cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e149841
title: LIFE-M Ohio Causes of Death

description: Longitudinal and Intergenerational Determinants of Aging and Mortality was funded by the National Institute on Aging to add critical information on cause of death to a new large-scale data resource, the Longitudinal, Intergenerational Family Electronic Micro-database (LIFE-M). Enhancing the LIFE-M with cause of death will facilitate innovative research on the relationship of longevity and cause of death with demographic, socio-economic, and early-life environmental factors for family networks across four generations.<br><br>The grant had the following specific aims:<br>(1) Use the Smart Indexing technology to digitize and cross-validate hand-written cause-of-death information;<br>(2) Link digitized causes of death to the LIFE-M database and create documentation for this new variable for public use; and<br>(3) Publicly release the causes of death variable and documentation with the LIFE-M dataset, metadata, and supporting documentation.
description: Not applicable
description: Response Rates: Not applicable
description: Presence of Common Scales: Not applicable
description: We attempted to digitize cause of death for all individuals (1) born in Ohio born from 1866 to 1963 whose births were registered with the state (2) who were linked to an Ohio death certificate as part of the LIFE-M project (www.life-m.org). An additional requirement for inclusion is that the death certificate needed to be (3) captured in a digital image and (4) include a cause of death.
description: We include individuals (1) born in Ohio born from 1866 to 1963 whose births were registered with the state (2) who were linked to an Ohio death certificate as part of the LIFE-M project (www.life-m.org). An additional requirement for inclusion is that the death certificate needed to be (3) captured in a digital image and (4) include a cause of death. . Smallest Geographic Unit: State
description: other; web scraping; The cause of death information comes from linked vital records, including birth and death certificates.
DOI: 10.3886/e149841v2
title: LIFE-M Ohio Causes of Death

description: Longitudinal and Intergenerational Determinants of Aging and Mortality was funded by the National Institute on Aging to add critical information on cause of death to a new large-scale data resource, the Longitudinal, Intergenerational Family Electronic Micro-database (LIFE-M). Enhancing the LIFE-M with cause of death will facilitate innovative research on the relationship of longevity and cause of death with demographic, socio-economic, and early-life environmental factors for family networks across four generations.<br><br>The grant had the following specific aims:<br>(1) Use the Smart Indexing technology to digitize and cross-validate hand-written cause-of-death information;<br>(2) Link digitized causes of death to the LIFE-M database and create documentation for this new variable for public use; and<br>(3) Publicly release the causes of death variable and documentation with the LIFE-M dataset, metadata, and supporting documentation.
description: Not applicable
description: Response Rates: Not applicable
description: Presence of Common Scales: Not applicable
description: We attempted to digitize cause of death for all individuals (1) born in Ohio born from 1866 to 1963 whose births were registered with the state (2) who were linked to an Ohio death certificate as part of the LIFE-M project (www.life-m.org). An additional requirement for inclusion is that the death certificate needed to be (3) captured in a digital image and (4) include a cause of death.
description: We include individuals (1) born in Ohio born from 1866 to 1963 whose births were registered with the state (2) who were linked to an Ohio death certificate as part of the LIFE-M project (www.life-m.org). An additional requirement for inclusion is that the death certificate needed to be (3) captured in a digital image and (4) include a cause of death. . Smallest Geographic Unit: State
description: other; web scraping; The cause of death information comes from linked vital records, including birth and death certificates.
DOI: 10.6084/m9.figshare.19071153
title: Investigating the effects of different levels of guidance in inquiry-based hands-on and virtual science laboratories

description: Inquiry-based learning can be an effective approach when students are provided with sufficient guidance. However, there is an ongoing debate among practitioners about the nature, type, and level of such guidance. This study investigated the effects on middle school students’ (<i>N</i> = 116) conceptual knowledge and inquiry skills acquisition of providing two different levels of guidance (support or direct presentation of information related to the inquiry process) in hands-on and virtual science laboratories on the topic of electricity. The study was based on a 2 × 2 between-subjects factorial quasi-experimental design. The effects of two different levels of guidance combined with either a hands-on or a virtual laboratory were examined. Findings revealed that although the level of guidance was important for students’ conceptual knowledge development, it had no significant impact on their acquisition of inquiry skills. In terms of laboratory environments, it appears that a virtual laboratory and a hands-on laboratory are equally effective for the development of middle school students’ conceptual knowledge and inquiry skills.
DOI: 10.6084/m9.figshare.19071153.v1
title: Investigating the effects of different levels of guidance in inquiry-based hands-on and virtual science laboratories

description: Inquiry-based learning can be an effective approach when students are provided with sufficient guidance. However, there is an ongoing debate among practitioners about the nature, type, and level of such guidance. This study investigated the effects on middle school students’ (<i>N</i> = 116) conceptual knowledge and inquiry skills acquisition of providing two different levels of guidance (support or direct presentation of information related to the inquiry process) in hands-on and virtual science laboratories on the topic of electricity. The study was based on a 2 × 2 between-subjects factorial quasi-experimental design. The effects of two different levels of guidance combined with either a hands-on or a virtual laboratory were examined. Findings revealed that although the level of guidance was important for students’ conceptual knowledge development, it had no significant impact on their acquisition of inquiry skills. In terms of laboratory environments, it appears that a virtual laboratory and a hands-on laboratory are equally effective for the development of middle school students’ conceptual knowledge and inquiry skills.
DOI: 10.6073/pasta/061a105b3ebb79d9e4d53192f070b97f
title: Unregulated Wells on the Navajo Nation Data Compilation

