cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2cmtd7
title: CCDC 2193789: Experimental Crystal Structure Determination

description: Related Article: Haozhong Liang, Yuting Yang, Li Shao, Weijie Zhu, Xiaofeng Liu, Bin Hua, Feihe Huang|2023|J.Am.Chem.Soc.|145|2870|doi:10.1021/jacs.2c10674
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.cc2cmtf8
title: CCDC 2193790: Experimental Crystal Structure Determination

description: Related Article: Haozhong Liang, Yuting Yang, Li Shao, Weijie Zhu, Xiaofeng Liu, Bin Hua, Feihe Huang|2023|J.Am.Chem.Soc.|145|2870|doi:10.1021/jacs.2c10674
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.cc2d7h16
title: CCDC 2211727: Experimental Crystal Structure Determination

description: Related Article: Xiangmei Lu, Yue Tang, Guoping Yang, Yao-Yu Wang|2023|J.Mater.Chem.C|11|2328|doi:10.1039/D2TC04978C
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.cc2d7h05
title: CCDC 2211726: Experimental Crystal Structure Determination

description: Related Article: Xiangmei Lu, Yue Tang, Guoping Yang, Yao-Yu Wang|2023|J.Mater.Chem.C|11|2328|doi:10.1039/D2TC04978C
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.cc2d8562
title: CCDC 2212383: Experimental Crystal Structure Determination

description: Related Article: Xi Chen|2022|CSD Communication|||
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.cc2dc8mp
title: CCDC 2215372: Experimental Crystal Structure Determination

description: Related Article: Jingwen Su, Wenbin Guo, Yi Liu, Lichun Kong, Hanliang Zheng, Gangguo Zhu|2023|Chem.Commun.|59|1821|doi:10.1039/D2CC06068J
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.cc2dcn8q
title: CCDC 2215733: Experimental Crystal Structure Determination

description: Related Article: Jingwen Su, Wenbin Guo, Yi Liu, Lichun Kong, Hanliang Zheng, Gangguo Zhu|2023|Chem.Commun.|59|1821|doi:10.1039/D2CC06068J
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.cc2d19rj
title: CCDC 2205797: Experimental Crystal Structure Determination

description: Related Article: Ikumi Aratani, Yoji Horii, Daisuke Takajo, Yoshinori Kotani, Hitoshi Osawa, Takashi Kajiwara|2023|J.Mater.Chem.C|11|2082|doi:10.1039/D2TC04963E
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: 10.15783/c7fw2d
title: CRAWDAD dataset rutgers/ap_density (v. 2007-08-09)

description: This dataset includes realistic client arrival patterns and realistic application 
workloads that are used to evaluate effects of high-density deployments of 
interfering access points to IEEE 802.11 WLAN performance. Both wired and 
wireless frame dump traces are provided from the experiments in ORBIT wireless 
research testbed involving four access points and seventy-five stations.
description: Changelog: the initial version
DOI: 10.15783/c7kk5p
title: CRAWDAD dataset rutgers/capture (v. 2007-04-20)

description: In an experiment involving two senders and one receiver, 
we placed a sniffer (wireless NIC in monitor mode) close to each of 
the senders so as to capture all transmitted MAC frames from each sender.
description: Changelog: the initial version
DOI: 10.5281/zenodo.6527764
title: Analysis of the P. lividus sea urchin genome highlights contrasting trends of genomic and regulatory evolution in deuterostomes

description: Supplementary datasets accompanying paper stage_peaks_anc_sel.xlsx : ATAC peaks with classification, conservation and binding sites<br> Pliv.mfuzz.enrichGO.txt : GO enrichment in MFuzz cluster<br> Pliv_genes_master_filt.xlsx : Gene models with corresponding information<br> bindetect_results_anf.txt : results of TOBIAS<br> hits_pprx_cl0_ord3vrr+Et_red.fa : alignment of homeobox sequences<br> Pliv_aH2p.gn.gtf.gz : annotation in GTF format
DOI: 10.15783/c7v30f
title: CRAWDAD dataset rice/midas (v. 2010-09-01)

description: Accelerometer and compass readings along with network usage and application 
data usage information from 11 smartphone users, each for one week in the field.
description: Changelog: the initial version
DOI: 10.15783/c7zw22
title: CRAWDAD dataset rice/context (v. 2007-08-01)

description: We gathered field data about cellular and Wi-Fi networks through participants 
from the Rice community in Houston, Texas, a major US urban area.
description: Changelog: the initial version
DOI: 10.5281/zenodo.7459274
title: Analysis of the P. lividus sea urchin genome highlights contrasting trends of genomic and regulatory evolution in deuterostomes

