dataesr/scientific-paragraphs-categorization huggingface dataset:
This paper deals with a major challenge linked to the collection of audiovisual documents within television and web archives. Looking for repeated sequences within a corpus of thousands of videos, we faced the fact that the footage we were looking for reveals itself to be reachable only as ghost data. In fact, any audiovisual sequence reused within different contexts exists conceptually as the repetition of one single visual unit, but also from the point of view of the metadata tagging its occurrences, each item is a distinct document. Like a ghost, the shot is there, scattered among different places, but the metadata cannot point us to the visual form repeated, despite its evidence to the human viewer. When facing large amounts of data, to relate a visual unit to its occurrences, data analysis techniques are needed.
This means identifying a certain number of case studies, and then carrying out targeted queries in databases aimed at finding these same images. This approach is used by large-scale research projects, such as the HIVI project, led by Valérie Schafer and Frédéric Clavert at the University of Luxembourg, which aims to collect significant memes to understand the phenomenon of virality 7. The collection carried out within the Artlas project, led by Béatrice Joyeux Prunel at the University of Geneva, puts into practice a real digital Atlas Mnémosyné 8 , dealing with databases of artworks around the globe. The objective is to make an epidemiology of images: to understand how they circulate and the exchanges necessary to their dynamism  9. The CADEAH project offers such a perspective on European audiovisual corpora, those of EUscreen and Europeana.
This means that instead of starting from a set of given videos to look for within a field, we wanted to understand which videos are repeated in the first place. A large part of our data comes from two national archives (the legal deposit of the French television kept at the INA, the audiovisual archives of the RAI in Italy) and a private deposit (the archives of Mediaset). These huge archives present the 24-hour feeds of national television channels and are protected by specific national rights. To find stock shots, we have thus used the documentation of audiovisual archives. These institutions perform a great work of indexing that allows us to trust the textualizations to find, if not all the stock shots, at least the most obvious ones to the documentalists. This means that when stock shots are reused in a television program, the presence of images is documented by RAI or INA and manifests itself in the metadata.
However, for all the archival deposits documenting TV broadcasting, the basic unit of document is supposed to be a subject (a piece of news report for example). So, via textual documentation, we could only find the whole video containing certain stock shot(s) in it (a news report on TF1 at 8pm for example), not the stock shot which is still embedded in its initial context. From the first collection based on textual search in databases, we then engaged in a second collection to pick up only redundant stock shots that interest us. A similar approach could be largely found in the studies on text tracking, with certain important efforts in algorithms realised to identify recurring texts, such as the Viral Texts project, which aims to detect, substantial repeated passages of tens of thousands of words embedded within unrelated newspaper issues  11  ; as well as other projects which are interested in finding repeated "memes" of only a few words.
G h o s t D a t a Studying the circulation of archive material means that we are interested in the process of repetition: the redundancy of the same items found at different times in different media. The main object of our research is more specifically the recurrence of the same audiovisual sequence (thus a stock shot) within a corpus aimed to be representative of an ample international media segment. To understand the circulation of a stock shot, we aim to find all its occurrences within a given period. It was thus necessary to proceed to an analysis of a given field to understand what is repeated and how the repetition is affected by different logics of refashioning and remediation of images. However, we face the fact that each occurrence of stock shot, visually repeated within different video programs, appears as a different document in the databases (first issue).
Ghost data is not some general information hidden in a corpus, which has been somewhat of a trivial notion for data analysis at least since Tukey  15  , but a factual element that requires some analytical and computational effort to be individually identified. The concept of ghost data is useful to understand how to manage some (visual) items within a dataset. The issue is how to work with stock footage whose occurrences are scattered in various forms. Our solution was to collect full videos (news items) and their metadata, then we took screenshots for each reused stock video sequence, and we annotated these screenshots. Despite this very laborious and almost totally manual process of data collection and enrichment (which involved one PhD researcher, one Postdoc researcher, and 8 interns), what we finally get is different images that are not yet indexed as individual occurrences of a smaller number of repeated units or types.
In the case of our example about the Treaty of Rome, the video source is kept by RAI and Istituto Luce (the second issue). What we do have are different parts of the video source, different timecodes, and even different edited versions. Ghost data are thus those items that need to be fully identified and somehow indirectly recovered from a dataset. We are now going to detail how the stock shots collected for the CROBORA project allow us to retrieve ghost data through their collection and documentation. In the following sections, we intend to describe the way in which we approached the corpus construction and the detection of ghost data.
