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This dataset contains analyses of 76 ceramic samples from the medieval workshop of Les Landes (Beaupréau-en-Mauges, Maine-et-Loire, France). The chemical composition of the samples was obtained by energy-dispersive X-ray fluorescence spectrometry (EDS). The mineralogical composition of the samples was studied by powder X-ray diffraction (XRD). Method The outer surfaces of all the samples were mechanically removed prior to analysis. EDS The samples were heated to 950°C for one hour after 24h drying at 50°C, weighted for LOI calculation and ground in a tungsten carbide mortar. Measurements were made on pelletized powders. The data were collected by energy dispersive X-ray fluorescence spectrometry (Oxford INCA 300 spectrometer) coupled with scanning electron microscopy imaging (JEOL JSM 6460LV variable pressure microscope). The measurements were carried out under partial vacuum (20 Pa) with an acceleration voltage of 20 kV. The acquisition time is 90 s per spectrum, over the energy range 0-20 keV. For all EDS spectra, the dead time during acquisition is about 30%, for an average number of hits per spectrum greater than 106. The compositions are calculated from Oxford Instruments standards made of metals, synthetic compounds and natural minerals. Six measurements were carried out on each pellet (the area analyzed during each measurement is approximately 5.10-2 mm2). The results are expressed in mass percentages of oxides and are normalized to 100%. Ten major elements were quantified: Na2O, MgO, Al2O3, SiO2, P2O5, K2O, CaO, TiO2, MnO, Fe2O3 (total iron). Although results of elementary composition below 0.5 wt% lack quantitative precision, they are mentioned in the general composition as they hold qualitative information. Values below 0.01% must be considered unreliable. The EDS.csv file contains all the chemical compositions, with the following columns: sample: sample reference. analysis: analysis reference (1 to 6 per sample). date: date of the analysis. laboratory: analysis laboratory. fait, stratigraphy, isolat: stratigraphic unit. artefact: typology. LOI: loss on ignition (percent). Na2O, MgO, Al2O3, SiO2, P2O5, K2O, CaO, TiO2, MnO, Fe2O3 (total): oxide mass percents. XRD The data were collected with a D8 Advance (Bruker) diffractometer in Bragg-Brentano configuration working at 1.6 kW (40 kV, 40 mA) and equipped with a copper anode source (kα1 = 1.5406 ; the kβ ray being removed by a Ni-filter in the diffracted beam). The tube was equipped with a 0.3° divergence slit and an 8 mm anti-scattering slit was mounted in front of the LynxEye© CCD detector. Data recording was performed from 3° to 70° (2θ), in steps of 0.02° with an acquisition time of 2 s, the sample being rotated. The stability of the device was controlled between the different series of measurements by the analysis of a standard (corundum crystal, NIST 1976). The XRD.zip archive contains all diffractograms in the Bruker raw format. The XRD.csv file contains all results expressed in counts, one sample per row, the first line gives the angular position (2 thêta) and the first columns contains the sample code.

The POLUSA dataset, as presented at the ACM/IEEE Joint Conference on Digital Libraries in August 2020 (JCDL '20)

csv files for quantifying artifact diversity. Further details at https://github.com/taphocoenose/Artifact_Class_Diversity.

