cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2cl7x4
title: CCDC 2192286: Experimental Crystal Structure Determination

description: Related Article: Caiyou Chen, Gregory C. Fu|2023|Nature (London)|618|301|doi:10.1038/s41586-023-06001-y
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.cc2dgjp3
title: CCDC 2218505: Experimental Crystal Structure Determination

description: Related Article: Tai-Rong Zhang, Peng-Gang Hu, Wen-Hao Yu, Yingbo Shi, Shi-Kai Xiang, Ping Hu, Ke-Qing Zhao, Biqin Wang, Yong-Yuan Gui, Chun Feng|2023|Cryst.Growth Des.|23|4424|doi:10.1021/acs.cgd.3c00186
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.cc2dys9g
title: CCDC 2233156: Experimental Crystal Structure Determination

description: Related Article: Xiao-Hui Chen, Hai-Lei Cui|2023|J.Org.Chem.|88|7347|doi:10.1021/acs.joc.3c00566
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.cc2f4750
title: CCDC 2238391: Experimental Crystal Structure Determination

description: Related Article: Emmanuele Parisi, Fabio Borbone, Antonio Carella, Stefano Lettieri, Amedeo Capobianco, Andrea Peluso, Roberto Centore|2023|Cryst.Growth Des.|23|4538|doi:10.1021/acs.cgd.3c00295
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.cc2f4761
title: CCDC 2238392: Experimental Crystal Structure Determination

description: Related Article: Emmanuele Parisi, Fabio Borbone, Antonio Carella, Stefano Lettieri, Amedeo Capobianco, Andrea Peluso, Roberto Centore|2023|Cryst.Growth Des.|23|4538|doi:10.1021/acs.cgd.3c00295
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.cc2f4772
title: CCDC 2238393: Experimental Crystal Structure Determination

description: Related Article: Emmanuele Parisi, Fabio Borbone, Antonio Carella, Stefano Lettieri, Amedeo Capobianco, Andrea Peluso, Roberto Centore|2023|Cryst.Growth Des.|23|4538|doi:10.1021/acs.cgd.3c00295
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.cc2f4783
title: CCDC 2238394: Experimental Crystal Structure Determination

description: Related Article: Emmanuele Parisi, Fabio Borbone, Antonio Carella, Stefano Lettieri, Amedeo Capobianco, Andrea Peluso, Roberto Centore|2023|Cryst.Growth Des.|23|4538|doi:10.1021/acs.cgd.3c00295
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.cc2f4794
title: CCDC 2238395: Experimental Crystal Structure Determination

description: Related Article: Emmanuele Parisi, Fabio Borbone, Antonio Carella, Stefano Lettieri, Amedeo Capobianco, Andrea Peluso, Roberto Centore|2023|Cryst.Growth Des.|23|4538|doi:10.1021/acs.cgd.3c00295
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.cc2f47b5
title: CCDC 2238396: Experimental Crystal Structure Determination

description: Related Article: Emmanuele Parisi, Fabio Borbone, Antonio Carella, Stefano Lettieri, Amedeo Capobianco, Andrea Peluso, Roberto Centore|2023|Cryst.Growth Des.|23|4538|doi:10.1021/acs.cgd.3c00295
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.cc2f47c6
title: CCDC 2238397: Experimental Crystal Structure Determination

description: Related Article: Emmanuele Parisi, Fabio Borbone, Antonio Carella, Stefano Lettieri, Amedeo Capobianco, Andrea Peluso, Roberto Centore|2023|Cryst.Growth Des.|23|4538|doi:10.1021/acs.cgd.3c00295
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.cc2f47d7
title: CCDC 2238398: Experimental Crystal Structure Determination

description: Related Article: Emmanuele Parisi, Fabio Borbone, Antonio Carella, Stefano Lettieri, Amedeo Capobianco, Andrea Peluso, Roberto Centore|2023|Cryst.Growth Des.|23|4538|doi:10.1021/acs.cgd.3c00295
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.cc2f47f8
title: CCDC 2238399: Experimental Crystal Structure Determination

description: Related Article: Emmanuele Parisi, Fabio Borbone, Antonio Carella, Stefano Lettieri, Amedeo Capobianco, Andrea Peluso, Roberto Centore|2023|Cryst.Growth Des.|23|4538|doi:10.1021/acs.cgd.3c00295
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.cc2f47g9
title: CCDC 2238400: Experimental Crystal Structure Determination

