Find research datasets worth reusing

opencc-by-4.0Oct 2024View details →

Hormone Replacement Therapy Social Media Claims

The claims made in social media posts about hormone replacement therapy, the tpye of person/organisation posting these claims, whether the claims agree with NICE/BNF guidance and whether the person/organisation posting these claims has a conflict of interest. Full unredacted dataset available on request to: <a href="mailto:mm494@st-andrews.ac.uk">mm494@st-andrews.ac.uk</a>

opencc-by-4.0Nov 2024View details →

Disinformation on YouTube: A dataset of YouTube comments on videos related to claims made by Trump and Vance on Haitian immigrants

<div> <div> <div> <div> <div> The corpus contains three files. First, the youtube_haitian_disinformation_videos_meta.csv file includes comments and YouTube video metadata. Data is organized around per video information. The columnar values are:  </div> </div> </div> <div> <ul> <li> video_id    </li> <li> date (video publication date)    </li> <li> title    </li> <li> description    </li> <li> channel_title    </li> <li> transcript    </li> <li> transcript_str (Video transcript without timestamps)    </li> <li> views    </li> <li> likes    </li> <li> comments (all comments per video)  </li> </ul> </div> <div> <div> <div> Second, the youtube_haitian_disinformation_comment_reply_metadata.csv file includes comments, replies, and comment metadata. Each comment occupies its own row in the spreadsheet. The columnar field are:  </div> </div> </div> <div> <ul> <li> video_id    </li> <li> comment    </li> <li> comment_date    </li> <li> comment_like_count    </li> <li> author    </li> <li> comment_id    </li> <li> in_reply_to    </li> <li> neg, neu, pos, compound (VADER polarity scores)    </li> <li> named_entities (spaCy named entity tags with tokens)    </li> <li> emoji (spaCy Emojis and token spans)  </li> </ul> </div> </div> <div> <div> <div> <div> Comments and associated metadata are represented in individual rows.  </div> <div> The third file contains the results of the TFIDF analysis described herein. The TFIDF analysis features the top 5000 terms weights for the comments to each video in a .csv file.  </div> </div> </div> <div> <ul> <li> youtube_disinfo_comments_tfidf_results_per_video.csv  </li> </ul> </div> </div> </div>

opencc-by-4.0Jan 2024View details →

Health Insurance Claims

The dataset is eligible in exploring Health Insurance fraud Claims using machine learning algorithms. Its well suited for students developimg ML models to predict Healthcare insurance claims fraud.

opencc-by-4.0Dec 2023View details →

Global Claims Dataset

Collection of claims collected from different fact-checking websites, covering various languages and topics. Described in "Global Claims: A Multilingual Dataset of Fact-Checked Claims with Veracity, Topic, and Salience Annotations"

