cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14036021
title: R Script – Filtering of 16S rRNA gene sequence counts and taxonomy of common contaminants from CROMO, California, USA CSW1.1 well depth profile May 2016

description: Script used for removing common contaminant taxa and counts from the original 16S rRNA gene sequence counts and taxonomy information from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016. The script allows for filtering of common contaminants identified in Census of Deep Life samples as described previously by Sheik et al., 2018. Sequences identified as eukaryotes, archaea, mitochondria, chloroplasts, singletons, and unknown by SILVA were removed. After this, the script filters the dataset to remove taxa identified by Census of Deep Life to be common contaminants. Data was removed to better identify the relevant organisms present at each depth in the profile.
DOI: 10.5517/ccdc.csd.cc25nwqg
title: CCDC 2016076: Experimental Crystal Structure Determination

description: Related Article: Heng-Shi Li, Sheng-Mei Zhang, Ping Ye, Tao Sun, Kai Wang, Xiu-Qing Zhang, Yan Li|2021|J.Coord.Chem.|74|927|doi:10.1080/00958972.2020.1861602
DOI: 10.6084/m9.figshare.14036021.v1
title: R Script – Filtering of 16S rRNA gene sequence counts and taxonomy of common contaminants from CROMO, California, USA CSW1.1 well depth profile May 2016

description: Script used for removing common contaminant taxa and counts from the original 16S rRNA gene sequence counts and taxonomy information from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016. The script allows for filtering of common contaminants identified in Census of Deep Life samples as described previously by Sheik et al., 2018. Sequences identified as eukaryotes, archaea, mitochondria, chloroplasts, singletons, and unknown by SILVA were removed. After this, the script filters the dataset to remove taxa identified by Census of Deep Life to be common contaminants. Data was removed to better identify the relevant organisms present at each depth in the profile.
DOI: 10.6084/m9.figshare.14036033
title: R Script – Diversity Metrics for 16S rRNA gene sequence counts and taxonomy from CROMO, California, USA CSW1.1 well depth profile May 2016

description: Script used for calculating diversity metrics on the final 16S rRNA gene sequence counts and taxonomy information from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016. Data was collected to identify the diversity of organisms present at each depth in the profile.
DOI: 10.6084/m9.figshare.14036033.v1
title: R Script – Diversity Metrics for 16S rRNA gene sequence counts and taxonomy from CROMO, California, USA CSW1.1 well depth profile May 2016

description: Script used for calculating diversity metrics on the final 16S rRNA gene sequence counts and taxonomy information from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016. Data was collected to identify the diversity of organisms present at each depth in the profile.
DOI: 10.6084/m9.figshare.14035994
title: Table S2 - 16S rRNA gene abundance Top 11 OTUs in the CSW1.1 depth profile from CROMO, California, USA May 2016

description: Data in this sheet are the top 11 most abundant operational taxonomic units (OTUs) identified from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016. See Data Sheet S3, S4 for more data. This table was created to showcase the dominant organisms in CSW1.1.
DOI: 10.6084/m9.figshare.14035994.v1
title: Table S2 - 16S rRNA gene abundance Top 11 OTUs in the CSW1.1 depth profile from CROMO, California, USA May 2016

description: Data in this sheet are the top 11 most abundant operational taxonomic units (OTUs) identified from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016. See Data Sheet S3, S4 for more data. This table was created to showcase the dominant organisms in CSW1.1.
DOI: 10.6084/m9.figshare.14035991
title: Table S1 – Gibbs Energy List of Reactions for CSW1.1 Depth Profile Study from CROMO, California, USA May 2016

description: Data listed are the predetermined reactions used for thermodynamic Gibbs energy calculations performed from a depth profile of the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA in May 2016 (see Data Sheet 1). The Gibbs energy change associated with reactions involving H2, CH4, CO, and acetate as electron donors and O2, NO3-, and SO42- as electron acceptors used measured fluid chemistries in Table 1. This table was constructed as a reference to the Gibbs energy figures created in this study.
DOI: 10.6084/m9.figshare.14035991.v1
title: Table S1 – Gibbs Energy List of Reactions for CSW1.1 Depth Profile Study from CROMO, California, USA May 2016

description: Data listed are the predetermined reactions used for thermodynamic Gibbs energy calculations performed from a depth profile of the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA in May 2016 (see Data Sheet 1). The Gibbs energy change associated with reactions involving H2, CH4, CO, and acetate as electron donors and O2, NO3-, and SO42- as electron acceptors used measured fluid chemistries in Table 1. This table was constructed as a reference to the Gibbs energy figures created in this study.
DOI: 10.6084/m9.figshare.14035988
title: Table 2 - Key genes in metagenomic data and their abundances (FPKM) from CSW1.1 depth profile, CROMO, California, USA May 2016

