cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7612539
title: Create regions file test

description: This file is software to test the automized download
DOI: 10.5281/zenodo.7032343
title: Create regions file test

description: This file is software to test the automized download
DOI: 10.6084/m9.figshare.22015238
title: Dictionary Article Data- DB - DF.pdf

description: The graded and corrected text was used for the translation exam and the data analysis for both groups.
DOI: 10.6084/m9.figshare.22015238.v1
title: Dictionary Article Data- DB - DF.pdf

description: The graded and corrected text was used for the translation exam and the data analysis for both groups.
DOI: 10.25446/oxford.21637250
title: Round 6 - Teacher survey

description: The folder contains datasets from a teacher survey of primary school grade 4. The data was collected in Vietnam in 2021.
DOI: 10.25446/oxford.21637250.v1
title: Round 6 - Teacher survey

description: The folder contains datasets from a teacher survey of primary school grade 4. The data was collected in Vietnam in 2021.
DOI: 10.6084/m9.figshare.20161232
title: Filling the gap in distribution ranges and conservation status in Ctenomys (Rodentia: Ctenomyidae)

description: Supplementary Data Supplementary Data SD1.—Interactive map showing Ctenomys geographic distribution areas (DAs), protected areas (PAs), and their intersection. To avoid an excessively large file, we retained PAs within the limits of the distribution of the genus. <br> Supplementary Data SD2.—This is a CSV spreadsheet that is downloadable and contains locality points used in the construction/modification of species distribution polygons. <br> Supplementary Data SD3.—Polygons of all Ctenomys species distributions that can be opened by any ARC-GIS compatible software. <br> Supplementary Data SD4.—Distributional information, previous conservation status assessments, and revised conservation status according to updated distribution areas, number of localities and auxiliary information (overlap with protected areas and habitat transformation), of extant Ctenomys species. <br> Supplementary Data SD5.—Distribution areas, extent of overlap with protected areas (expressed in km2, as a percentage and also the number of PAs), and measures of naturalseminatural and anthropogenic land cover changes in the period for the years 2000–2020 for all extant Ctenomys species. <br> Supplementary Data SD6.—An interactive list of protected areas that overlap Ctenomys geographic distributions. This is available as a web-based table that can be searched. <br> Supplementary Data SD7.—Interactive table showing distribution areas, conservation status, overlap with protected areas, and habitat transformation in the past 20 years for 68 extant Ctenomys species.
DOI: 10.6084/m9.figshare.20161232.v1
title: Filling the gap in distribution ranges and conservation status in Ctenomys (Rodentia: Ctenomyidae)

description: Supplementary Data <em>Supplementary Data SD1.</em> — Interactive map showing <em>Ctenomys</em> geographic distribution areas (DAs), protected areas (PAs), and their intersection. To avoid an excessively large file, we retained PAs within the limits of the distribution of the genus. — <em>Supplementary Data SD2.</em> — This is a CSV spreadsheet that is downloadable and contains locality points used in the construction/modification of species distribution polygons. — <em>Supplementary Data SD3.</em> — Polygons of all <em>Ctenomys</em> species distributions that can be opened by any ARC-GIS compatible software. — <em>Supplementary Data SD4.</em> — An interactive list of protected areas that overlap <em>Ctenomys </em>geographic distributions. This is available as a web based table that can be searched.— <em>Supplementary Data SD5</em>. Interactive table showing distribution areas, conservation status, overlap with protected areas, and habitat transformation in the past 20 years for 68 extant <em>Ctenomys</em> species.
DOI: 10.6084/m9.figshare.20161232.v2
title: Filling the gap in distribution ranges and conservation status in Ctenomys (Rodentia: Ctenomyidae)

description: Supplementary Data Supplementary Data SD1.—Interactive map showing Ctenomys geographic distribution areas (DAs), protected areas (PAs), and their intersection. To avoid an excessively large file, we retained PAs within the limits of the distribution of the genus. <br> Supplementary Data SD2.—This is a CSV spreadsheet that is downloadable and contains locality points used in the construction/modification of species distribution polygons. <br> Supplementary Data SD3.—Polygons of all Ctenomys species distributions that can be opened by any ARC-GIS compatible software. <br> Supplementary Data SD4.—Distributional information, previous conservation status assessments, and revised conservation status according to updated distribution areas, number of localities and auxiliary information (overlap with protected areas and habitat transformation), of extant Ctenomys species. <br> Supplementary Data SD5.—Distribution areas, extent of overlap with protected areas (expressed in km2, as a percentage and also the number of PAs), and measures of naturalseminatural and anthropogenic land cover changes in the period for the years 2000–2020 for all extant Ctenomys species. <br> Supplementary Data SD6.—An interactive list of protected areas that overlap Ctenomys geographic distributions. This is available as a web-based table that can be searched. <br> Supplementary Data SD7.—Interactive table showing distribution areas, conservation status, overlap with protected areas, and habitat transformation in the past 20 years for 68 extant Ctenomys species.
DOI: 10.25446/oxford.21637253
title: Round 6 - Community survey

