or the "curves" tool (Figs. 9. 6-7), which can lighten or darken an image, can maximize the legibility of writing on inscriptions.
Images of seals can be flipped to facilitate epigraphic study. For the purposes of script analysis, it is often very useful to be able to measure the angles of certain letter strokes and to analyze the synchronic variation and diachronic development of stroke angles. Photoshop has tools that can be easily used to measure these developments (Fig. 9. 8). Just as Photoshop is a preferable program for the initial manipulation of digital images, Adobe Illustrator28 is a preferable program for the production of digital drawings. While both Photoshop and Illustrator offer essentially the same basic digital drawing tools, such as brushes and pens, Illustrator, as a vector graphics creation application, facilitates the production of more precise and accurate drawings, and offers a higher degree of editing capabilities and limitless output resolution, than does a raster graphics program like Photoshop.
Furthermore, it makes the epigraphic data in a drawing both easily comparable and easily transferable. This aids in both script analysis and in the publication and presentation of such analysis. The most obvious way is in the preparation of palaeographic script charts, which will be discussed in more detail shortly. The drawing tools in Illustrator can be controlled through various means. The most basic is with the use of a standard computer mouse. However, we  have found that using additional hardware, such as a tablet with a stylus, like those made by Wacom, affords an epigrapher greater control and, thus, facilitates more accurate drawing (Fig. 9. 10). Using handheld tablets and Ultrabooks that allow one to draw with a stylus directly onto the screen, and thus directly onto the image of an inscription itself, is also advantageous.
Part Two: Presentation of Epigraphic Analysis During the second part of class, the instructor will give assignments aimed at teaching the specific skills necessary for presenting an epigraphic analysis, such as the manipulation of digital images and the production of digital drawings and script charts. While drawing (and making decisions about how and what to draw), many methodological principles taught in the first part of the class will be exemplified and reinforced. Students will first need to learn basic skills for using digital imaging and drawing software. Fortunately, both Adobe Photoshop and Illustrator come equipped with their own help documentation and/or video tutorials. The course instructor might also offer some basic lessons. A most useful resource for many scholars and students at academic institutions are teaching and learning centers.