NeMig represents a bilingual news collection and knowledge graphs on the topic of migration. The news corpora in German and English were collected from online media outlets from Germany and the US, respectively. NeMIg contains rich textual and metadata information, sentiment and political orientation annotations, as well as named entities extracted from the articles' content and metadata and linked to Wikidata. The corresponding knowledge graphs (NeMigKG) built from each corpus are expanded with up to two-hop neighbors from Wikidata of the initial set of linked entities. NeMigKG comes in four flavors, for both the German, and the English corpora: Base NeMigKG: contains literals and entities from the corresponding annotated news corpus; Entities NeMigKG: derived from the Base NeMIg by removing all literal nodes, it contains only resource nodes; Enriched Entities NeMigKG: derived from the Entities NeMig by enriching it with up to two-hop neighbors from Wikidata, it contains only resource nodes and Wikidata triples; Complete NeMigKG: the combination of the Base and Enriched Entities NeMig, it contains both literals and resources. Information about uploaded files: (all files are b-zipped and in the N-Triples format.) A description of the NeMigKG files is provided in the table below: NeMigKG Files Description File Description nemig_${language}_ ${graph_type}-metadata.nt.bz2 Metadata about the dataset, described using void vocabulary. nemig_${language}_ ${graph_type}-instances_types.nt.bz2 Class definitions of news and event instances. nemig_${language}_ ${graph_type}-instances_labels.nt.bz2 Labels of instances. nemig_${language}_ ${graph_type}-instances_related.nt.bz2 Relations between news instances based on one another. nemig_${language}_ ${graph_type}-instances_metadata_literals.nt.bz2 Relations between news instances and metadata literals (e.g. URL, publishing date, modification date, sentiment label, political orientation of news outlets). nemig_${language}_ ${graph_type}-instances_content_mapping.nt.bz2 Mapping of news instances to content instances (e.g. title, abstract, body). nemig_${language}_ ${graph_type}-instances_topic_mapping.nt.bz2 Mapping of news instances to sub-topic instances. nemig_${language}_ ${graph_type}-instances_sentiment_mapping.nt.bz2 Mapping of news instances to sentiment classes. emig_${language}_ ${graph_type}-instances_political_orientation_mapping.nt.bz2 Mapping of news outlets instances to political orientation classes. nemig_${language}_ ${graph_type}-instances_content_literals.nt.bz2 Relations between content instances and corresponding literals (e.g. text of title, abstract, body). nemig_${language}_ ${graph_type}-instances_sentiment_polorient_literals.nt.bz2 Relations between instances and corresponding sentiment or political orientation literals. nemig_${language}_ ${graph_type}-instances_metadata_resources.nt.bz2 Relations between news or sub-topic instances and entities extracted from metadata (i.e. publishers, authors, keywords). nemig_${language}_ ${graph_type}-instances_event_mapping.nt.bz2 Mapping of news instances to event instances. nemig_${language}_ ${graph_type}-event_resources.nt.bz2 Relations between event instances and entities extracted from the text of the news (i.e. actors, places, mentions). nemig_${language}_ ${graph_type}-resources_provenance.nt.bz2 Provenance information about the entities extracted from the text of the news (e.g. title, abstract, body). nemig_${language}_ ${graph_type}-wiki_resources.nt.bz2 Relations between Wikidata entities from news and their k-hop entity neighbors from Wikidata. The corresponding user data has been collected through online studies in Germany and the US. We used the participants' implicit feedback regarding their interest in an article to build their click history, and the explicit feedback in terms of news click behaviors to construct the impression logs. To protect user privacy, we assign each user an anonymized ID. The German and English user datasets are zip-compressed folders, which contain two files each. NeMig User Dataset File Description File Description behaviors.tsv The click history and impression logs of users. demographics_politics.tsv Demographic and political information of users. The behaviors.tsv file contains the users' news click histories and the impression