description: Data in this sheet are normalized metagenomic data presented as Fragments Per Kilobase of Target Per Million Mapped Reads (FPKM) from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016. Metagenomic data was sequenced, library prep was performed, and quality control was done at the University of Utah before assembly using Megahit and prediction using Prodigal. Mapping of sequence reads was done using Bowtie2 and FPKM was calculated using the seq-annot package. This table shows key genes, and their description, and associated KEGG ID as well as their FPKM value at each depth in the CSW1.1 well. This table was assembled to list the key genes relevant to the depth profile.
DOI: 10.6084/m9.figshare.14035988.v1
title: Table 2 - Key genes in metagenomic data and their abundances (FPKM) from CSW1.1 depth profile, CROMO, California, USA May 2016

description: Data in this sheet are normalized metagenomic data presented as Fragments Per Kilobase of Target Per Million Mapped Reads (FPKM) from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016. Metagenomic data was sequenced, library prep was performed, and quality control was done at the University of Utah before assembly using Megahit and prediction using Prodigal. Mapping of sequence reads was done using Bowtie2 and FPKM was calculated using the seq-annot package. This table shows key genes, and their description, and associated KEGG ID as well as their FPKM value at each depth in the CSW1.1 well. This table was assembled to list the key genes relevant to the depth profile.
DOI: 10.6084/m9.figshare.14035982
title: Data Sheet S6 - Accessory genes from Metagenome Assembled Genomes in CSW1.1 depth profile study from CROMO, California, USA, and reference organisms

description: Data in this sheet are from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016. Genes present in MAGs listed in Data Sheet S5 that were important to note but not directly relevant to the CROMO depth profile study are listed here as Fragments Per Kilobase of Target Per Million Mapped Reads (FPKM). Data was collected to identify the genes present at each depth in the profile, and present some of genetic data less relevant to the main story but relevant to the larger CROMO field site and future studies.
DOI: 10.6084/m9.figshare.14035982.v1
title: Data Sheet S6 - Accessory genes from Metagenome Assembled Genomes in CSW1.1 depth profile study from CROMO, California, USA, and reference organisms

description: Data in this sheet are from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016. Genes present in MAGs listed in Data Sheet S5 that were important to note but not directly relevant to the CROMO depth profile study are listed here as Fragments Per Kilobase of Target Per Million Mapped Reads (FPKM). Data was collected to identify the genes present at each depth in the profile, and present some of genetic data less relevant to the main story but relevant to the larger CROMO field site and future studies.
DOI: 10.6084/m9.figshare.14035979
title: Data Sheet S5 - Metagenome Assembled Genome Bins for CSW1.1 depth profile samples from CROMO, California, USA May 2016

description: Metagenome Assembled Genomes (MAGs) were created from metagenomic data collected from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA during a one-day sampling event in May 2016. Bins here were created using BinSanity and CheckM for quality. Data was collected to gain a better sense of which genes belonged to which organisms present in the profile.
DOI: 10.6084/m9.figshare.14035979.v1
title: Data Sheet S5 - Metagenome Assembled Genome Bins for CSW1.1 depth profile samples from CROMO, California, USA May 2016

description: Metagenome Assembled Genomes (MAGs) were created from metagenomic data collected from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA during a one-day sampling event in May 2016. Bins here were created using BinSanity and CheckM for quality. Data was collected to gain a better sense of which genes belonged to which organisms present in the profile.
DOI: 10.6084/m9.figshare.14035976
title: Data Sheet S4 - Fragments Per Kilobase of Target Per Million Mapped Reads (FPKM) of each CSW1.1 depth profile metagenomic sample from CROMO, California, USA May 2016

description: Data in this sheet are normalized metagenomic data presented as Fragments Per Kilobase of Target Per Million Mapped Reads (FPKM) from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016. Metagenomic data was sequenced, library prep was performed, and quality control was done at the University of Utah before assembly using Megahit and prediction using Prodigal. Mapping of sequence reads was done using Bowtie2 and FPKM was calculated using the seq-annot package. Data was collected to identify the genes present at each depth in the profile.<br>
DOI: 10.6084/m9.figshare.14035976.v1
title: Data Sheet S4 - Fragments Per Kilobase of Target Per Million Mapped Reads (FPKM) of each CSW1.1 depth profile metagenomic sample from CROMO, California, USA May 2016