description: Related Article: Emmanuele Parisi, Fabio Borbone, Antonio Carella, Stefano Lettieri, Amedeo Capobianco, Andrea Peluso, Roberto Centore|2023|Cryst.Growth Des.|23|4538|doi:10.1021/acs.cgd.3c00295
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.cc2f47hb
title: CCDC 2238401: Experimental Crystal Structure Determination

description: Related Article: Emmanuele Parisi, Fabio Borbone, Antonio Carella, Stefano Lettieri, Amedeo Capobianco, Andrea Peluso, Roberto Centore|2023|Cryst.Growth Des.|23|4538|doi:10.1021/acs.cgd.3c00295
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: 10.6084/m9.figshare.23498748
title: <strong>Dataset of DNA microarray probe for every gene in the genome of </strong><em><strong>Escherichia coli</strong></em><strong> K-12</strong>

description: Prior to the maturation of RNA-seq technology, DNA microarray is the go-to tool for understanding the genome-level gene expression pattern of particular organism. However, the probes used to fish out the mRNA are often designed by commercial companies, which although useful, could not tailor to the specific research needs of individual researchers in different fields. This work presents a dataset of DNA microarray probe for every gene in the genome of <em>Escherichia coli</em> K-12 designed by an in-house MATLAB software through automated algorithm. Each probe targets the front part of every gene, and the specificity check (TRUE / FALSE) reveals that not every probe is specific for each gene. This means that the probe could also fish out mRNA from other genes in the same genome. A count is provided for the number of genes that could be targeted for a given probe. Naturally, a higher count meant that the probe could target more genes, and is thus, not specific. Overall, this work represents an initial attempt to design a software for automatic design of probes for each gene in the genome of <em>E. coli</em> K-12. The result is a satisfactory list of probes useful for the tasks, but there are a number of genes that requires more refined strategies for developing more specific probes.
DOI: 10.6084/m9.figshare.23498748.v1
title: <strong>Dataset of DNA microarray probe for every gene in the genome of </strong><em><strong>Escherichia coli</strong></em><strong> K-12</strong>

description: Prior to the maturation of RNA-seq technology, DNA microarray is the go-to tool for understanding the genome-level gene expression pattern of particular organism. However, the probes used to fish out the mRNA are often designed by commercial companies, which although useful, could not tailor to the specific research needs of individual researchers in different fields. This work presents a dataset of DNA microarray probe for every gene in the genome of <em>Escherichia coli</em> K-12 designed by an in-house MATLAB software through automated algorithm. Each probe targets the front part of every gene, and the specificity check (TRUE / FALSE) reveals that not every probe is specific for each gene. This means that the probe could also fish out mRNA from other genes in the same genome. A count is provided for the number of genes that could be targeted for a given probe. Naturally, a higher count meant that the probe could target more genes, and is thus, not specific. Overall, this work represents an initial attempt to design a software for automatic design of probes for each gene in the genome of <em>E. coli</em> K-12. The result is a satisfactory list of probes useful for the tasks, but there are a number of genes that requires more refined strategies for developing more specific probes.
DOI: 10.5281/zenodo.7964426
title: Raw metadata

description: Raw metadata for "<strong>Long-term elevated precipitation induces grassland soil carbon loss via microbe-plant-soil interplay" </strong>published in <em>Global Change Biology</em>.
DOI: 10.5281/zenodo.7964425
title: Raw metadata

description: Raw metadata for "<strong>Long-term elevated precipitation induces grassland soil carbon loss via microbe-plant-soil interplay" </strong>published in <em>Global Change Biology</em>.
DOI: 10.5281/zenodo.8025026
title: KarmaLoop_Raw_Data

description: The training protein data required by KarmaLoop, the data should be processed according to the guideline proposed by KarmaLoop, and the pdb should be downloaded from Protein Data Bank, the command is ./batch_download.sh -f newpdb.txt -p
DOI: 10.5281/zenodo.8025025
title: KarmaLoop_Raw_Data

description: The training protein data required by KarmaLoop, the data should be processed according to the guideline proposed by KarmaLoop, and the pdb should be downloaded from Protein Data Bank, the command is ./batch_download.sh -f newpdb.txt -p
DOI: 10.5281/zenodo.8020688
title: Natural Language Inference Dataset for Software Engineering