logs. It has 4 columns divided by the tab symbol: Impression ID: the ID of an impression. User ID: The anonymized ID of an user. Click History: The news click history (list of news IDs) of a user before an impression. Impression Log: List of news displayed to the user in a session and the user's click behavior on them (1 for click, 0 for non-click). The demographics_politics.tsv file contains detailed information about the users' demographics and political interests. It has columns divided by the tab symbol. An explanation of all the columns and the questions used in the online studies to collect this information is shown in the table below. Demographic and political user data description Column Name Question in German study Scale in German Question in English study Scale in English Demographics Gender Bitte geben Sie Ihr Geschlecht an 0 = männlich 1 = weiblich 2 = divers 3 = Keine Angabe Please indicate your gender. 0 = male 1 = female 2 = other 3 = no answer Age Bitte geben Sie Ihr Alter an 1-120 Please indicate your age. 1-120 Qualification Welches ist Ihr höchster Bildungsabschluss? 0 = Kein Schulabschluss 1 = Haupt-/Gesamtschulabschluss 2 = Realschulabschluss, Mittlere Reife, Fachschulreife 3 = Fachhochschulreife, Abitur 4 = Studium mit Abschluss 5 = Promotion 6 = Keine Angabe Please indicate your highest educational qualification. 0 = less than high school 1 = high school/GED 2 = Vo-tech/business school 3 = some college 4 = college degree 5 = university degree 6 = doctoral degree 7 = no answer Nationality Welche Staatsangehörigkeit besitzen Sie? 0 = Nur die deutsche Staatsangehörigkeit 1 = Die deutsche und eine andere Staatsangehörigkeit 2 = Nur eine andere Staatsangehörigkeit 3 = Keine Angabe What is your citizenship? 0 = U.S. citizenship 1 = U.S. and another non-U.S. citizenship 2 = Only non-U.S. citizenship 3 = No Answer BornIn Sind Sie in Deutschland geboren? 0 = Ja 1 = Nein 2 = Keine Angabe Were you born in the U.S.? 0 = Yes 1 = No 2 = No answer ParentsBornIn Sind Ihre Eltern in Deutschland geboren? 0 = Mein Vater und meine Mutter sind beide in Deutschland geboren 1 = Mein Vater ist in Deutschland geboren, meine Mutter nicht 2 = Meine Mutter ist in Deutschland geboren, mein Vater nicht 3 = Weder meine Mutter noch mein Vater sind in Deutschland geboren 4 = Keine Angabe Were your parents born in the U.S.? 0 = My father and my mother were both born in the U.S. 1 = My father was born in the U.S., my mother was not 2 = My mother was born in the U.S., my father was not 3 = Neither my mother nor my father were born in the U.S 4 = No answer Income Was ist Ihr persönliches monatliches Nettoeinkommen (nach Abzug der Steuern)? Bitte geben Sie eine ungefähre Schätzung an, falls Sie die genaue Zahl nicht kennen. 0 = Weniger als 1000 € 1 = 1001 € bis 2000 € 2 = 2001 € bis 3000 € 3 = 3001 € bis 4000 € 4 = 4001 € bis 5000 € 5 = Mehr als 5000 € 6 = Keine Angabe What is your personal monthly net income (after taxes)? Please give an approximate estimation in case you are unsure. 0 = Less than 1000 $ 1 = 1001 $ to 2000 $ 2 = 2001 $ to 3000 $ 3 = 3001 $ to 4000 $ 4 = 4001 $ to 5000 $ 5 = More than 5000 $ 6 = No Answer Empathy Wie sehr stimmen Sie den folgenden Aussagen zu? 7-point Likert scale 1=Trifft überhaupt nicht zu 7=Trifft voll und ganz zu How strongly do you agree with the following statements? 7-point Likert scale 1=Strongly disagree 7=Strongly agree EMP1 Wenn jemand anderes erfreut ist, tendiere ich dazu auch erfreut zu sein. When someone else is feeling excited, I tend to get excited too. EMP2 Es regt mich auf, wenn jemand respektlos behandelt wird. It upsets me to see someone being treated disrespectfully. EMP3 Es macht mir Freude, andere aufzumuntern. I enjoy making other people feel better. EMP4 Ich bin besorgt um Personen, die weniger Glück haben als ich. I have tender, concerned feelings for people less fortunate than me. EMP5 Ich fühle, wenn andere traurig sind, selbst wenn sie nichts sagen. I can tell when others are sad even when they do not say anything. EMP6 Meistens bin ich mit den Stimmungen anderer Leute im Einklang. I find that I am “in tune” with other people’s moods. EMP7 Ich empfinde einen starken Drang zu helfen, wenn ich jemanden sehe, der aufgebracht ist. I get a strong urge to help when I see someone who is upset. EMP8 Wenn ich jemanden sehe, der