description: The folder contains datasets from a community survey conducted in Vietnam in 2021.
DOI: 10.25446/oxford.21637253.v1
title: Round 6 - Community survey

description: The folder contains datasets from a community survey conducted in Vietnam in 2021.
DOI: 10.5281/zenodo.7612513
title: Dataset for "Review article: Earth's ice imbalance"

description: ################################## This archive contains source data to reproduce Figure 4 from Slater et al. 2021, "Review article: Earth’s ice imbalance" https://tc.copernicus.org/articles/15/233/2021/ Centre for Polar Observation and Modelling, School of Earth and Environment, University of Leeds<br> Corresponding author: t.slater1@leeds.ac.uk ################################## Time series of annual cumulative ice mass change are provided for each individual component between 1994 and 2017 in the following data files: --------------------------------------------------------------<br> AIS_cumul_1994_2017_annual.csv<br> --------------------------------------------------------------<br> Antarctic Ice Sheet --------------------------------------------------------------<br> Glacier_cumul_1994_2017_annual.csv<br> -------------------------------------------------------------- Mountain glaciers --------------------------------------------------------------<br> GrIS_cumul_1994_2017_annual.csv<br> -------------------------------------------------------------- Greenland Ice Sheet --------------------------------------------------------------<br> iceshelves_calving_cumul_1994_2017_annual.csv<br> -------------------------------------------------------------- Antarctic ice shelf calving --------------------------------------------------------------<br> iceshelves_thinning_cumul_1994_2017_annual.csv<br> -------------------------------------------------------------- Antarctic ice shelf thinning --------------------------------------------------------------<br> NH_seaice_cumul_1994_2017_annual.csv<br> -------------------------------------------------------------- Sea ice in the Northern Hemisphere --------------------------------------------------------------<br> SH_seaice_cumul_1994_2017_annual.csv<br> -------------------------------------------------------------- Sea ice in the Southern Hemisphere -------------------------------------------------------------- Acknowledgements: We thank Axel Schweiger and Jinlun Zhang for their help with PIOMAS/GIOMAS data and Susheel Adusumilli for providing ice shelf thickness change data. We thank the editor, Xavier Fettweis, and two anonymous reviewers for their comments, which greatly improved the manuscript.
DOI: 10.6084/m9.figshare.22016282
title: Distraction, learning and performance in dogs

description: Distractions that lead to performance deficits in working dogs can be life-threatening to the dog, its handler, and others. To reduce the impact of extraneous stimuli, distractors that are likely to occur in the working environment can be incorporated into training. Yet, the impact that distraction training has on learning efficacy remains unclear. Similarly, whether dogs are able to generalise from their trained distractors to novel stimuli that they may face whilst working is unknown. Here, we investigated how training with an acoustic distractor impacts dogs’ capacity to learn and later perform a task in the presence of novel stimuli that vary in similarity to the trained distractor (a similar acoustic and a dissimilar light distractor). We found that dogs trained with a distractor learned the task less efficiently than dogs trained in silence, and did not perform the task more efficiently when subsequently tested in the presence of the novel sound or light distractors. Interestingly, the similar auditory stimulus impaired task performance more than the dissimilar light stimulus in dogs from both training conditions. While dogs trained with the acoustic distractor habituated faster to the novel sound distractor than dogs trained without a distractor, this trend was not significant. Our findings highlight potentially negative effects of distraction training on learning, and provide limited support that it improves task performance in dynamic working environments.
DOI: 10.6084/m9.figshare.21997712.v1
title: Dataset for Influence of environmental factors on seasonal variation in plankton dynamics in nearshore waters of Eastern Guangdong, China

description: Established the impact of various environmental parameters on the richness and distribution of phytoplankton and zooplankton in Eastern Guangdong, China.
DOI: 10.15472/n38eys
title: Flora del Jardín Botánico de Cartagena "Guillermo Piñeres"

description: Esta lista de chequeo contiene 318 nombres de plantas nativas presentes en el bosque del Jardín Botánico de Cartagena "Guillermo Piñeres" (JBGP) y alrededores. Se construyó teniendo en cuenta los datos de los inventarios realizados previamente en el JBGP, registros de herbario y observaciones en campo. Incluye plantas de la clase Magnoliopsida, Liliopsida y Polypodiopsida.
DOI: 10.15787/vtt1/1ictsn
title: A data driven approach to the development and evaluation of acoustic electric vehicle alerting systems for vision impaired pedestrians (05-086)

description: Project Description: This project focused on evaluating candidate warning sounds for electric vehicles in a road and laboratory environment. Road and lab results were compared with the end goal of validating a lab environment which would facilitate future testing. Data Scope: The data consists of a multiple excel sheets that contain time series data from on-road testing. Data Specification: Listener-testing trials Only valid trials Trimmed to 100m and -10m (10m past the seated participants) distance Timeseries data, including: SPL (dB) Sound Data (Octave Bands) Vehicle Distance (m) Horizontal Speed (from DGPS, m/s) Button Status for each participant (pressed, not pressed)
DOI: 10.6073/pasta/b7e9ef5f68af5efba9da60d87a7e6a9e
title: Plant biomass in heath tundra experimental plots, 1996, Arctic LTER, Toolik Lake, Alaska.

