cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10522969
title: The rural exodus of young people from empty Spain. Socio-educational aspects (Datasets)

description: Estos datos abordan la opinión de los miembros de los Grupos de Desarrollo Rural de una de las zonas más despobladas del sur de Europa. El objetivo de este estudio fue conocer su opinión sobre los principales factores que impulsan a los jóvenes a abandonar las tierras altas celtíberas, así como as medidas socioeducativas que estiman necesarias para evitar este éxodo. Un total de 165 participantes contestaron a un cuestionario de elaboración propia. Estas respuestas fueron complementadas con entrevistas y observaciones in situ. 

Estos datos están asociados a la siguiente publicación:

LLORENT-BEDMAR, V., COBANO-DELGADO PALMA, V. Y NAVARRO-GRANADOS, M. 2021. The rural exodus of young people from empty Spain. Socio-educational aspects. Journal of Rural Studies, 82, 303-314. https://doi.org/10.1016/j.jrurstud.2021.01.014. Editorial: PERGAMON-ELSEVIER SCIENCE LTD. País de publicación: England. ISSN: 0743-0167.
DOI: 10.6084/m9.figshare.25065890
title: Appendix A Data-base.pdf

description: The available data set is used in a study that analyze the different factors affecting the Egyptian real estate cycle.
DOI: 10.6084/m9.figshare.25065890.v1
title: Appendix A Data-base.pdf

description: The available data set is used in a study that analyze the different factors affecting the Egyptian real estate cycle.
DOI: 10.5281/zenodo.10011600
title: The TongueSwitcher Corpus of German-English Code-Switching

description: This is the TongueSwitcher Corpus of German-English code-switching tweets. Included are the train and dev sets with automatic word (and subword for mixed words) language identification, alongside the human-annotated corpus with test and interlingual homograph sets.

BibTeX entry and citation info

@inproceedings{sterner2023tongueswitcher,   author     = {Igor Sterner and Simone Teufel},   title          = {TongueSwitcher: Fine-Grained Identification of German-English Code-Switching},   booktitle  = {Sixth Workshop on Computational Approaches to Linguistic Code-Switching},   publisher = {Empirical Methods in Natural Language Processing},   year        = {2023},}
DOI: 10.5281/zenodo.10011601
title: The TongueSwitcher Corpus of German-English Code-Switching

description: This is the TongueSwitcher Corpus of German-English code-switching tweets. Included are the train and dev sets with automatic word (and subword for mixed words) language identification, alongside the human-annotated corpus with test and interlingual homograph sets.

BibTeX entry and citation info

@inproceedings{sterner2023tongueswitcher,   author     = {Igor Sterner and Simone Teufel},   title          = {TongueSwitcher: Fine-Grained Identification of German-English Code-Switching},   booktitle  = {Sixth Workshop on Computational Approaches to Linguistic Code-Switching},   publisher = {Empirical Methods in Natural Language Processing},   year        = {2023},}
DOI: 10.7910/dvn/f9blhq
title: European Organization for Nuclear Research and "MLK Technology and Science"

description: "Marek-Lars Kruusen's technology and science" is a company primarily engaged in scientific research on wormholes and technology development. Marek-Lars Kruusen collaborates with the European Organization for Nuclear Research (CERN). This collaboration involves the publication of scientific papers and preprints on a platform managed by CERN. For example, preprints can be found on Zenodo, which is hosted at CERN. The European Organization for Nuclear Research, known as CERN, is an intergovernmental organization that operates the largest particle physics laboratory in the world. Established in 1954, it is based in a suburb of Geneva, on the France–Switzerland border. It comprises 23 member states. Israel, admitted in 2013, is the only non-European full member. CERN is an official United Nations General Assembly observer. The acronym CERN is also used to refer to the laboratory; in 2019, it had 2,660 scientific, technical, and administrative staff members, and hosted about 12,400 users from institutions in more than 70 countries. In 2016, CERN generated 49 petabytes of data. CERN's main function is to provide the particle accelerators and other infrastructure needed for high-energy physics research – consequently, numerous experiments have been constructed at CERN through international collaborations. CERN is the site of the Large Hadron Collider (LHC), the world's largest and highest-energy particle collider. The main site at Meyrin hosts a large computing facility, which is primarily used to store and analyze data from experiments, as well as simulate events. As researchers require remote access to these facilities, the lab has historically been a major wide area network hub. CERN is also the birthplace of the World Wide Web. Since its foundation by 12 members in 1954, CERN regularly accepted new members. All new members have remained in the organization continuously since their accession, except Spain and Yugoslavia. Spain first joined CERN in 1961, withdrew in 1969, and rejoined in 1983. Yugoslavia was a founding member of CERN but quit in 1961. Of the 23 members, Israel joined CERN as a full member on 6 January 2014, becoming the first (and currently only) non-European full member. The Open Science movement focuses on making scientific research openly accessible and on creating knowledge through open tools and processes. Open access, open data, open source software and hardware, open licenses, digital preservation and reproducible research are primary components of open science and areas in which CERN has been working towards since its formation.
DOI: 10.23719/1526408
title: Lightning Assimilation WRF Data