ausgenutzt wird, möchte ich die Person beschützen. When I see someone being taken advantage of, I feel kind of protective towards him\her. Big5 Ich bin... 7-point Likert scale 0 = Sehr 1 = Ziemlich 2 = Etwas 3 = Teils=Teils 4 = Etwas 5 = Zeimlich 6 = Sehr I see myself as... 7-point Likert scale 1=Strongly disagree 7=Strongly agree BIG1 extrovertiert -- introvertiert ...extroverted, enthusiastic BIG2 emotional -- ausgeglichen ...critical, quarrelsome BIG3 aufgeschlossen -- festgelegt ...dependable, self-disciplined BIG4 barsch -- umgänglich ...anxious, easily upset BIG5 gewissenhaft -- nachlässig ...open to new experiences, complex BIG6 - ...reserved, quiet BIG7 - ...sympathetic, warm BIG8 - ...disorganized, careless BIG9 - ..calm, emotionally stable BIG10 - ...conventional, uncreative Ideological Polarization Im Folgenden sehen Sie eine Reihe von gegensätzlichen Aussagen. Bitte geben Sie jeweils an, wie sehr Sie der Aussage zustimmen oder diese ablehnen. Es gibt keine richtigen oder falschen Antworten. 7-point Likert scale 0 = Sehr 1 = Ziemlich 2 = Etwas 3 = Teils=Teils 4 = Etwas 5 = Zeimlich 6 = Sehr In the following, you will see a series of opposing statements. Please indicate how strongly you agree or disagree with the statements. There are no right or wrong answers. 7-point Likert scale 1=Strongly disagree 7=Strongly agree IPO1 Deutschland sollte mehr Geflüchtete aufnehmen. The U.S. should take in more refugees. IPO2 Deutschland hat schon zu viele Flüchtlinge aufgenommen. The U.S. should take in more refugees. IPO3 Deutschland sollte sich für sichere und einfache Fluchtwege nach Europa einsetzen. The U.S. should advocate safe and easy escape routes to North America. IPO4 Deutschland sollte sich dafür einsetzen, dass Flüchtlinge nicht einfach nach Europa kommen können. The U.S. should work to ensure that refugees cannot easily come to North America. IPO5 Immigranten bemühen sich um ein friedliches Zusammenleben mit Deutschen. Immigrants strive for peaceful cohabitation with U.S.-Americans. IPO6 Immigranten treten den Deutschen feindselig gegenüber. Immigrants are hostile toward U.S.-Americans. IPO7 Immigranten wollen auf Kosten des deutschen Wohlstands leben. Immigrants want to live at the expense of U.S.-American prosperity. IPO8 Immigranten helfen dabei, den deutschen Wohlstand zu sichern. Immigrants help in securing U.S.-American prosperity. IPO9 Immigranten bedrohen die deutsche Kultur und Lebensweise. Immigrants threaten the U.S.-American culture and lifestyle. IPO10 Immigranten bereichern die deutsche Kultur und Lebensweise. Immigrants enrich the U.S.-American culture and lifestyle. IPO11 Immigranten sind krimineller und gewalttätiger als Deutsche. Immigrants are more criminal and more violent than U.S.-Americans. IPO12 Immigranten sind nicht krimineller oder gewalttätiger als Deutsche. Immigrants are not more criminal and violent than U.S.-Americans. Emotions Welche Emotionen empfinden Sie gegenüber Geflüchteten und Immigranten in Deutschland? 7-point Likert Scale 1 = Stimme überhaupt nicht zu 7 = Stimme voll zu Which emotions do you feel towards refugees and immigrants in the USA? 7-point Likert Scale 1=Strongly disagree 7=Strongly agree EMO1 Wut Anger EMO2 Angst Fear EMO3 Verachtung Contempt EMO4 Trauer Grief EMO5 Ekel Disgust EMO6 Neid Envy EMO7 Schadenfreude Gloat EMO8 Mitleid Pity EMO9 Mitgefühl Compassion EMO10 Bewunderung Admiration EMO11 Freude Joy EMO12 Hoffnung Hope EMO13 Dankbarkeit Gratitude EMO14 Ehrfurcht Awe Media Usage Informationen über die deutsche Politik bekomme ich aus/von: 7-point Likert Scale 1=Nie 7=Sehr häufig I receive information about US-American politics via: 7-point Likert Scale 1=Never 7=Very Often MED1 Zeitungen und Magazinen oder deren Internet-Angeboten (z.B. BILD-Zeitung/ bild.de, Der Spiegel/spiegel.de, ...) newspapers and magazines or their websites (e.g. New York Times, The Wallstreet Journal, ...) MED2 dem Fernsehen oder deren Internet-Angeboten (z.B. ARD/ard.de, RTL /rtl.de, ...) TV networks or their websites (e.g. Fox News, CNN... ) MED3 dem Radio deren Internet-Angeboten (z.B. Energy/energy.de, Deutschlandfunk/deutschlandfunk.de, …) radio stations or their websites (e.g. WHTZ-FM, KIIS-FM, … ) MED4 Facebook (zur politischen Information) Facebook (for political information) MED5 Twitter (zur politischen Information) Twitter (for political information) MED6 Instagram (zur politischen Information) Instagram (for political information) MED7 Messenger Diensten wie z.B. WhatsApp und Telegram (zur politischen Information) messenger services such as WhatsApp or Telegram (for political information) MED8 YouTube (zur politischen Information) YouTube (for political information) MED9 Politischen Blogs und/oder speziellen Nachrichtenanbietern, die es nur im Internet gibt political blogs and/or alternative news providers, which can only be found online Participation Können Sie sich vorstellen, in naher Zukunft… 7-point Likert Scale 1=Kann ich mir gar nicht vorstellen 7=Kann ich mir gar nicht vorstellen Please indicate how likely it is that you will engage in the following activities in the near future. 7-point Likert Scale 1=Not likely at all 7=Very likely PPA1 … an einer politische Onlinediskussion zum Thema Immigration in Deutschland teilzunehmen? Participating in an online political discussion on the topic of immigration to the U.S. PPA2 … eine politische Onlinepetition zum Thema Immigration in Deutschland zu unterschreiben? Signing an online political petition on the topic of immigration to the U.S. PPA3 … eine/n Politiker/in in Deutschland zum Thema Immigration mit einer E-Mail oder über Social Media zu kontaktieren? Contacting a U.S.-American politician on the topic of immigration via e-mail or social media. PPA4 … einer politischen Partei oder Gruppe auf Social Media zu folgen, die sich besonders im Themenfeld Immigration in Deutschland engagiert? Following a party or group on social media, that is particularly engaged in the field of immigration to the U.S. PPA5 … einer politischen Partei oder Gruppe Geld zu spenden, die sich besonders im Themenfeld Immigration in Deutschland engagiert? Donating money to a political party or group that is especially involved in the field of immigration to the U.S. PPA6 … an einer politischen Demonstration zum Thema Immigration in Deutschland teilzunehmen? Participating in a political demonstration on the topic of immigration to the U.S. PPA7 … einer politischen Partei oder einer Gruppe beizutreten, die sich besonders im Themenfeld Immigration in Deutschland engagiert? Joining a political party or group that is especially involved in the field of immigration to the U.S. PPA8 … für eine politische Partei, oder einer Gruppe Freiwilligenarbeit zu leisten, die sich besonders im Themenfeld Immigration in Deutschland engagiert? Volunteering for a political party or group that is especially involved in the field of immigration. Perceived Polarization Wie bewerten Sie folgende Aussagen? 7-point Likert Scale 1 = Stimme überhaupt nicht zu 7 = Stimme voll zu How strongly do you agree or disagree with the following statements? 7-point Likert Scale 1=Strongly disagree 7=Strongly agree PRO1 Die Anhänger der verschiedenen politischen Parteien in Deutschland stehen sich immer feindseliger gegenüber. Democratic and Republican partisans in the U.S. are increasingly hostile to one another. PRO2 Die Anhänger der verschiedenen politischen Parteien in Deutschland haben sich immer weniger zu sagen. There is less and less common meeting ground between Democratic and Republican partisans in the U.S. PRO3 Die Anhänger der verschiedenen politischen Parteien in Deutschland sind sehr polarisiert. Democratic and Republican partisans in the U.S. are very polarized. PRO4 Die Meinungen zum Thema Immigration gehen in der deutschen Bevölkerung immer weiter auseinander. Opinions about immigration issues are increasingly diverging in U.S. society. PRO5 Es wird immer schwieriger, in der deutschen Bevölkerung Einigung zu Fragen der Immigration zu erreichen. It is becoming increasingly difficult to reach agreement on immigration issues among the U.S. population. PRO6 Das Thema Immigration spaltet die Menschen in Deutschland. Immigration issues are dividing the people in the U.S. Affective polarization Hier sehen Sie die Liste aller im Bundestag vertretenen Parteien. Bitte markieren Sie auf jeder der Skalen wie positiv oder negativ Sie für die jeweilige Partei empfinden. 0 (negative) to 100 (positive) In the following we would like to know about your party identification. Please mark on the scale how warm or cold you feel towards the respective parties. 