description: This repository introduces a specialized NLI dataset designed to optimize the performance of the language models targeted for tackling NLP tasks related to the software engineering domain. We consider different texts from the software engineering domain and manually curated the entailment relationships among different sentences. The source of the sentence pairs includes PROMISE dataset, PURE dataset, user guides of different softwares, articles on operating systems (e.g. official documentation of Windows, Mac), databases (e.g. official documentation of MongoDB, Oracle), cyber security (MITRE documentation) and software product descriptions including AWS documentation. Our created text entailment dataset contains 10k sentence pairs manually labeled for balanced classification with the labels Entailment, Contradiction, and Neutral.
DOI: 10.5281/zenodo.8020689
title: Natural Language Inference Dataset for Software Engineering

description: This repository introduces a specialized NLI dataset designed to optimize the performance of the language models targeted for tackling NLP tasks related to the software engineering domain. We consider different texts from the software engineering domain and manually curated the entailment relationships among different sentences. The source of the sentence pairs includes PROMISE dataset, PURE dataset, user guides of different softwares, articles on operating systems (e.g. official documentation of Windows, Mac), databases (e.g. official documentation of MongoDB, Oracle), cyber security (MITRE documentation) and software product descriptions including AWS documentation. Our created text entailment dataset contains 10k sentence pairs manually labeled for balanced classification with the labels Entailment, Contradiction, and Neutral.
DOI: 10.5281/zenodo.8025035
title: Natural Language Inference Dataset for Software Engineering

description: This repository introduces a specialized NLI dataset designed to optimize the performance of the language models targeted for tackling NLP tasks related to the software engineering domain. We consider different texts from the software engineering domain and manually curated the entailment relationships among different sentences. The source of the sentence pairs includes PROMISE dataset, PURE dataset, user guides of different softwares, articles on operating systems (e.g. official documentation of Windows, Mac), databases (e.g. official documentation of MongoDB, Oracle), cyber security (MITRE documentation) and software product descriptions including AWS documentation. Our created text entailment dataset contains 10k sentence pairs manually labeled for balanced classification with the labels Entailment, Contradiction, and Neutral.
DOI: 10.5281/zenodo.8025041
title: Natural Language Inference Dataset for Software Engineering

description: This repository introduces a specialized NLI dataset designed to optimize the performance of the language models targeted for tackling NLP tasks related to the software engineering domain. We consider different texts from the software engineering domain and manually curated the entailment relationships among different sentences. The source of the sentence pairs includes PROMISE dataset, PURE dataset, user guides of different softwares, articles on operating systems (e.g. official documentation of Windows, Mac), databases (e.g. official documentation of MongoDB, Oracle), cyber security (MITRE documentation) and software product descriptions including AWS documentation. Our created text entailment dataset contains 10k sentence pairs manually labeled for balanced classification with the labels Entailment, Contradiction, and Neutral.
DOI: 10.3886/e104642v4-139401
title: 2016 Annual Survey of Refugees

description: Since the 1980s, the Office of Refugee Resettlement[1] (ORR) has conducted the Annual Survey of Refugees (ASR), which collects information on refugees during their first five years after arrival in the U.S. The ASR is the only scientifically-collected source of national data on refugees’ progress toward self-sufficiency and integration. ORR uses the ASR results alongside other information sources to fulfill its Congressionally-mandated reporting following the Refugee Act of 1980. Historically, the microdata from these surveys have generally been unavailable to researchers. <br> <br>In the Spring of 2017 ORR completed its 50th Annual Survey of Refugees (ASR). The data from the ASR offer a window into respondents’ first five years in the United States and shows the progress that refugee families made towards learning English, participating in the workforce, and establishing permanent residence. This public use data deposit is only for the 2016 ASR with future years likely to be added to the ICPSR archive.. <br> [1] The Office of Refugee Resettlement (ORR) at the Administration for Children and Families in the U.S. Department of Health and Human Services (HHS) serves refugees and other humanitarian entrants, including asylees, Cuban and Haitian entrants, Special Immigrant Visa holders, Amerasians, victims of human trafficking, and unaccompanied children. By providing these arrived populations with critical resources, ORR promotes their economic and social well-being. The Annual Survey of Refugees focuses solely on those refugees who have come to the U.S. in the past five fiscal years.
description: Household- and person-level analytic weights were developed for the 2016 ASR to allow for valid statistical estimates of the target refugee population. Both sets of weights are comprised of two components – a base weight reflecting the selection probability and an adjustment that corrects for differential nonresponse and aligns the population to known totals from the sampling frame (RADS universe file).
description: Response Rates: An overall response rate of 24 percent was achieved. The response rate was driven by the ability to locate and speak to (1500+468)/6176 = 32 percent of the sample, <b>meaning that two thirds of the sample could either not be located, or (if located) could </b> <b>not be successfully contacted. </b> <b> </b> The overall response rates decreased with time since arrival to the U.S., varying from 20 percent for FY 2011-2012 refugees to 25 percent for FY 2013-2014 refugees and a high of 31 percent for FY 2015 refugees.
description: The 2016 ASR employed a stratified probability sample design of refugees. The first stage of selection was the household (PA), and the second stage was the selection of persons within households.
description: Refugees aged 16 years old or over at the time of interview who arrived in the U.S. during FY 2011-2015 . Smallest Geographic Unit: Census region originally resettled in and the country of birth
description: telephone interview;
DOI: 10.34740/kaggle/dsv/5901318
title: Nigerian Presidential Election 2023 Tweets
DOI: 10.5281/zenodo.8025081
title: Housework Reallocation Between Genders and Generations during China's COVID-19 Lockdowns: Patterns & Reasons