zenodo44/100

patccat: A classifier for patent claims

Data version: 3.3.0 Authors: Bernhard Ganglmair (University of Mannheim, Department of Economics, and ZEW Mannheim) W. Keith Robinson (Wake Forest University, School of Law) Michael Seeligson (Southern Methodist University, Cox School of Business) 1. Notes on Data Construction 2. Citation and Code 3. Description of the Data Files 3.1. File List 3.2. List of Variables for Files with Claim-Level Information 3.3. List of Variables for Files with Patent-Level Information 4. Coming Soon! 1. Notes on Data Construction This is version 3.3.0 of the patccat data (patent claim classification by algorithmic text analysis). Patent claims define an invention. A patent application is required to have one or more claims that distinctly claim the subject matter which the patent applicant regards as her invention or discovery. We construct a classifier of patent claims that identifies three distinct claim types: process claims, product claims, and product-by-process claims. For this classification, we combine information obtained from both the preamble and the body of a claim. The preamble is a general description of the invention (e.g., a method, an apparatus, or a device), whereas the body identifies steps and elements (specifying in detail the invention laid out in the preamble) that the applicant is claiming as the invention. The combination of the preamble type and the body type provides us with a more detailed and more accurate classification of claims than other approaches in the literature. This approach also accounts for unconventional drafting approaches. We eventually validate our classification using close to 10,000 manually classified claims. The data files contain the results of our classification. We provide claim-level information for each independent claim of U.S. utility patents granted between 1836 and 2020. We also provide patent-level information, i.e., the counts of different claim types for a given patent. For a detailed description of our classification approach, please take a look at the accompanying paper (Ganglmair, Robinson, and Seeligson 2022). 2. Citation Please cite the following paper when using the data in your own work: Ganglmair, Bernhard, W. Keith Robinson, and Michael Seeligson (2022): "The Rise of Process Claims: Evidence from a Century of U.S. Patents," unpublished manuscript available at <a href="https://papers.ssrn.com/abstract=4069994">https://papers.ssrn.com/abstract=4069994</a>. In the paper, we document the use of process claims in the U.S. over the last century, using the patccat data. We show an increase in the annual share of process claims of about 25 percentage points (from below 10% in 1920). This rise in process intensity of patents is not limited to a few patent classes, but we observe it across a broad spectrum of technologies. Process intensity varies by applicant type: companies file more process-intense patents than individuals, and U.S. applicants file more process-intense patents than foreign applicants. We further show that patents with higher process intensity are more valuable but are not necessarily cited more often. Last, process claims are on average shorter than product claims (with the gap narrowing since the 1970s). We would love to see how other researchers use the data and eventually learn from it. If you have a discussion paper or a publication in which you use the data, please send us a copy at patccat.data@gmail.com. We will the R code used to construct the data on Github with the next data version (version 3.4.0). Contact us at b.ganglmair@gmail.com if you would like to take a look at an earlier version of the code. 3. Description of the Data Files The data files contain claim-level information for independent claims of 10,140,848 U.S. utility patents granted between 1836 and 2020. The files further contain patent-level information for U.S. utility patents. 3.1. File List File list <table><tbody> <tr> <td>claims-patccat-v3-3-sample.csv</td> <td>claim-level information for independent claims of a sample of 1000 patents issued between 1976 and 2020</td> </tr> <tr> <td>claims-patccat-v3-3-1836-1919.csv</td> <td>claim-level information for independent claims of 1,038,041 patents issued between 1836 and 1919</td> </tr> <tr> <td>claims-patccat-v3-3-1920-2020.csv</td> <td>claim-level information for independent claims of 9,102,807 patents issued between 1920 and 2020</td> </tr> <tr> <td>patents-patccat-v3-3-sample.csv</td> <td>patent-level information for a sample of 1000 patents issued between 1976 and 2020</td> </tr> <tr> <td>patents-patccat-v3-3-1836-1919.csv</td> <td>patent-level information for 1,038,041 patents issued between 1836 and 1919</td> </tr> <tr> <td>patents-patccat-v3-3-1920-2020.csv</td> <td>patent-level information for 9,102,807 patents issued between 1920 and 2020</td> </tr> </tbody> </table> 