1 D a t a S o u r c e s a n d S e a r c h S c o p e The phenomenon that the presence of audiovisual archive material in media spaces is growing can be clearly observed in our research scope. The two graphs below show a tendency of a growth of stock shots both on France and Italian territory, precisely from 2012 for France, and from 2016 for Italy. This observation proves a necessity to take a closer look at these specific archives. Based on this 'archival rise', we have chosen to collect the stock shots from different sources. Our data collection is based on several heterogeneous data sources. First, our research field covers television in France and in Italy, which means the implication of multiple repositories of different natures: the French Legal Deposit of Web and Television, held by INA; the archives of the Italian public service of television, at RAI; and those of the main italian private broadcaster, Mediaset.
We also collected data from a selected number of social media accounts on the French and Italian web. A first problem comes from the heterogeneity of data sources, which leads to a heterogeneity of metadata structures. On the one hand, RAI and INA archives, being conceived as public heritage institutions, present highly detailed metadata for the programs they gather; however their style, language and criteria are different: for example, while RAI annotates both the journalistic discourse in voice over and the visual content in a program, INA focuses more on the discourse made by journalists and anchors, and tends to categorise the footages instead of describing their visual content ("images d'archives", "images factuelles", "extraits", etc. ). On the other hand, Mediaset stocks documents with different aims, not being interested, as a private player, to document broadcast programs for the aim of preservation.
Mediaset archives serve a dual purpose: to collect everything that has been shot by the broadcaster's personnel and to retain what was broadcast for potential future reuse. Furthermore, social media accounts are documented in a  non-systematic way, starting from the descriptions used by each account. While these original metadata may be interesting for textual analysis, enrichment by annotation had to be conceived and executed to harmonise them. For the French and Italian public television archives, we limited ourselves to the genre of news broadcasting, to the period from 2001 to 2021, and only to some major national channels: TF1, France 2, and ARTE on the French side, Rai 1, Rai 2, and Canale 5 on the Italian one. The search queries were based on the words "archiv*/repertorio" and "europ*": in this way we aimed to collect most stock images concerning the European Union.
To this day, we successfully gathered a total of 22,520 instances of stock footage from television archives. As for the Web, there exist two approaches for data collection: firstly, the legal deposit of the archived Web curated by INA constitutes the first data source; secondly, we also collected straight from the live Web 18. Therefore, two methodological approaches were involved: textual search in the INA database and search engines of the live web. In this way, we had to rely on the previous enrichment of visual data by humans who documented this data, either by archivists or online professionals. However, the problem with the web lies in the fact that with a large quantity of videos, the metadata is much less structured than that of television. In the absence of structured metadata, it is not possible to filter the data and then visualize all these videos through human efforts, as we did for the television data.
tv, were also included in our sources. Generally, our data primarily originated from heritage institutions, media sources, and EU institutions. We managed to accumulate 5,393 instances of stock shots from the live Web. It's worth noting that our data collection process heavily depended on pre-existing metadata and recommendation algorithms from video platforms, which means the exhaustiveness of our dataset might not be fully ensured given the somewhat "random" nature of our keyword research. Additionally, it's important to acknowledge that any online content that has been removed from the original platforms or websites leaves no trace on the live Web. This is precisely why we also resorted to the French legal deposit of the Web, which boasts a collection of 16,069 media websites that specialise in audiovisual content.
From this resource, we leveraged an expansive, unfiltered corpus consisting of 56,000 videos. These videos, published on platforms such as YouTube, Dailymotion, Twitter and Facebook between 2001 and 2021, were selected based on their metadata containing the keyword "Europ*". This body of videos was curated and imported by INA archivists. A detailed automatic exploration of the corpus will be provided in part 6 of this article. We chose from 2001 to 2021 as our limited period to demarcate a limited set of news reports for our data collection. What's more, this period is also considered as rich in the construction of Europe, a time range that we try to divide in two phases: from 2001 to the late 2000s, twenty years of institutional and diplomatic work between the Maastricht Treaty and Treaty of Lisbon, when it comes to the choices of the euro and an independent European central bank, the thunderclap of the rejection of the European Constitution and the adoption of the Lisbon Treaty without fanfare, as well as EU civil-military engagement in the Western Balkans, etc.
As our initial research is based on the metadata accompanying the audiovisual archives, a richer documentation work done by archivists would probably help us find more results than earlier years in the database. This hypothesis related to metadata could be partly proved by the drop after 2020 -the high volume of news about the pandemic has a great impact on archivists' workload when it comes to document indexing. The reports which should be initially indexed as « archiv* » and « europ* » could be left behind during this special time. While the database is large, there are significant gaps in its holdings and problems with its data that influence our results.