description: The immediate data, except the lightning flash data behind the figures,
are available from https://doi.org/10.5281/zenodo.6493145
DOI: 10.3886/e164541
title: Data and Code for: Collateralized Marriage

description: Data and code to replicate empirical results in "Collateralized Marriage."<br><br>Abstract: Marriage rates have become increasingly stratified by homeownership. We investigate this in a household model where investments in public goods reduce future earnings, and thus divorce risk creates inefficiencies. Access to a joint savings technology, like a house, "collateralizes" marriage, providing insurance to the lower earning partner and increasing specialization, public goods, and value from marriage. We use idiosyncratic variation in housing prices to show that homeownership access indeed leads to greater specialization. The model also predicts that policies that erode the marriage contract in other ways will make wealth a more important determinant of marriage, which we confirm empirically.<br><br>Data includes compiled Census / American Community Survey Data, PSID data, SIPP data, ATUS data, and FHFA housing price data, all of which are publicly available.
DOI: 10.3886/e164541v1-145642
title: Data and Code for: Collateralized Marriage

description: Data and code to replicate empirical results in "Collateralized Marriage."<br><br>Abstract: Marriage rates have become increasingly stratified by homeownership. We investigate this in a household model where investments in public goods reduce future earnings, and thus divorce risk creates inefficiencies. Access to a joint savings technology, like a house, "collateralizes" marriage, providing insurance to the lower earning partner and increasing specialization, public goods, and value from marriage. We use idiosyncratic variation in housing prices to show that homeownership access indeed leads to greater specialization. The model also predicts that policies that erode the marriage contract in other ways will make wealth a more important determinant of marriage, which we confirm empirically.<br><br>Data includes compiled Census / American Community Survey Data, PSID data, SIPP data, ATUS data, and FHFA housing price data, all of which are publicly available.
DOI: 10.48511/8g94-g294
title: Coal Creek (COL005)

description: Temperature readings are taken at 1-hour intervals at Coal Creek downstream of the Cokato Road bridge as part of Elk River Alliance's Hydrometric program. Over time, water temperature data can be used to understand the effects of climate change on the Elk River and its tributaries.
DOI: 10.48511/v1h7-8422
title: Aldridge Creek (ALD001)

description: Temperature readings are taken at 1-hour intervals at Aldridge Creek (ALD001) downstream of Round Prairie FSR as part of Elk River Alliance's Hydrometric program. Over time, water temperature data can be used to understand the effects of climate change on the Elk River and its tributaries.
DOI: 10.5281/zenodo.10578037
title: Raw Data for paper "A Parametric Study of the SASI Comparing General Relativistic and Non-Relativistic Treatments" (2/2)

description: These gzipped tarballs contain all of the 2D data used in the paper. Each tarball corresponds to a different model. The naming convention is the same as in the paper. It also contains one gzipped tarball that holds the software used to generate the data for the figues in the paper.
DOI: 10.5281/zenodo.10578038
title: Raw Data for paper "A Parametric Study of the SASI Comparing General Relativistic and Non-Relativistic Treatments" (2/2)

description: These gzipped tarballs contain all of the 2D data used in the paper. Each tarball corresponds to a different model. The naming convention is the same as in the paper. It also contains one gzipped tarball that holds the software used to generate the data for the figues in the paper.
DOI: 10.34740/kaggle/dsv/7500203
title: Afluencia Metro de Medellín 2023
DOI: 10.5517/ccdc.csd.cc2gkbzc
title: CCDC 2280793: Experimental Crystal Structure Determination

description: Related Article: Baoqi Wu, Zhi-Wei Li, Feng Lin, Rongzhi Tang, Wanqing Zhang, Hongwei Liu, Gangfeng Ouyang, Yu Tan|2024|J.Hazard.Mater.|465|133449|doi:10.1016/j.jhazmat.2024.133449
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2gkc0f
title: CCDC 2280794: Experimental Crystal Structure Determination