3.2. List of Variables for Files with Claim-Level Information For detailed descriptions, see the appendix in Ganglmair, Robinson, and Seeligson (2022). List of Variables (Claim-Level Information) <table><tbody> <tr> <td>PatentClaim</td> <td>patent claim identifier; 8-digit patent number and 4-digit claim number (Ex: 01234567-0001)</td> </tr> <tr> <td>singleLine</td> <td>=1 if claim is published in single-line format</td> </tr> <tr> <td>singleReformat</td> <td>outcome code of reformating of single-line claims</td> </tr> <tr> <td>Jepson</td> <td>=1 if claim is a Jepson claim</td> </tr> <tr> <td>JepsonReformat</td> <td>outcome code of reformating of Jepson claims</td> </tr> <tr> <td>inBegin</td> <td>=1 if claim begins with the word "in"</td> </tr> <tr> <td>wordsPreamble</td> <td>number of words in the claim preamble</td> </tr> <tr> <td>wordsBody</td> <td>number of words in the claim body</td> </tr> <tr> <td>dependentClaims</td> <td>number of dependent claims that refer to this independent claim</td> </tr> <tr> <td>isMeansPreamble</td> <td>=1 if term "means" is used in the preamble</td> </tr> <tr> <td>isMeansBody</td> <td>=1 if term "means" is used in the body</td> </tr> <tr> <td>isMeans</td> <td>=1 if term "means" is used anywhere in the claim (~ means-plus-function claim)</td> </tr> <tr> <td>processPreamble</td> <td>=1 if terms "method" or "process" are used in the preamble</td> </tr> <tr> <td>processBody</td> <td>=1 if terms "method" or "process" are used in the body</td> </tr> <tr> <td>processSimple</td> <td>=1 if terms "method" or "process" are used anywhere in the claim (for simple approach of process claim classification)</td> </tr> <tr> <td>claimType</td> <td>claim type of full classification (1 = process; 2 = product; 3 = product-by-process; 0 = no type)</td> </tr> <tr> <td>preambleType</td> <td>preamble type</td> </tr> <tr> <td>preambleTerm</td> <td>keyword used to classify preamble type</td> </tr> <tr> <td>preambleTermAlt</td> <td>alternative keyword (if preambleTerm were not used)</td> </tr> <tr> <td>preambleTextStub</td> <td>first 15 words of the preamble</td> </tr> <tr> <td>bodyType</td> <td>body type</td> </tr> <tr> <td>bodyLinesStep</td> <td>number of steps in the body</td> </tr> <tr> <td>bodyLinesElement</td> <td>number of elements in the body</td> </tr> <tr> <td>bodyLinesTotal</td> <td>total number of identified lines in the body</td> </tr> <tr> <td>label</td> <td>2-character label of the preamble-body combination; classification table maps label to claim type</td> </tr> </tbody> </table>   3.3. List of Variables for Files with Patent-Level Information For detailed descriptions, see the appendix in Ganglmair, Robinson, and Seeligson (2022). List of Variables (Patent-Level Information) <table><tbody> <tr> <td>patent_id</td> <td>U.S. patent number (8-digit patent number)</td> </tr> <tr> <td>claims</td> <td>number of independent claims (the sum of the four claim types: 0, 1, 2, and 3)</td> </tr> <tr> <td>noCategory</td> <td>number of claims without a classified type</td> </tr> <tr> <td>processClaims</td> <td>number of process claims</td> </tr> <tr> <td>productClaims</td> <td>number of product claims</td> </tr> <tr> <td>prodByProcessClaims</td> <td>number of product-by-process claims</td> </tr> <tr> <td>firstClaim</td> <td>type of the first claim (1 = process; 2 = product; 3 = product-by-process; 0 = no type)</td> </tr> <tr> <td>simpleProcessClaims</td> <td>number of process claims by simple approach (terms "method" or "process" anywhere in the claim)</td> </tr> <tr> <td>simpleProcessPreamble</td> <td>number of process claims by simple approach (terms "method" or "process" in the preamble)</td> </tr> <tr> <td>meansClaims</td> <td>number of means-plus-function claims</td> </tr> <tr> <td>meansFirst</td> <td>=1 if first claim is a means-plus-function claim</td> </tr> <tr> <td>JepsonClaims</td> <td>number of Jepson claims</td> </tr> <tr> <td>JepsonFirst</td> <td>=1 if first claim is a Jepson claim</td> </tr> </tbody> </table> Note: The following variables/fields are currently empty (March 30, 2020); we will populate these variables/fields with data version 3.4.0. preambleTerm preambleTermAlt preambleTextStub bodyLinesStep bodyLinesElement bodyLinesTotal Note: We will release the data for patents issued in 2021 with data version 3.4.0. 4. Coming Soon! We are working on a number of extensions of the patccat data. - With data version 3.4.0, we plan to release data for all published U.S. patent applications (2001 through 2021) - In late spring/early summer 2022, we will release data for patents issued by the European Patent Office (EPO) [Update: March 28, 2023: see <a href="https://doi.org/10.5281/zenodo.7776092">https://doi.org/10.5281/zenodo.7776092</a>] - In late spring/early summer 2022, we will release data for patents issued by the Canadian Intellectual Property Office (CIPO)