0 (negative) to 100 (positive) CDU / Rep CDU Republican SPD / Dem SPD Democrat GRU GRU - FDP FDP - LIN LIN - AFD AFD - Political Scale (POL1) Wo würden Sie Ihren eigenen politischen Standpunkt auf der folgenden Skala einordnen? 11-point Likert Scale 1 = Links 6 = Mitte 11 = Rechts Where on the scale would you place your political point of view? 11-point Likert Scale 1 = Left 6 = Center 11 = Right Political Topics Bitte geben Sie an, inwiefern die folgenden Aussagen auf Sie zutreffen? 7-point Likert Scale 1 = Trifft überhaupt nicht zu 7 = Trifft voll und ganz zu Please indicate how strongly you agree or disagree with the following statements. 7-point Likert Scale 1 = Strongly disagree 7 = Strongly agree POL2 Ich interessiere mich im Allgemeinen sehr für Politik. I am generally very interested in politics. POL3 Ich informiere mich regelmäßig über das aktuelle politische Geschehen in Deutschland. I regularly inform myself about current political affairs in the U.S.. POL4 Mir ist es wichtig, über das aktuelle politische Geschehen in Deutschland informiert zu sein. It is important to me to be informed about current political affairs in the U.S.. POL5 Ich lese viele politische Nachrichtenartikel. I read many political news articles. POL6 Im Vergleich zu meinen Freunden bin ich ein Experte für das aktuelle politische Geschehen. Compared to my friends, I am an expert on current political affairs. POL7 Ich interessiere mich sehr für das Thema Immigration und die Immigrationspolitik in Deutschland. I am very interested in the topic of immigration and U.S. immigration policy. POL8 Ich informiere mich regelmäßig über Neuigkeiten zum Thema Immigration und Immigrationspolitik in Deutschland. I try to keep up-to-date with news about immigration and U.S. immigration policy. POL9 Mir ist es wichtig, über die aktuellen Entwicklungen zum Thema Immigration und Immigrationspolitik in Deutschland informiert zu sein. It is important to me to be informed about current developments in the field of immigration and U.S. immigration policy. Prosocial behavior Bitte geben Sie nachfolgend an wie sehr Sie den Aussagen zustimmen. Please indicate how strongly you agree or disagree with the following statements. PRO1 Ich wäre bereit, Gegenstände (z. B. Kleidung, Spielzeug, Möbel, Elektrogeräte) für Geflüchtete in Deutschland zu spenden. 7-point Likert Scale 1 = Stimme überhaupt nicht zu 7 = Stimme voll zu I would be willing to donate items (e.g. clothing, toys, furniture, electronics) to refugees living in the U.S. 7-point Likert Scale 1 = Strongly disagree 7 = Strongly agree PRO2 Ich wäre dazu bereit, Geflüchtete im Alltag zu unterstützen (z. B. Behördengänge begleiten, Deutschunterricht geben, eine gemeinsame Freizeitaktivität unternehmen). 7-point Likert Scale 1 = Stimme überhaupt nicht zu 7 = Stimme voll zu I would be willing to support refugees living in the U.S. with their everyday life (e.g. support with bureaucratic procedures, teaching English, leisure activities). 7-point Likert Scale 1 = Strongly disagree 7 = Strongly agree PRO3 Ich wäre bereit __ € für Geflüchtete in Deutschland zu spenden. float number >= 0 I am willing to make a one-time donation of __$ for refugees in the U.S. float number >= 0 PRO4 Wie häufig haben Sie beruflich (z. B. auf der Arbeit, im Studium) Kontakt mit Menschen mit Migrationshintergrund? 7-point Likert Scale 1 = Nie 7 = Sehr häufig How often do you have professional contact (e.g. at work or at school) with immigrants? 7-point Likert Scale 1 = Never 7 = Very often PRO5 Wie häufig haben Sie privat (z. B. Freunde, Verwandte, Bekannte) Kontakt mit Menschen mit Migrationshintergrund? 7-point Likert Scale 1 = Nie 7 = Sehr häufig How often do you have private contact (e.g. friends, relatives, acquaintances) with immigrants? 7-point Likert Scale 1 = Never 7 = Very often

Le dépôt correspond à l’export de la base de données Titulus (http://titulus.huma-num.fr/exist/apps/titulus/index.html), dans son état du 30 août 2022. Elle contient l’édition XML-TEI de 286 inscriptions médiévales (VIIIe-XIIIe s.) également publiées dans Corpus des inscriptions de la France médiévale (CIFM). Les volumes concernés sont les numéros 25 (Départements de l’Indre, Indre-et-Loire et Loir-et-Cher), 26 (Département du Cher), ainsi que les hors-série I (Inscriptions carolingiennes) et II (Nouvelle édition des inscriptions de Poitiers).