description: Related Article: Baoqi Wu, Zhi-Wei Li, Feng Lin, Rongzhi Tang, Wanqing Zhang, Hongwei Liu, Gangfeng Ouyang, Yu Tan|2024|J.Hazard.Mater.|465|133449|doi:10.1016/j.jhazmat.2024.133449
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2gkc1g
title: CCDC 2280795: Experimental Crystal Structure Determination

description: Related Article: Baoqi Wu, Zhi-Wei Li, Feng Lin, Rongzhi Tang, Wanqing Zhang, Hongwei Liu, Gangfeng Ouyang, Yu Tan|2024|J.Hazard.Mater.|465|133449|doi:10.1016/j.jhazmat.2024.133449
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2gs69s
title: CCDC 2287375: Experimental Crystal Structure Determination

description: Related Article: Siyu Liang, Yuxin Zhou, Wei Yu|2024|Org.Lett.|26|613|doi:10.1021/acs.orglett.3c03883
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2h01rb
title: CCDC 2293961: Experimental Crystal Structure Determination

description: Related Article: Haijian Wu, Yanan Li, Manman Sun, Jing Zhang, Jinshan Li, Jianguo Yang|2024|Org.Lett.|26|751|doi:10.1021/acs.orglett.3c04251
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d3zvc
title: CCDC 2208373: Experimental Crystal Structure Determination

description: Related Article: Rong-Jin Zhang, Xiang-Rui Li, Rong-Bin Liang, Yonghong Xiao, Qing-Xiao Tong, Jian-Ji Zhong, Li-Zhu Wu|2024|Org.Lett.|26|591|doi:10.1021/acs.orglett.3c03740
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2h1jwz
title: CCDC 2295391: Experimental Crystal Structure Determination

description: Related Article: Yan-Xue Li, Wen-Chao Xiong, He Zheng, Xing-Cong Wang, Shouzhi Pu, Rongwei Shi, Qi Sun, Shan-Shan Gong|2024|Dyes Pigm.|223|111949|doi:10.1016/j.dyepig.2024.111949
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2h9wvk
title: CCDC 2303419: Experimental Crystal Structure Determination

description: Related Article: Yun-Long Zhu, Yi-Fan Dong, Si-Ru Wang, You-Gui Li, Xiang Wu, Long-Wu Ye|2024|Org.Lett.|26|631|doi:10.1021/acs.orglett.3c03856
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2hllj7
title: CCDC 2311779: Experimental Crystal Structure Determination

description: Related Article: Xiangyu Lu, Dingyi Liu, Yajun Zou, Jialiang Yang, Liji Wu, Xiaoyu Zhang, Gang Zhao|2024|J.Organomet.Chem.|1006|123022|doi:10.1016/j.jorganchem.2024.123022
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.7017178
title: Making virtual species less virtual by reverse engineering of spatiotemporal ecological models v3

description: The most up-to-date version of the archive that contains all the necessary data to perform analyses supporting the paper Making virtual species less virtual by reverse engineering of spatiotemporal ecological models (https://doi.org/10.1111/2041-210X.14176) .

The research was supported by the National Science Centre, Poland (grant no. 2018/29/B/NZ8/00066) and Poznań Supercomputing and Networking Centre (grant no. 403).
DOI: 10.5281/zenodo.7017179
title: Making virtual species less virtual by reverse engineering of spatiotemporal ecological models v1

description: The research was supported by the National Science Centre, Poland (grant no. 2018/29/B/NZ8/00066). The previous version of the archive that contains all data necessary to perform analyses supporting the paper "Making virtual species less virtual by reverse engineering of spatiotemporal ecological models".
DOI: 10.5281/zenodo.7061476
title: Making virtual species less virtual by reverse engineering of spatiotemporal ecological models v2

description: The research was supported by the National Science Centre, Poland (grant no. 2018/29/B/NZ8/00066). The previous version of the archive contains all data necessary to perform analyses supporting the paper "Making virtual species less virtual by reverse engineering of spatiotemporal ecological models".
DOI: 10.5281/zenodo.7732648
title: Making virtual species less virtual by reverse engineering of spatiotemporal ecological models v3

description: The most up-to-date version of the archive that contains all the necessary data to perform analyses supporting the paper Making virtual species less virtual by reverse engineering of spatiotemporal ecological models (https://doi.org/10.1111/2041-210X.14176) .