opencc-by-4.0Mar 2022View details →

zenodo44/100

PANACEA dataset - Heterogeneous COVID-19 Claims

The peer-reviewed publication for this dataset has been presented in the 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), and can be accessed here: https://arxiv.org/abs/2205.02596. Please cite this when using the dataset.   This dataset contains a heterogeneous set of True and False COVID claims and online sources of information for each claim.   The claims have been obtained from online fact-checking sources, existing datasets and research challenges. It combines different data sources with different foci, thus enabling a comprehensive approach that combines different media (Twitter, Facebook, general websites, academia), information domains (health, scholar, media), information types (news, claims) and applications (information retrieval, veracity evaluation).   The processing of the claims included an extensive de-duplication process eliminating repeated or very similar claims. The dataset is presented in a LARGE and a SMALL version, accounting for different degrees of similarity between the remaining claims (excluding respectively claims with a 90% and 99% probability of being similar, as obtained through the MonoT5 model). The similarity of claims was analysed using BM25 (Robertson et al., 1995; Crestani et al., 1998; Robertson and Zaragoza, 2009) with MonoT5 re-ranking (Nogueira et al., 2020), and BERTScore (Zhang et al., 2019).   The processing of the content also involved removing claims making only a direct reference to existing content in other media (audio, video, photos); automatically obtained content not representing claims; and entries with claims or fact-checking sources in languages other than English.   The claims were analysed to identify types of claims that may be of particular interest, either for inclusion or exclusion depending on the type of analysis. The following types were identified: (1) Multimodal; (2) Social media references; (3) Claims including questions; (4) Claims including numerical content; (5) Named entities, including: PERSON − People, including fictional; ORGANIZATION − Companies, agencies, institutions, etc.; GPE − Countries, cities, states; FACILITY − Buildings, highways, etc. These entities have been detected using a RoBERTa base English model (Liu et al., 2019) trained on the OntoNotes Release 5.0 dataset (Weischedel et al., 2013) using Spacy.   The original labels for the claims have been reviewed and homogenised from the different criteria used by each original fact-checker into the final True and False labels.   The data sources used are: - The CoronaVirusFacts/DatosCoronaVirus Alliance Database. https://www.poynter.org/ifcn-covid-19-misinformation/ - CoAID dataset (Cui and Lee, 2020) https://github.com/cuilimeng/CoAID - MM-COVID (Li et al., 2020) https://github.com/bigheiniu/MM-COVID - CovidLies (Hossain et al., 2020) https://github.com/ucinlp/covid19-data - TREC Health Misinformation track https://trec-health-misinfo.github.io/ - TREC COVID challenge (Voorhees et al., 2021; Roberts et al., 2020) https://ir.nist.gov/covidSubmit/data.html   The LARGE dataset contains 5,143 claims (1,810 False and 3,333 True), and the SMALL version 1,709 claims (477 False and 1,232 True).   The entries in the dataset contain the following information: - Claim. Text of the claim. - Claim label. The labels are: False, and True. - Claim source. The sources include mostly fact-checking websites, health information websites, health clinics, public institutions sites, and peer-reviewed scientific journals. - Original information source. Information about which general information source was used to obtain the claim. - Claim type. The different types, previously explained, are: Multimodal, Social Media, Questions, Numerical, and Named Entities.   Funding. This work was supported by the UK Engineering and Physical Sciences Research Council (grant no. EP/V048597/1, EP/T017112/1). ML and YH are supported by Turing AI Fellowships funded by the UK Research and Innovation (grant no. EP/V030302/1, EP/V020579/1).   References - Arana-Catania M., Kochkina E., Zubiaga A., Liakata M., Procter R., He Y.. Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims. NAACL 2022 https://arxiv.org/abs/2205.02596 - Stephen E Robertson, Steve Walker, Susan Jones, Micheline M Hancock-Beaulieu, Mike Gatford, et al. 1995. Okapi at trec-3. Nist Special Publication Sp,109:109. - Fabio Crestani, Mounia Lalmas, Cornelis J Van Rijsbergen, and Iain Campbell. 1998. “is this document relevant?. . . probably” a survey of probabilistic models in information retrieval. ACM Computing Surveys (CSUR), 30(4):528–552. - Stephen Robertson and Hugo Zaragoza. 2009. The probabilistic relevance framework: BM25 and beyond. Now Publishers Inc. - Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020. Document ranking with a pre-trained sequence-to-sequence model. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pages 708–718. - Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with bert. In International Conference on Learning Representations. - Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692. - Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, et al. 2013. Ontonotes release 5.0 ldc2013t19. Linguistic Data Consortium, Philadelphia, PA, 23. - Limeng Cui and Dongwon Lee. 2020. Coaid: Covid-19 healthcare misinformation dataset. arXiv preprint arXiv:2006.00885. - Yichuan Li, Bohan Jiang, Kai Shu, and Huan Liu. 2020. Mm-covid: A multilingual and multimodal data repository for combating covid-19 disinformation. - Tamanna Hossain, Robert L. Logan IV, Arjuna Ugarte, Yoshitomo Matsubara, Sean Young, and Sameer Singh. 2020. COVIDLies: Detecting COVID-19 misinformation on social media. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics. - Ellen Voorhees, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, William R Hersh, Kyle Lo, Kirk Roberts, Ian Soboroff, and Lucy Lu Wang. 2021. Trec-covid: constructing a pandemic information retrieval test collection. In ACM SIGIR Forum, volume 54, pages 1–12. ACM New York, NY, USA.