Description Magi Open Information Extraction Dataset (MOIED) is a Chinese Open IE dataset containing 7,618,181 records extracted from plain text across 3,319,763 webpages in various domains. Each record in the dataset consists of the (subject, predicate, object) tuple, the associated confidence score, and the context information. The dataset comprises 1,427,742 distinct facts of 272,522 entities and 117,731 predicates. A notable property of MOIED is that each distinct fact has multiple records with URLs referring to mentions in diverse contexts, which enables multiple-instance learning (MIL) and other correlative approaches. As a paragraph level Open IE dataset, at least 45.1% of the records in MOIED can only be extracted through synthesizing information from multiple sentences. Magi is an extraction engine that continuously learns from the Internet, which combines cross-referencing, timeline analysis, and other heuristics to mitigate the inevitable false positives in the extractions. All records in MOIED were randomly sampled from a database dump of magi.com in January 2020. To provide more reliable evaluation results, human annotators examined the dataset and selected 19,161 verified records for the dev and test sets. Disclaimers The dataset is expected to be used in weakly supervised scenarios since the records in the training set are not human-annotated and could be imprecise or erroneous. Records are not guaranteed to be universally correct. The correctness of extractions should be evaluated based on contexts (specified by the URLs). The extraction was made at a certain time Magi visits the URL, thus it is not guaranteed that the URL is still accessible, or the content is unmodified since the extraction was conducted. Due to legal and regulatory issues, the webpage URLs are mostly ones accessible from Mainland China, yet, the content of certain webpages, as well as the extraction results, could be in violation of law and regulation of certain countries or regions in certain ways. This dataset contains content from the Internet, for copyright reasons, please do not redistribute or use it for non-research purposes.

This dataset was assembled to train a neural network to undertake a multi-task, multi-class classification challenge, as well as to facilitate parameterized image searches for research queries in the humanities. This is significant for several reasons. To illustrate, large quantities of textual and visual data can be processed more efficiently through specially trained neural networks. Additionally, parameterized image searches permit a detailed examination and analysis of visual data, enabling tasks such as identifying image copies or tracking the evolution and reuse of image motifs. The images hail from diverse research fields, including art and architecture, design, and life sciences. Each image has been classified for two tasks: media type classification and content type classification. The content type, which refers to the subject depicted in the image, is categorized into 14 classes, whereas the media type, denoting whether the image is a graphic, photograph, or drawing, is divided into 3 classes. The class distribution is skewed, with a single class housing a large number of entries and the remaining classes having fewer. For example, in the content type, the "object" class contains the bulk of the data, whereas classes like "musical notation" and "map" form the tail, each with fewer than 100 examples in the training and validation subsets. Similarly, in the media type, the "graphic" subset contains the majority of samples, with the "photography" and "drawing" subsets containing considerably fewer.

We collect this dataset from some mental health-related subreddits in https://www.reddit.com/ to further the study of mental disorders and suicidal ideation. We name this dataset as Reddit SuicideWatch and Mental Health Collection, or SWMH for short, where discussions comprise suicide-related intention and mental disorders like depression, anxiety, and bipolar. We use the Reddit official API and develop a web spider to collect the targeted forums. This collection contains a total of 54,412 posts. Specific subreddits are listed in Table 4 of the below paper, as well as the number and the percentage of posts collected in the train-val-test split. This dataset is only for research. Please request with your institutional email. If you use this dataset, please cite the paper as: Ji, S., Li, X., Huang, Z. et al. Suicidal ideation and mental disorder detection with attentive relation networks. Neural Comput & Applic (2021). https://doi.org/10.1007/s00521-021-06208-y @article{ji2021suicidal, title={Suicidal ideation and mental disorder detection with attentive relation networks}, author={Ji, Shaoxiong and Li, Xue and Huang, Zi and Cambria, Erik}, journal={Neural Computing and Applications}, year={2021}, publisher={Springer} }

Images from all 46 archaeological test bones associated with the archaeological test set MALDI-TOF data. MALDI-TOF data from both reference and test sets. Reference sets are labeled with their species identification. Each set consists of .tex files. Files in the same folder are technical replicates of the same extract.