description: Plant biomass in arctic heath experimental plots. Plots set up in 1989 with nitrogen, phosphorus, nitrogen plus phosphorus and a shade treatment were harvested for above ground biomass. Root mass was also measured on a smaller subsample.
DOI: 10.25446/oxford.21583056
title: Impact of district education policies on learning: PPG Selectivity

description: This study forms part of the teams Reform Area B workstream that focusses on decentralisation and district innovations through four specially selected innovative districts that will be used as "learning laboratories" to generate findings. In each district, the team studies a particular system innovation and its impact on learning outcomes. Some of these innovations are co-designed with the district government.
DOI: 10.5517/ccdc.csd.cc11dxt5
title: CCDC 965737: Experimental Crystal Structure Determination

description: Related Article: Hui Zhang, Jinhuan Chen, Zhenming Yin|2015|Tianjin Shif.Dax.Xue.Zir.Kex.|10|85|
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.cc2950k2
title: CCDC 2119983: Experimental Crystal Structure Determination

description: Related Article: Letian Zhang, Yong Yang, Pengfei Zhang, Chao Chen, Chao Shen|2022|Eur.J.Org.Chem.|2022|e202200696|doi:10.1002/ejoc.202200696
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: 10.34740/kaggle/dsv/4955731
title: Nifty-50 & Nifty Future 50 Dataset
DOI: 10.5517/ccdc.csd.cc2bk2cc
title: CCDC 2161362: Experimental Crystal Structure Determination

description: Related Article: Can-Ming Zhu, Yonghong Xiao|2022|CSD Communication|||
description: Related Article: Can-Ming Zhu, Rong-Bin Liang, Yonghong Xiao, Wei Zhou, Qing-Xiao Tong, Jian-Ji Zhong|2022|Green Chemistry|25|960|doi:10.1039/D2GC03347J
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.cc2bn68h
title: CCDC 2164366: Experimental Crystal Structure Determination

description: Related Article: Gang Yu, Huanyu Liu, Wenchao Yan, Ruoyao Guo, Aoben Wu, Zifeng Zhao, Zhiwei Liu, Zuqiang Bian|2022|Materials Horizons|10|625|doi:10.1039/D2MH01123A
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.cc2bn6bk
title: CCDC 2164368: Experimental Crystal Structure Determination

description: Related Article: Gang Yu, Huanyu Liu, Wenchao Yan, Ruoyao Guo, Aoben Wu, Zifeng Zhao, Zhiwei Liu, Zuqiang Bian|2022|Materials Horizons|10|625|doi:10.1039/D2MH01123A
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: 10.15784/601661
title: Discrete bulk sediment properties data from Mercer Subglacial Lake

description: This dataset contains bulk sediment properties measurements from cores collected from Mercer Subglacial Lake by the SALSA project. Included are: physical properties (bulk density, mass water content, porosity, shear strength, particle size distribution, and mineralogy); carbon (inorganic and organic); iron (ascorbate- and dithionite-extractable); and sulfur (acid-volatile and chromium-reducible).
DOI: 10.34740/kaggle/dsv/4955755
title: Mahanagar Yatayat Dataset
DOI: 10.15784/601663
title: Water column biogeochemical data from Mercer Subglacial Lake

description: This dataset contains water column biogeochemical properties measured on discrete samples collected from Mercer Subglacial Lake by the SALSA project. Data included are: specific conductance; carbonic acid system parameters (total alkalinity, total inorganic carbon, and pH); water stable isotopes (δ2H and δ18O); dissolved gases (oxygen, methane, nitrous oxide, and hydrogen); dissolved nutrients (ammonium, nitrite and phosphate), major anions (including nitrate) and cations; size-fractionated colloidal and dissolved trace elements); dissolved organic carbon; and microbial cell and virus-like particle counts.
DOI: 10.15784/601664
title: Sediment porewater properties data from Mercer Subglacial Lake