description: <br> Supplementary datasets accompanying paper: stage_peaks_anc_sel.xlsx : ATAC peaks with classification, conservation and binding sites<br> Pliv.mfuzz.enrichGO.txt : GO enrichment in MFuzz cluster<br> Pliv_genes_master_filt.xlsx : Gene models with corresponding information<br> bindetect_results_anf.txt : results of TOBIAS<br> hits_pprx_cl0_ord3vrr+Et_red.fa : alignment of homeobox sequences<br> Pliv_aH2p.gn.gtf.gz : annotation in GTF format<br> Pliv_PqN3S_sm.fa.gz : genome of P. livius <br> ansr_*_network.tsv.gz : Stage specific networks from ANANSE analysis<br> ATAC_pks_normcov.tsv : Coverage of unified peaks for ATAC-seq<br> Cttg_pks_normcov.tsv : Coverage of unified peaks for Cut-and-tag H3K27Ac data<br> lncRNA_stgSpe_fpkm.tsv : Expression levels (FPKM) for predicted lncRNAs for available RNA-seq samples <br> Split_Urchin_FPKMs.clean.txt.gz : Expression levels for unified ATAc-seq peaks following direct and reverse orientation
DOI: 10.5281/zenodo.6527763
title: Analysis of the P. lividus sea urchin genome highlights contrasting trends of genomic and regulatory evolution in deuterostomes

description: <br> Supplementary datasets accompanying paper: stage_peaks_anc_sel.xlsx : ATAC peaks with classification, conservation and binding sites<br> Pliv.mfuzz.enrichGO.txt : GO enrichment in MFuzz cluster<br> Pliv_genes_master_filt.xlsx : Gene models with corresponding information<br> bindetect_results_anf.txt : results of TOBIAS<br> hits_pprx_cl0_ord3vrr+Et_red.fa : alignment of homeobox sequences<br> Pliv_aH2p.gn.gtf.gz : annotation in GTF format<br> Pliv_PqN3S_sm.fa.gz : genome of P. livius <br> ansr_*_network.tsv.gz : Stage specific networks from ANANSE analysis<br> ATAC_pks_normcov.tsv : Coverage of unified peaks for ATAC-seq<br> Cttg_pks_normcov.tsv : Coverage of unified peaks for Cut-and-tag H3K27Ac data<br> lncRNA_stgSpe_fpkm.tsv : Expression levels (FPKM) for predicted lncRNAs for available RNA-seq samples <br> Split_Urchin_FPKMs.clean.txt.gz : Expression levels for unified ATAc-seq peaks following direct and reverse orientation
DOI: 10.15783/c77c78
title: CRAWDAD dataset princeton/zebranet (v. 2007-02-14)

description: The data contained in this data set are movement traces collected from
two real-world ZebraNet deployments at Sweetwaters Game Reserve near
Nanyuki, Kenya. The first deployment was in January 2004 and the second
deployment was during summer of 2005.  The data offer detailed animal
position information using UTM format.
description: Changelog: the initial version
DOI: 10.5281/zenodo.7613558
title: SuperDARN data in netCDF format (2021-Jul)

description: 2021-Jul SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7613250
title: SuperDARN data in netCDF format (2021-Oct)

description: 2021-Oct SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7612932
title: SuperDARN data in netCDF format (2021-Dec)

description: 2021-Dec SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7615438
title: SuperDARN data in netCDF format (2021-May)

description: 2021-May SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.15783/c7c303
title: CRAWDAD dataset pdx/vwave (v. 2007-08-13)

description: We collected six wireless LAN traffic traces around Portland, Oregon using 
a commercial sniffer VWave which has a nano-second time resolution.
description: Changelog: the initial version
DOI: 10.5281/zenodo.7617772
title: SuperDARN data in netCDF format (2021-Apr)

description: 2021-Apr SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7613493
title: SuperDARN data in netCDF format (2021-Aug)

description: 2021-Aug SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7613126
title: SuperDARN data in netCDF format (2021-Nov)

description: 2021-Nov SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7613365
title: SuperDARN data in netCDF format (2021-Sep)

description: 2021-Sep SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7613616
title: SuperDARN data in netCDF format (2021-Jun)

description: 2021-Jun SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.15783/c7rc7x
title: CRAWDAD dataset pdx/metrofi (v. 2011-10-24)

description: This is a dataset of location, signal strength, and performance data of
MetroFi, a 802.11x municipal wireless mesh network in Portland, Oregon in 2007. 
The data was collected by Caleb Phillips and Russell Senior to determine the 
coverage and performance of the network.
description: Changelog: the initial version
DOI: 10.5281/zenodo.7527397
title: Replication package for: Beyond Dividing the Pie: Multi-Issue Bargaining in the Laboratory

description: Replication package for the article Bochet, Olivier, Manshu Khanna, Simon Siegenthaler (2023), Beyond Dividing the Pie: Multi-Issue Bargaining in the Laboratory. The Review of Economic Studies. The replication package contains the data and software code to replicate the statistical analysis, tables, and figures published in the article and the online appendix.
DOI: 10.15783/c7w30r
title: CRAWDAD dataset nyupoly/video (v. 2014-05-09)

description: This dataset describes measurements from Dynamic Adaptive Streaming over HTTP (DASH) and WebRTC video services, collected over the GENI WiMAX networks at NYU-Poly and UMass Amherst. These measurements are meant to elucidate the experience of an individual user of these services who is moving at walking speeds through the coverage area of a typical cellular network.
description: Changelog: the initial version