opencc-by-4.0Jul 2022View details →

zenodo44/100

The patccat classifier for patent claims - EPO edition

!!! This is the EPO/European version of the patccat classifier of patent claims. !!! Note: We use the same approach that we use for USPTO patents. For a detailed description, see <a href="https://doi.org/10.5281/zenodo.6395307">https://doi.org/10.5281/zenodo.6395307</a>. Data version: 3.4.0 Authors: Bernhard Ganglmair (University of Mannheim, Department of Economics, and ZEW Mannheim) W. Keith Robinson (Wake Forest University, School of Law) Michael Seeligson (Southern Methodist University, Cox School of Business) Please cite the following paper when using the data in your own work: Ganglmair, Bernhard, W. Keith Robinson, and Michael Seeligson (2022): "The Rise of Process Claims: Evidence from a Century of U.S. Patents," unpublished manuscript available at <a href="https://papers.ssrn.com/abstract=4069994">https://papers.ssrn.com/abstract=4069994</a>.

opencc-by-4.0Mar 2023View details →

opencc-by-4.0Jan 2021View details →

Political Claims Analysis: Support and Opposition to Migration in Portugal

Claims analysis of newspapers in Portugal, 1995-2014. Random sample of days, all articles on immigration and integration. This is a faithful replication of the methodology of the SOM project (http://www.som-project.eu) For the codebook, see Berkhout, Joost and Sudulich, Maria Laura, Codebook for Political Claims Analysis (2011). SOM Working Paper No. 2011-02. Available at SSRN: http://ssrn.com/abstract=1990221.

opencc-by-4.0Aug 2024View details →

Auto Insurance Claims

The data set can be crucial in predicting insurance claims in the US or fraud cases in insurance firms

opencc-zeroJan 2024View details →

Linked collectors and determiners for: Abyssal fauna of the UK-1 polymetallic nodule exploration claim, Clarion-Clipperton Zone, central Pacific Ocean: Echinodermata.

Natural history specimen data linked to collectors and determiners held within, "Abyssal fauna of the UK-1 polymetallic nodule exploration claim, Clarion-Clipperton Zone, central Pacific Ocean: Echinodermata". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/0751b002-8da2-436d-981f-a02ac44783bf">https://bionomia.net/dataset/0751b002-8da2-436d-981f-a02ac44783bf</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/0751b002-8da2-436d-981f-a02ac44783bf">https://gbif.org/dataset/0751b002-8da2-436d-981f-a02ac44783bf</a>. Formatted as a Frictionless Data package.

opencc-by-4.0Dec 2017View details →

Text-fig. 4. Known geographic distribution of Microtscoptini on a modern-day biome map (Arc-GIS feature TNC terrestrial ecoregions). 1 – Ertemte 1 and 2; 2 – Olan Chorea; 3 – Harr Obo 2; 4 – Shala; 5 – Baogeda Ula; 6 – Bilutu; 7 – Kholu (Southern Tuva); 8 – Sarayskoe (Olkhon Island); 9 – Hyargas-nuur; 10 – Petropavlovsk; 11 – Pavlodar; 12 – Akshauli; 13 – Selety 1A; 14 – Kedej 1A; 15 – Makovka; 16 – Cherevychne 3; 17 – Protopopovka 3; 18 – Verkhnya Krynytsa 2; 19 – Vasylivka 1; 20 – Lobkove; 21 – Rome; 22 – Bartlett Mountain; 23 – Bartlett Mountain (General); 24 – Juniper Creek; 25 – Little Valley; 26 – Stroud Claim; 27 – Kelley Road; 28 – Moonstone Formation; 29 – Lemoyne Quarry; 30 – Feltz Ranch; 31 – Cambridge; 32 – Rick Irwin Site; 33 – Rabbit Hole. 1–20, 30–32 – Steppe biomes, 21–29, 33 – xeric shrubland biomes. in Comments On The Age And Dispersal Of Microtoscoptini (Rodentia: Cricetidae)

Text-fig. 4. Known geographic distribution of Microtscoptini on a modern-day biome map (Arc-GIS feature TNC terrestrial ecoregions). 1 – Ertemte 1 and 2; 2 – Olan Chorea; 3 – Harr Obo 2; 4 – Shala; 5 – Baogeda Ula; 6 – Bilutu; 7 – Kholu (Southern Tuva); 8 – Sarayskoe (Olkhon Island); 9 – Hyargas-nuur; 10 – Petropavlovsk; 11 – Pavlodar; 12 – Akshauli; 13 – Selety 1A; 14 – Kedej 1A; 15 – Makovka; 16 – Cherevychne 3; 17 – Protopopovka 3; 18 – Verkhnya Krynytsa 2; 19 – Vasylivka 1; 20 – Lobkove; 21 – Rome; 22 – Bartlett Mountain; 23 – Bartlett Mountain (General); 24 – Juniper Creek; 25 – Little Valley; 26 – Stroud Claim; 27 – Kelley Road; 28 – Moonstone Formation; 29 – Lemoyne Quarry; 30 – Feltz Ranch; 31 – Cambridge; 32 – Rick Irwin Site; 33 – Rabbit Hole. 1–20, 30–32 – Steppe biomes, 21–29, 33 – xeric shrubland biomes.