The research was supported by the National Science Centre, Poland (grant no. 2018/29/B/NZ8/00066) and Poznań Supercomputing and Networking Centre (grant no. 403).
DOI: 10.5281/zenodo.10558961
title: Datasets for "Auditing Elon Musk's Impact on Hate Speech and Bots"

description: Datasets for the publication "Auditing Elon Musk's Impact on Hate Speech and Bots" [1].

File information:



baseline_tweet_ids_2022.csv, hate_tweet_ids_2022.csv: List of IDs and their corresponding dates from the "baseline" and "hate" samples of tweets used in the publication, respectively. The former is used to create the number of baseline tweets each day (‘baseline_freq.csv’) while the latter is used to create the number of hate tweets each day (‘hate_freq.csv'). We share the date a tweet was made as well as its tweet ID from which you can find the original tweet’s URL with the help of this web page.  As you explore these data, you may notice in a minority of cases hate tweets that are not hateful or, alternatively, baseline tweets that are hateful. This is a product of our filtering method used to collect and analyze tweets at scale. We always look forward to hearing your suggestions to improve the tweet filtering process.

baseline_freq.csv, hate_freq.csv: Number of collected tweets per day for the baseline and hate samples, respectively. The file 'freq_data.py' is used to calculate these frequencies from the raw data. Feel free to consult this if you have questions about how the frequencies are calculated (or if you want to change how the data are aggregated). Use these data to recreate Figure 2 from Hickey et al [1]. See the Methods section of the publication for more details.

user_hate_levels_per_day.csv: CSV file with dates (YYYY-MM-DD format) and the mean proportion of slurs used by hateful users each day from October 1st to November 30th, 2022. Use these data to recreate Figure 1 from Hickey et al [1]. See the Methods section for more details.

hate_keywords.txt: Words used to query the Twitter Academic API for hate tweets.

unfiltered_tweets_containing_hate_words.csv: All tweets with hate words collected with values for Perspective API attributes.


Reference:



Hickey, D., Schmitz, M., Fessler, D.M.T, Smaldino, P., Muric, G., & Burghardt, K. Auditing Elon Musk's Impact on Hate Speech and Bots. In Proceedings of the 17th International AAAI Conference on Web and Social Media, (2023).


 

V2 note: This is an updated version of this dataset that was previously uploaded on 1/23/2024. In the previous version, tweet IDs were stored as floating point values which truncated them. In this version, that issue has been fixed and all IDs are complete.
DOI: 10.5281/zenodo.10558962
title: Datasets for "Auditing Elon Musk's Impact on Hate Speech and Bots"

description: Datasets for the publication "Auditing Elon Musk's Impact on Hate Speech and Bots" [1].

File information:



baseline_tweet_ids_2022.csv, hate_tweet_ids_2022.csv: List of IDs and their corresponding dates from the "baseline" and "hate" samples of tweets used in the publication, respectively. The former is used to create the number of baseline tweets each day (‘baseline_freq.csv’) while the latter is used to create the number of hate tweets each day (‘hate_freq.csv'). We share the date a tweet was made as well as its tweet ID from which you can find the original tweet’s URL with the help of this web page.  As you explore these data, you may notice in a minority of cases hate tweets that are not hateful or, alternatively, baseline tweets that are hateful. This is a product of our filtering method used to collect and analyze tweets at scale. We always look forward to hearing your suggestions to improve the tweet filtering process.

baseline_freq.csv, hate_freq.csv: Number of collected tweets per day for the baseline and hate samples, respectively. The file 'freq_data.py' is used to calculate these frequencies from the raw data. Feel free to consult this if you have questions about how the frequencies are calculated (or if you want to change how the data are aggregated). Use these data to recreate Figure 2 from Hickey et al [1]. See the Methods section of the publication for more details.

user_hate_levels_per_day.csv: CSV file with dates (YYYY-MM-DD format) and the mean proportion of slurs used by hateful users each day from October 1st to November 30th, 2022. Use these data to recreate Figure 1 from Hickey et al [1]. See the Methods section for more details.

hate_keywords.txt: Words used to query the Twitter Academic API for hate tweets.

unfiltered_tweets_containing_hate_words.csv: All tweets with hate words collected with values for Perspective API attributes.


Reference:

1. Hickey, D., Schmitz, M., Fessler, D.M.T, Smaldino, P., Muric, G., & Burghardt, K. Auditing Elon Musk's Impact on Hate Speech and Bots. In Proceedings of the 17th International AAAI Conference on Web and Social Media, (2023).