description: Data in this sheet are normalized metagenomic data presented as Fragments Per Kilobase of Target Per Million Mapped Reads (FPKM) from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016. Metagenomic data was sequenced, library prep was performed, and quality control was done at the University of Utah before assembly using Megahit and prediction using Prodigal. Mapping of sequence reads was done using Bowtie2 and FPKM was calculated using the seq-annot package. Data was collected to identify the genes present at each depth in the profile.<br>
DOI: 10.6084/m9.figshare.14035970
title: Data Sheet S3 – 16S rRNA gene sequence counts and taxonomy of common contaminants that were filtered out from CROMO, California, USA CSW1.1 well depth profile May 2016

description: Data included are taxa and their counts that were removed from the final 16S rRNA gene sequence counts and taxonomy information from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016. Filtering of common contaminants identified in Census of Deep Life samples was performed as described previously by Sheik et al., 2018. Sequences identified as eukaryotes, archaea, mitochondria, chloroplasts, singletons, and unknown by SILVA were removed. After this, the dataset was filtered to remove taxa identified by Census of Deep Life to be common contaminants. Data was removed to better identify the relevant organisms present at each depth in the profile.
DOI: 10.6084/m9.figshare.14035970.v1
title: Data Sheet S3 – 16S rRNA gene sequence counts and taxonomy of common contaminants that were filtered out from CROMO, California, USA CSW1.1 well depth profile May 2016

description: Data included are taxa and their counts that were removed from the final 16S rRNA gene sequence counts and taxonomy information from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016. Filtering of common contaminants identified in Census of Deep Life samples was performed as described previously by Sheik et al., 2018. Sequences identified as eukaryotes, archaea, mitochondria, chloroplasts, singletons, and unknown by SILVA were removed. After this, the dataset was filtered to remove taxa identified by Census of Deep Life to be common contaminants. Data was removed to better identify the relevant organisms present at each depth in the profile.
DOI: 10.6084/m9.figshare.14035961
title: Data Sheet S2 – 16S rRNA gene sequence counts and taxonomy from CROMO, California USA CSW1.1 well depth profile May 2016

description: Data included are final 16S rRNA gene sequence counts and taxonomy information from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016. Water samples for DNA extraction were collected from each depth using a peristaltic pump and sterile tubing. A 0.2 µm Sterivex filter was attached to collect cells, and DNA was extracted using phenol/chloroform extractions and precipitated with ethanol before purification using a Genomic Clean and Concentrate Kit (Zymo Research) and quantification via Qubit dsDNA High Sensitivity Assay. Purified bacterial samples were sequenced at the Genomics Core Facility at Michigan State University for analysis and processed on an Illumina MiSeq instrument. Sequences were processed and taxonomy was assigned using the SILVA SSURef alignment (v132) in Mothur. Filtering of common contaminants identified in Census of Deep Life samples was performed as described previously by Sheik et al., 2018. Data was collected to identify the organisms present at each depth in the profile.
DOI: 10.6084/m9.figshare.14035961.v1
title: Data Sheet S2 – 16S rRNA gene sequence counts and taxonomy from CROMO, California USA CSW1.1 well depth profile May 2016

description: Data included are final 16S rRNA gene sequence counts and taxonomy information from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016. Water samples for DNA extraction were collected from each depth using a peristaltic pump and sterile tubing. A 0.2 µm Sterivex filter was attached to collect cells, and DNA was extracted using phenol/chloroform extractions and precipitated with ethanol before purification using a Genomic Clean and Concentrate Kit (Zymo Research) and quantification via Qubit dsDNA High Sensitivity Assay. Purified bacterial samples were sequenced at the Genomics Core Facility at Michigan State University for analysis and processed on an Illumina MiSeq instrument. Sequences were processed and taxonomy was assigned using the SILVA SSURef alignment (v132) in Mothur. Filtering of common contaminants identified in Census of Deep Life samples was performed as described previously by Sheik et al., 2018. Data was collected to identify the organisms present at each depth in the profile.
DOI: 10.6084/m9.figshare.14035952
title: Data Sheet S1 – Thermodynamic Gibbs Energy Calculations for CROMO, California, USA CSW1.1 well depth profile May 2016

description: Data listed are the results of thermodynamic Gibbs energy calculations performed from a depth profile of the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA in June 2016 using a set of predetermined reactions. The Gibbs energy change associated with reactions involving H2, CH4, CO, and acetate as electron donors and O2, NO3-, and SO42- as electron acceptors used measured fluid chemistries in Table 1. The data was calculated using the activities established by fluid speciation calculations performed using Geochemist’s Workbench (Aqueous Solutions LLC, Champaign, IL, USA). Data was collected to assess the energy available for microbes to survive in these energy-limited fluids.
DOI: 10.6084/m9.figshare.14035952.v1
title: Data Sheet S1 – Thermodynamic Gibbs Energy Calculations for CROMO, California, USA CSW1.1 well depth profile May 2016