opencc-by-4.0Dec 2017View details →

Text-fig. 1. Known geographic distribution of Microtoscoptini. 1 – Ertemte 1 and 2; 2 – Olan Chorea; 3 – Harr Obo 2; 4 – Shala; 5 – Baogeda Ula; 6 – Bilutu; 7 – Kholu (Southern Tuva); 8 – Sarayskoe (Olkhon Island); 9 – Hyargas-nuur; 10 – Petropavlovsk; 11 – Pavlodar; 12 – Akshauli; 13 – Selety 1A; 14 – Kedej 1A; 15 – Makovka; 16 – Cherevychne 3; 17 – Protopopovka 3; 18 – Verkhnya Krynytsa 2; 19 – Vasylivka 1; 20 – Lobkove; 21 – Rome; 22 – Bartlett Mountain; 23 – Bartlett Mountain (General); 24 – Juniper Creek; 25 – Little Valley; 26 – Stroud Claim; 27 – Kelley Road; 28 – Moonstone Formation; 29 – Lemoyne Quarry; 30 – Feltz Ranch; 31 – Cambridge; 32 – Rick Irwin Site; 33 – Rabbit Hole. in Comments On The Age And Dispersal Of Microtoscoptini (Rodentia: Cricetidae)

Text-fig. 1. Known geographic distribution of Microtoscoptini. 1 – Ertemte 1 and 2; 2 – Olan Chorea; 3 – Harr Obo 2; 4 – Shala; 5 – Baogeda Ula; 6 – Bilutu; 7 – Kholu (Southern Tuva); 8 – Sarayskoe (Olkhon Island); 9 – Hyargas-nuur; 10 – Petropavlovsk; 11 – Pavlodar; 12 – Akshauli; 13 – Selety 1A; 14 – Kedej 1A; 15 – Makovka; 16 – Cherevychne 3; 17 – Protopopovka 3; 18 – Verkhnya Krynytsa 2; 19 – Vasylivka 1; 20 – Lobkove; 21 – Rome; 22 – Bartlett Mountain; 23 – Bartlett Mountain (General); 24 – Juniper Creek; 25 – Little Valley; 26 – Stroud Claim; 27 – Kelley Road; 28 – Moonstone Formation; 29 – Lemoyne Quarry; 30 – Feltz Ranch; 31 – Cambridge; 32 – Rick Irwin Site; 33 – Rabbit Hole.

opencc-by-4.0Sep 2023View details →

Identifying and Aligning Medical Claims Made on Social Media with Medical Evidence

A synthetically generated dataset of medical claims.

opencc-by-4.0Dec 2022View details →

The ePEStemology database: a meta-analysis of 15 years of research on knowledge claims of Payments for Ecosystem Services (PES)