For example, the "Distant Viewing Toolkit" (DVT), a Python toolkit for computational analysis of visual culture that addresses the challenge of working with moving images through automatic analysis such as narrative arcs detection  22. For the CROBORA project, the challenge is that it is not the whole content of videos that attracts our attention, but the very fragmented stock shots hidden in each video. We considered in the first place to automatically extract key frames for each shot in our initial corpus, whether it is a stock shot or not, and then, using algorithms to cluster all those key frames according to their visual characteristics, hoping to be able to retrieve identical images from the result. For example, we applied PixPlot 23 on our corpus. It turns out that some general trends could indeed be detected by the software, such as clusters of all European flags, documents, or meetings of European leaders, etc.
Original Metadata of Videos Original metadata are the text accompanying the videos that we collected, such as documentary records produced by documentalists of INA, or video descriptions written by official accounts on Youtube. It was during the process of data collection that we first realised how important and possibly influential metadata were for the construction of our corpus and we decided that further studies on them could be proven interesting. In fact, the process by which the metadata are originally entered by the institutions who manage the collections imposes cultural categories on data themselves. Furthermore, as an important form of documentation of digital/digitised artifacts, these metadata constitute elements for textual analysis with the aim of understanding the context and meaning of the audiovisual objects that we collected.
CROBORA Metadata of Video Sequences The fine-grained annotation for specific sequences is a very valuable supplement to previous institutional metadata. CROBORA metadata are new descriptors added to each stock shot by the project's participants. If for the original metadata produced by institutions, the focus stays on the resume of the whole video content (in which the stock shots that we are looking for are embedded), our purpose is instead to establish a form of re-coding that focuses only on the stockshots. We have chosen four aspects to describe each sequence: personality, event, place, and illustration. English is used for both French and Italian metadata. In addition, each sequence has been attributed a unique identifier (UID). It is important to remark that qualitative annotation of new metadata considers the full news items in which the shot appears, so to correctly retrieve its meaning in context.
), while what we need is a form of annotation way more precise and figurative (Angela Merkel, Treaty of Rome, Paris, Border control, etc. ). Besides, millions of training images are needed to build a deep CNN model from scratch, which largely surpasses the limits of our project when it comes to expensive and time-consuming costs. Therefore, manual indexing seems the quickest and easiest way to deal with our data from the start. However, a homogenization to standardise human indexing and construct a thesaurus has proved to be necessary in the later phase, which we will not detail in this paper. Figure  5. CROBORA metadata of video sequences in our corpus.
As we mentioned above, we have annotated every stock shot assigning it to one or more specific media events. Since events aren't intrinsically embedded in an image, it is clearly the journalistic discourse that makes a shot refer to a certain event: considered from a culturally grounded perspective, reference is realised by semiotic means  31. Therefore, we have made recourse to the full videos, offering a context to the shots, including the verbal language used by journalists with the effect of linking the shot to some event. Our manual annotation of the shots therefore required an attentive interpretation, enriching the visual data with dense metadata (we use dense here with reference to Geertz  32  ), capturing the intention of use as it appears to the eyes of the model viewer. The attribution of events to shots allows us to categorise and aggregate them accordingly.
By doing so, we will also be able to compare our first corpus with the enlarged one, and check whether we observe some systematic bias in our collection that may be interpreted as a sign of global nonrepresentativity. There are two ways by which we plan to enlarge our corpus. First, by looking for the stock images we already identified as pertinent within the much less structured Web data we obtained. This refers to an additional 56,000 videos about Europe, imported by specialists at INA. In absence of structured metadata, it is not possible to filter the data and then to visualise all these videos by human efforts. It therefore becomes necessary to employ an automatic detector of images, as the one in development at INA under the name Snoop. The second way by which we want to validate the exhaustiveness of our corpus, is by automatically detecting more occurrences of the stock shots we already identified as pertinent in the full corpus, also with the help of Snoop.
Based on a content-based detection technique, Snoop is an interactive visual search engine which is solicited by an initial input of a few images chosen by the user and representing their search intention. Iteratively, similar visual content found by the software is then marked by the user, either positively (those that match her search) or negatively (false positives, retrieved images that do not match with the researcher' intention). A similarity search query can be very subjective depending on a specific user in a given situation. Snoop then uses this information from the user to strengthen a model of the user's intention  34. By feeding the software with the historical stock shots manually identified by us, we launched Snoop on a corpus of 6,000 news programs plus 56,000 new videos. By using the software Snoop, we not only intend to extend and validate our corpus, but more specifically to achieve the research of those hidden repetitions that we called ghost data, by: 1.