description: Coding summary of interview participants
DOI: 10.5281/zenodo.8025080
title: Housework Reallocation Between Genders and Generations during China's COVID-19 Lockdowns: Patterns & Reasons

description: Coding summary of interview participants
DOI: 10.34740/kaggle/dsv/5901263
title: Hacker News OpenAI Embeddings
DOI: 10.6084/m9.figshare.23498769
title: Supplementary Materials

description: The tables S1 to S4 in the supplementary material show the results of the sensitivity analysis on the net work of the Sol and gastrocnemii at the ankle joint as well as the net work of the gastrocnemii at the knee joint, which represents the contribution of the biarticular mechanisms to the increase of net ankle work. Different force potentials and ratios of the GM EMG-activity affect the absolute values in all three parameters. Nevertheless, in all combinations, the contribution of the Sol to the net mechanical work at the ankle joint was higher than the gastrocnemii. Both the energy production of the monoarticular Sol and the contribution of the biarticular mechanisms (i.e., in increased net ankle work) increased with walking speed. Thus, the sensitivity analysis clearly demonstrates that the main findings and conclusions generally hold true even under the assumption of varying force-length-velocity potentials and activation of the individual muscles. <br> The video clip illustrates a graphical animation of the right leg during the maximal walking speed of an individual. The vastus lateralis and the gastrocnemii muscles are colored in red, and the soleus in the blue spectrum. Lighter red and blue colors show lower muscle activity, and darker red and blue colors show higher muscle activity. From 47% to 72% of the stance phase, the knee joint extends, and the ankle joint is plantarflexing. This simultaneous knee extension and ankle plantar flexion initiate the energy transfer from the knee extensor to the ankle plantar flexor muscles.
DOI: 10.6084/m9.figshare.23498769.v1
title: Supplementary Materials

description: The tables S1 to S4 in the supplementary material show the results of the sensitivity analysis on the net work of the Sol and gastrocnemii at the ankle joint as well as the net work of the gastrocnemii at the knee joint, which represents the contribution of the biarticular mechanisms to the increase of net ankle work. Different force potentials and ratios of the GM EMG-activity affect the absolute values in all three parameters. Nevertheless, in all combinations, the contribution of the Sol to the net mechanical work at the ankle joint was higher than the gastrocnemii. Both the energy production of the monoarticular Sol and the contribution of the biarticular mechanisms (i.e., in increased net ankle work) increased with walking speed. Thus, the sensitivity analysis clearly demonstrates that the main findings and conclusions generally hold true even under the assumption of varying force-length-velocity potentials and activation of the individual muscles. <br> The video clip illustrates a graphical animation of the right leg during the maximal walking speed of an individual. The vastus lateralis and the gastrocnemii muscles are colored in red, and the soleus in the blue spectrum. Lighter red and blue colors show lower muscle activity, and darker red and blue colors show higher muscle activity. From 47% to 72% of the stance phase, the knee joint extends, and the ankle joint is plantarflexing. This simultaneous knee extension and ankle plantar flexion initiate the energy transfer from the knee extensor to the ankle plantar flexor muscles.