Introduction Payments for ecosystem services (PES) are a leading conservation finance tool to encourage land-users to deliver ecosystem services in exchange for financial incentives (Blundo-Canto et al., 2018; Kaiser et al., 2021; Salzman et al., 2018; Schomers and Matzdorf, 2013). They have generated considerable interest over the past two decades, following the publication of the UN’s Millennium Ecosystem Assessment (MEA, 2005). Yet, there has been a dearth of analysis that reveals the outcomes and evolution of the PES research body itself, with most studies having a very specific or limited focus on the theoretical and empirical development of these PES programmes. The ePEStemology database assembles a systematic literature review into a meta-analysis aimed at understanding knowledge generation in PES research. The database allows for an analysis of key trends in how the body of literature on PES has informed scholars and practitioners since the emergence of this conservation tool. Research questions and main objectives: The ePEStemology database is the largest review of PES scientific research to date and addresses the following research questions: <ul> <li>In the history of 15 years of PES research, what are the dominant criteria used to define and assess success of PES in practice? Are there geographical patterns in defining those criteria? Where does research take place? And by whom?</li> <li>What does 15 years of PES research illustrate in terms of the social and political consequences that arise in the implementation of PES? To what extent does literature engage with territorial dynamics and conflicting frameworks around nature’s values?</li> <li>How has PES research evolved? Do research recommendations feed into future research objectives over time?</li> <li>How does the epistemological position, including methods used, geographic location and disciplinary focus of authors among other variables, influence PES research outcomes?</li> </ul> A core objective of the database is to ensure it remains a living compendium of PES research. While the database does not account for every peer-reviewed published article on PES, it facilitates the possibility of iteratively adding new publications or retroactively adding in missing articles as well as those that use cognate terms to define PES (e.g. as rewards or compensation for ecosystem services).  The database also aims to serve as an important basis for future research questions on overall or regional trends emerging from PES research. Search strategy Using these key objectives and questions, we defined a set of variables (see attached) and search strategy to construct the ‘ePEStemology’ database. The database is populated by International Scientific Indexed (ISI) peer-reviewed journal articles. It includes Anglophone articles in Scopus and Web of Science (WoS), using all of the search terms “PES”, “Payments for Ecosystem Services,” “Payment for Ecosystem Services,” “Payment for Environmental Service,” or Payments for Environmental Services” either in the title, abstract, or keywords of queried articles.   Articles were excluded if they were produced in a language other than English or if they were book chapters, books, conference papers, reviews, or webpages. While recognizing the caveats of excluding research articles in other languages as well as in other media (e.g. as books or conference papers), we justify our approach in order to ensure consistency and comparability of the body of research literature. Following the different steps prescribed by the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) protocol (Moher et al., 2009), we identified a total of 1,067 published articles between 2005 and 2019. We took the year 2005 as the starting point for published research on PES, following the Millennium Ecosystem Assessment (Millennium Ecosystem Assessment, 2005) of the same year as well as an initial seminal publication on the subject (Wunder, 2005). Initial screening of articles began in September, 2018 with a total of 1,215 ISI-peer reviewed research articles on PES identified between both the Scopus and WoS queries. Additional articles were included to account for the year 2019 in October, 2020 to total 1,439 articles. It is also worth noting that Scopus providing the greatest coverage of published articles on PES, accounting for 78% of the total identified. WoS accounted for 66% of the total, but also included 305 articles that did not fall within the Scopus search. The screening procedure for selecting articles was contingent on the time in which the search in SCOPUS and WoS were conducted. It should be noted that this systematic review does not account for every ISI-research article published on PES during the time period considered. Since the database search tools are continuously updated retroactively, the query date may alter the number of articles retrieved in the search, increasing them over time despite being limited to specific dates. However, even if the database gets adapted over time, we can safely assume that the corpus represents a representative account of peer-reviewed published research on PES. In addition to the language exclusions made (i.e. our database only includes Anglophone publications), there are various permutations on the PES terminology, including “payments for hydrological services,” “payments for watershed services”, “conservation payments,” “rewards for ecosystem services,” or “agro-ecological incentives,” and many others that make it challenging to fully account for every relevant article. However, the protocol does not include all these possible variations, but can easily allow these variations to be included and brought into the analysis at a later stage. Coding strategy For the initial database (2005-2019) four independent reviewers to review each abstract and full text of each article were selected (BT, CJ, GVH, VK).  Each reviewer had experience in (empirical) PES research and have published peer-reviewed research on PES or other market-like transactions for ecosystem services (e.g. Tabaichount et al., 2019; Jacob et al., 2016; Van Hecken et al., 2015; Kolinjivadi and Sunderland, 2012). Broad variables of interest that guide our analysis (e.g. research objectives, disciplines, methods, author affiliations and positionality, geographical base, research outcomes, and others) served as the basis for coding. However, in order to establish a foundation of codes for each of these categories of interest and further refine each of those broader categories, the research team randomly selected 100 articles of the final 1,067 articles identified and applied a grounded theory open-coding process (e.g. Strauss and Corbin, 1990; Thornberg and Charmaz, 2014). The latter implies a process in which any preconceived identification for each category of interest was open to be further adapted prior to the final coding process. The iterative process of initial coding determined the most relevant sets of codes for each identified variable of interest, as well as possible values for each of them. Additional codes were added to the list as more articles were reviewed and until saturation was reached. After discussing and deciding on the final categories/variables with the whole group,  all remaining  articles in the corpus were evenly and randomly divided among all reviewers. On average, each of the four reviewers coded between 500 and 600 articles.    Subsequently, and for over a period of 15 months, the coding team revised and coded all articles based on the abstracts and a review of the full text in case the abstract did not allow to accurately code some of the variables. Each article was independently (blindly) coded by two different reviewers. To ensure robustness of coding, pairs of reviewers for each set compared results of the coding process for each article through a triangulation process and resolved discrepancies through collective deliberation and cross-checked consensus. Variable list The variable list and description of variables and their corresponding values can be found in the attached Word-document