New annotation: During data collection, we have noticed that whenever the event of the creation of the European Economic Community is mentioned, it is probable that the stock shots in black and white showing the signature of a document by a group of politicians at the Palazzo dei Conservatori are used as illustration. Thus, group members who have detected the presence of this specific scene would annotate the stock shots as "Treaty of Rome" as a referred event. 2. Aggregation according to annotation: Next, we have managed to aggregate all the data marked as "Treaty of Rome" in our initial corpus into a separate group. For French TV news data for example, we find 43 occurrences (different stock shots). Most of the frames seem highly similar and probably come from the same video source.
With the help of a machine, we have also managed to retrieve certain relevant occurrences existing in our initial corpus but were missed by researchers during manual data collection. However, larger quantities may not be able to serve our second objective, that is tracing stock shots all the way back to their very first video source. In metaphorical terms, even if we could capture all the repetitions, we may lack access to the archived original incarnation of the "ghost" that is the video source, simply because it doesn't exist in the databases accessible to us (including INA, Rai Teche, Mediaset, Web archives, and the live web). In this case, our solution is to rebuild the "ghost" ourselves, starting from the data we have, by using the fragments in our collection that look highly similar and thus very possibly come from the same source.
Anyway, in some cases, the original video source is available online or happens to be stocked in the database of our archival partners, and Snoop may effectively help us to find it. Sometimes, an even simpler textual search is sufficient for retrieving a certain source. For example, for the sequence of the signature of the Treaty of Rome, we tried to search for the original video in available archival deposits, a copy of which turns out to be stocked on RaiPlay, the multimedia portal owned by RAI. Another example is the shot where a customs barrier is falling and being burnt out. In fact, in our corpus, the events related to the history of the Schengen agreement are often illustrated by occurrences of this stock shot. On the platform of EUscreen, we have managed to trace back the original video, whose initial title was "Strasburgo.
C o n c l u s i o n In this article, we first presented the process of corpus construction of the CROBORA project, and highlighted a theoretical question, crossing semiotics and history, about how to reach some missing source (that we called ghost data) from several signs of it. Then we described three different types of stock shots we found in our collection, the illustrative, the historical and the recent. We then focused on only one type, the historical stock shots, which is the most interesting to us although it applies to a relatively small number of stock occurrences. At last, we quickly presented the software Snoop, with the help of which we proceeded to increase the scope of our corpus, and we also aimed for reaching the original source of our stock footage. It is fascinating to trace the reuses of certain born digital or digitalized artifacts to draw out a genealogy of their trajectory.
Subjects Data for this study were drawn from the GENFI multicenter cohort study, which consists of 26 research centers in Europe and Canada. Inclusion and exclusion criteria have been previously described  (Rohrer et al. , 2015). Local ethics committees approved the study at each site and all participants provided written informed consent according to the Declaration of Helsinki. We considered both symptomatic patients fulfilling current clinical criteria for FTD  (Rascovsky et al. , 2011; Gorno-Tempini et al. , 2011) , and asymptomatic participants at risk to carry GRN, C9orf72 or MAPT mutations. Between January 2012 and March 2020, we considered 668 participants, of which 392 were mutation carriers (157 with C9orf72, 164 with GRN, and 71 with MAPT mutations) and 276 were mutation non-carriers.
MRI preprocessing and analyses T1-weighted images were processed and analysed with the voxelbased morphometry (VBM) pipeline implemented in the Computational Anatomy Toolbox (CAT12 v. 1742) (www. neuro. uni-jena. de/cat) for SPM12 (SPM12 v. 7219) (www. fil. ion. ucl. ac. uk/spm/softw are/spm12) running on MATLAB R2019b (the MathWorks, Inc. , Natick, Massachusetts, United States). The VBM pipeline consists of several stages (tissue segmentation, spatial normalization to a standard Montreal National Institute [MNI] template, modulation and smoothing), as previously described  (Kurth et al. , 2015). CAT12 potentially provides more robust and accurate performances compared to other VBM pipelines  (Farokhian et al. , 2017). The normalized and modulated grey matter images were then smoothed with 8 mm full width at halfmaximum Gaussian kernel to reduce the probability of misalignment errors, increasing the chance to detect differences over small regions of the brain.