description: Data listed are the results of thermodynamic Gibbs energy calculations performed from a depth profile of the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA in June 2016 using a set of predetermined reactions. The Gibbs energy change associated with reactions involving H2, CH4, CO, and acetate as electron donors and O2, NO3-, and SO42- as electron acceptors used measured fluid chemistries in Table 1. The data was calculated using the activities established by fluid speciation calculations performed using Geochemist’s Workbench (Aqueous Solutions LLC, Champaign, IL, USA). Data was collected to assess the energy available for microbes to survive in these energy-limited fluids.
DOI: 10.5281/zenodo.4739364
title: The Agile Success Model

description: Replication Package for the paper: "The Agile Success Model: A Mixed Methods Study of a Large-Scale Agile Transformation". ACM Transactions on Software Engineering and Methodology. 2021.
DOI: 10.5281/zenodo.3549601
title: The Agile Success Model

description: Replication Package for the paper: "The Agile Success Model: A Mixed Methods Study of a Large-Scale Agile Transformation". ACM Transactions on Software Engineering and Methodology. 2021.
DOI: 10.3886/e137121
title: Data and Code for: Public Economics and Inequality: Uncovering Our Social Nature

description: This paper argues that the social nature of humans, absent from the standard economic model, is crucial to understand our large modern social states and why concerns about inequality are so pervasive. A social solution arises when a situation is resolved at the group level (rather than the individual level) through cooperation and fair distribution of the resulting surplus. In human societies, childcare and education for the young, retirement benefits for the old, health care for the sick, and income support for those in need, is resolved at the social level, and through the social state in advanced economies. Social situations are pervasive even outside government and play a significant role in the distribution of pre-tax market incomes.
DOI: 10.3886/e137121v1
title: Data and Code for: Public Economics and Inequality: Uncovering Our Social Nature

description: This paper argues that the social nature of humans, absent from the standard economic model, is crucial to understand our large modern social states and why concerns about inequality are so pervasive. A social solution arises when a situation is resolved at the group level (rather than the individual level) through cooperation and fair distribution of the resulting surplus. In human societies, childcare and education for the young, retirement benefits for the old, health care for the sick, and income support for those in need, is resolved at the social level, and through the social state in advanced economies. Social situations are pervasive even outside government and play a significant role in the distribution of pre-tax market incomes.
DOI: 10.3886/e138901
title: Data and Code for: Sectoral Effects of Social Distancing

description: The outbreak of the Covid-19 virus has led many states to take drastic measures of social distancing. Using US executive order, occupation and survey data, we measure the fall in labor supply due to these measure. Starting from a model of production networks, we analyze the sectoral effects of these labor shocks for the United States. We find that non-linearities in the production network account for around half of the drop in GDP associated to the implementation of social distancing measures. The model also generates realistic dispersion in sectoral output change. We provide here the replication package to construct the fall in labor supply, the figures and tables of the paper, and, to solve our model of production network.<br>
DOI: 10.3886/e138901v1
title: Data and Code for: Sectoral Effects of Social Distancing

description: The outbreak of the Covid-19 virus has led many states to take drastic measures of social distancing. Using US executive order, occupation and survey data, we measure the fall in labor supply due to these measure. Starting from a model of production networks, we analyze the sectoral effects of these labor shocks for the United States. We find that non-linearities in the production network account for around half of the drop in GDP associated to the implementation of social distancing measures. The model also generates realistic dispersion in sectoral output change. We provide here the replication package to construct the fall in labor supply, the figures and tables of the paper, and, to solve our model of production network.<br>
DOI: 10.3886/e139121
title: Data and Code for: The Family Origin of the Math Gender Gap is a White Affluent Phenomenon

description: Previous research has shown that norms around the role of women in society could help explain the gender gap in mathematics, and that these norms could be transmitted within the family. Using data from the Florida Department of Education combined with birth certificates we uncover important heterogeneity in the transmission of gender biases within the family. We find that gender role norms can explain the lower performance of girls in mathematics only in relatively affluent White families, whereas they do not apparently matter for the performance of Black girls.<br><br>
DOI: 10.3886/e139121v1
title: Data and Code for: The Family Origin of the Math Gender Gap is a White Affluent Phenomenon

description: Previous research has shown that norms around the role of women in society could help explain the gender gap in mathematics, and that these norms could be transmitted within the family. Using data from the Florida Department of Education combined with birth certificates we uncover important heterogeneity in the transmission of gender biases within the family. We find that gender role norms can explain the lower performance of girls in mathematics only in relatively affluent White families, whereas they do not apparently matter for the performance of Black girls.<br><br>