opencc-by-4.0Jan 2020View details →

ClaimBuster: A Benchmark Dataset of Check-worthy Factual Claims

The ClaimBuster dataset consists of statements extracted from all U.S. general election presidential debates (1960-2016) along with human-annotated check-worthiness labels where each sentence is categorized into one of the three categories: non-factual statement, unimportant factual statement, and check-worthy factual statement. If you use this dataset, please cite the following paper: @inproceedings{arslan2020claimbuster,     title={{A Benchmark Dataset of Check-worthy Factual Claims}},     author={Arslan, Fatma and Hassan, Naeemul and Li, Chengkai and Tremayne, Mark },     booktitle={14th International AAAI Conference on Web and Social Media},     year={2020},     organization={AAAI} }

opencc-by-4.0Feb 2023View details →

Wikidata selection of Cultural Heritage, Stars, Galaxies and Random entities and claims

This work presents an analysis of the use of different representation methods in Wikidata to encode information with weaker logical status (e.g. contradictory information, temporally evolving information). The study examines three main approaches: ranked statements, null-valued objects, and qualified statements with properties P5102 (reason of statement), P1480 (sourcing circumstances) and P2241 (reason for deprecated rank) by analysing their prevalence, success, and clarity in Wikidata. The analysis is performed over cultural heritage artefacts stored in Wikidata divided in three subsets (i.e. visual heritage, textual heritage and audio-visual heritage) and compared with astronomical data (stars and galaxies entities from Wikidata) and a Random dataset (entities chosen randomly from the most used 100 classes). The findings indicate that (1) the representation of weaker logical status information is limited, with only a small proportion of items reporting such information, (2) the representation of WLS varies significantly between the two datasets. Finally, we propose several representation alternatives to simplify and standardize the representation of this type of information in Wikidata, with the hope of increasing its accuracy and richness

opencc-by-4.0Sep 2021View details →

Green Claims Dataset

This dataset contains 773 tweets of companies from the cosmetics and electronics domain. Each Tweets has a binary label as either a green claim or not green claim and a multi-class label as either Explicit green claim, Implicit green claim or Not green claim.  Green claims are defined as "all appeals that include ecological, environmental sustainability, or nature-friendly messages that target the needs and desires of environmentally concerned stakeholders."  An explicit green claim is different than an implicit green claim in that it is specific to certain products or processes of the company making the claim while implicit claims are vague in general and avoid any kind of commitments.

opencc-by-4.0Feb 2021View details →

Claims State Aid cases: Apple and Ilva

Datasets of the claims made by different actors during the politicisation of the Apple and Ilva state aid cases.  <div> </div>

opencc-by-4.0Apr 2024View details →

Exploring Behavioral Tendencies on Social Media: A Perspective Through Claim Check-Worthiness

<h2>Randomly Sampled Users Dataset (RSU.csv)</h2> This dataset consists of 11,173 users collected through Twitter's APIs. We collected 10,000 random English tweets in February 2023 using Twitter's Volume Stream API.  The tweets were posted by around 3,000 users.  For each user, we collected up to 100 of its most recent followees using Twitter's Following API. Through the Timeline and Liking APIs, for each user, we collected their most recent tweets (up to 3,200 tweets due to Twitter's limit) and liked-tweets (up to 3,200 too). We then filtered out users that have insufficient tweets (less than 100 original-tweets or less than 80 retweets/liked-tweets) to ensure that the sample sizes are statistically significant in our analyses.  Finally, we have 11,173 users along with 40,405,150 tweets.  <h2>Humanities Dataset (HUM.csv)</h2> This dataset contains 341,285 tweets and 498 Twitter accounts from selected Twitter lists including Book Author, Christianity, Artists, Buddhism, Musician, and Philosophers. We use Twitter's List and Timeline APIs to collect the accounts and their most recent tweets (up to 1,000). The dataset was collected in January 2024. <h2>Politics Dataset (POL.csv)</h2> This dataset contains all tweets from selected U.S. news media and U.S. politicians including Senators, House Members, US Governors, US Secretaries of State, US Cabinet, and US Election Officials at collection time. We used Twitter's Timeline API to collect the accounts' tweets (up to 3,200 tweets). The dataset was collected in May 2023, with 8,153,745 tweets and 3,784 Twitter accounts.  <h2>Data Fields</h2> Due to Twitter's content redistribution policy, we are only allowed to publish tweet IDs and user IDs. Therefore, in each dataset, each row/datapoint represent a tweet, containing two fields --- tweet_id and user_id.