description: This dataset contains measurements of sediment porewater properties from cores collected from Mercer Subglacial Lake by the SALSA project. Included are: specific conductance; water stable isotopes (δ2H and δ18O); dissolved gases (methane and its stable isotopes δ13C and δ2H, ethylene, and ethane); and major anions and cations.
DOI: 10.34740/kaggle/dsv/4955765
title: Egypt Gold Prices | Daily Updated
DOI: 10.5281/zenodo.7611072
title: Dataset related to publication "Non-contact thermometer for improved air temperature measurements"

description: These datasets were used to produce Fig. 6, 8 and 9 in paper "Non-contact thermometer for improved air temperature measurements" to be published in <em>Sensors </em><strong>2023</strong><em>, 23. </em>The data were produced within the EMPIR project 18SIB01 GeoMetre.
description: The EMPIR 18SIB01 GeoMetre project has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme.
DOI: 10.5281/zenodo.7611071
title: Dataset related to publication "Non-contact thermometer for improved air temperature measurements"

description: These datasets were used to produce Fig. 6, 8 and 9 in paper "Non-contact thermometer for improved air temperature measurements" to be published in <em>Sensors </em><strong>2023</strong><em>, 23. </em>The data were produced within the EMPIR project 18SIB01 GeoMetre.
description: The EMPIR 18SIB01 GeoMetre project has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme.
DOI: 10.34943/325e814b-2011-48a8-a421-dbf82d5456ad
title: ODP 1026 Broadband Seismometer Deployed 2021-08-15

description: The Guralp CMG-IT Broadband Seismometer 5223 was deployed on 2021-08-15 at ODP 1026. ODP 1026 is a borehole site used to pinpoint an area within Cascadia. This device is a Broadband Seismometer. Broadband Seismometers measure seismic waves over a broad frequency range depending on the device (e.g. 0.00278 Hz - 250 Hz). A broadband seismometer facilitates measurement of seismic events in the widest frequency band possible. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.5281/zenodo.7612639
title: How do CONTRIBUTING files support newcomers in Open Source projects?

description: Welcome to the replication package for the paper entitled "Do CONTRIBUTING files provide information about OSS newcomers' onboarding barriers?" <strong>Repository Structure </strong><br> Our package is divided in folders and it is organized as follows: <strong>app</strong>: This folder contains the implementation of our streamlit application, used to demonstrate the capabilities of our classification model. <strong>data</strong>: If you are looking for the data we have used for classification, this is the folder where it is located. This folder also contains the raw contributing files of each project analyzed, and the spreadsheets qualitatively analyzed by the authors. <strong>results</strong>: This folder contains all the files related to our results section. From the analysis of our classification model to the results of our online questionnaire. <strong>scripts</strong>: If you want to see the code we wrote during the whole classification process, including but not limited to the classification process, this is the folder you are looking for. <strong>survey</strong>: instrument used in the survey, raw answers and a spreadsheet with analyzed data. <strong>misc</strong>: This folder contains miscellaneous files that were not used in this paper, but support the statements of it (e.g. a screenshot of the top ten languages used on GitHub from the Octoverse website). <em>Each folder has its specific README.md with more specific details about their content.</em> <strong>I want to use your model!</strong><br> We are glad you are interested in our classification model. The final model is available as a `classification_model.sav` file inside the `app` folder that you can load using Pickle. If you are not familiar with Pickle or don't know how to load a model, we recommend you to take a look at our code implementation inside the `app` folder (`classify_content.py` is a good starting point).<br>
DOI: 10.5281/zenodo.7042353
title: How do CONTRIBUTING files support newcomers in Open Source projects?

description: Welcome to the replication package for the paper entitled "Do CONTRIBUTING files provide information about OSS newcomers' onboarding barriers?" <strong>Repository Structure </strong><br> Our package is divided in folders and it is organized as follows: <strong>app</strong>: This folder contains the implementation of our streamlit application, used to demonstrate the capabilities of our classification model. <strong>data</strong>: If you are looking for the data we have used for classification, this is the folder where it is located. This folder also contains the raw contributing files of each project analyzed, and the spreadsheets qualitatively analyzed by the authors. <strong>results</strong>: This folder contains all the files related to our results section. From the analysis of our classification model to the results of our online questionnaire. <strong>scripts</strong>: If you want to see the code we wrote during the whole classification process, including but not limited to the classification process, this is the folder you are looking for. <strong>survey</strong>: instrument used in the survey, raw answers and a spreadsheet with analyzed data. <strong>misc</strong>: This folder contains miscellaneous files that were not used in this paper, but support the statements of it (e.g. a screenshot of the top ten languages used on GitHub from the Octoverse website). <em>Each folder has its specific README.md with more specific details about their content.</em> <strong>I want to use your model!</strong><br> We are glad you are interested in our classification model. The final model is available as a `classification_model.sav` file inside the `app` folder that you can load using Pickle. If you are not familiar with Pickle or don't know how to load a model, we recommend you to take a look at our code implementation inside the `app` folder (`classify_content.py` is a good starting point).<br>