description: In the United States the use of unregulated water sources – defined as sources that do not meet criteria to be classified as a public water system as defined by the Safe Drinking Water Act - are used regularly for livestock watering, agriculture, domestic, and other purposes. Nationally, more than 45 million people rely on unregulated water sources for drinking water; however, there remains infrastructure disparities for drinking water access in communities on Tribal nations. For the Navajo Nation, a sovereign Indigenous nation in the Southwestern United States, between 7% and 30% of homes lack plumbing to deliver household drinking water, so residents are compelled to access other water sources – regulated and unregulated alike. Previous unregulated water quality studies on the Navajo Nation were regionally focused and unsuitable for evaluating water quality trends across the Navajo Nation, an area that encompasses more than 71,000 square kilometers in Arizona, New Mexico, and Utah. Therefore, beginning in 2011 the Community Environmental Health Program at the University of New Mexico began to compile existing water quality datasets, principally for unregulated groundwater sources, in a single geospatial relational database. Researchers at the University of New Mexico Center for Native Environmental Health Equity Research, University of New Mexico METALS Superfund Research Program, University of Arizona, Northern Arizona University, and the Southwest Research and Information Center have compiled a database of water quality measurements from groundwater wells on the Navajo Nation using data from the U.S. Geological Survey, U.S. Army Corps of Engineers, U.S. Centers for Disease Control and Prevention, Navajo Nation Environmental Protection Agency, and data from researchers at the University of New Mexico, Dine College and Northern Arizona University. To date, this data compilation has been used for publications but has not been disseminated publicly. The purpose of this website is to facilitate access to these compiled water quality data. The application design enables users to view water quality information using statistical and geospatial tools. Our hope is that this information will support individual and community decisions about water use from unregulated sources.
DOI: 10.25345/c5hd7nw5w
title: MassIVE MSV000089066 - Evaluating the carrier proteome effect on a TIMSTOF mass analyzer
title: We have simulated a carrier proteome effect using a two-proteome quantitative TMTPro 9-plex labeled sample series to evaluate the effects of extreme carrier channels on TIMSTOF quantification.
DOI: 10.6084/m9.figshare.19364714.v1
title: Data File for: "Institutions, Entrepreneurship, and Regional Differences in Economic Growth"

description: Data file for:<br>Joshua Hall and Russell Sobel, “Institutions, Entrepreneurship, and Regional Differences in Economic Growth,” <i>Southern Journal of Entrepreneurship</i> 1(1) 2008: 69-96.
DOI: 10.6084/m9.figshare.19364714
title: Data File for: "Institutions, Entrepreneurship, and Regional Differences in Economic Growth"

description: Data file for:<br>Joshua Hall and Russell Sobel, “Institutions, Entrepreneurship, and Regional Differences in Economic Growth,” <i>Southern Journal of Entrepreneurship</i> 1(1) 2008: 69-96.
DOI: 10.5517/ccdc.csd.cc25bww9
title: CCDC 2006471: Experimental Crystal Structure Determination

description: Related Article: Claudio A. G. da Camara, Bogdan Doboszewski, João P. R. de Melo, Alexander Y. Nazarenko, Rodrigo B. dos Santos, Marcilio M. Moraes|2022|J.Braz.Chem.Soc.|33|196|doi:10.21577/0103-5053.20210137
DOI: 10.5517/ccdc.csd.cc28tj1p
title: CCDC 2109892: Experimental Crystal Structure Determination

description: Related Article: Brian W. Skelton, Effendy, Allan H. White|2021|CSD Communication|||
description: Related Article: Effendy, Corrado Di Nicola, Peter C. Healy, Fabio Marchetti, Claudio Pettinari, Brian W. Skelton, Alessia Tombesi, Allan H. White|2022|Inorg.Chim.Acta|536|120895|doi:10.1016/j.ica.2022.120895
DOI: 10.5517/ccdc.csd.cc28ws32
title: CCDC 2112064: Experimental Crystal Structure Determination

description: Related Article: Shanping Chen, Yi Xia, Shikai Jin, Hanwen Lei, Kuiyi You, Guo-Jun Deng|2022|J.Org.Chem.|87|3212|doi:10.1021/acs.joc.1c02927
DOI: 10.5517/ccdc.csd.cc28ws43
title: CCDC 2112065: Experimental Crystal Structure Determination

description: Related Article: Shanping Chen, Yi Xia, Shikai Jin, Hanwen Lei, Kuiyi You, Guo-Jun Deng|2022|J.Org.Chem.|87|3212|doi:10.1021/acs.joc.1c02927
DOI: 10.5517/ccdc.csd.cc28z4t7
title: CCDC 2114349: Experimental Crystal Structure Determination

description: Related Article: Anna Ordyszewska, Natalia Szynkiewicz, Jarosaw Chojnacki, Rafa Grubba|2022|Inorg.Chem.|61|4361|doi:10.1021/acs.inorgchem.1c03618
DOI: 10.5517/ccdc.csd.cc28z4v8
title: CCDC 2114350: Experimental Crystal Structure Determination

description: Related Article: Anna Ordyszewska, Natalia Szynkiewicz, Jarosaw Chojnacki, Rafa Grubba|2022|Inorg.Chem.|61|4361|doi:10.1021/acs.inorgchem.1c03618
DOI: 10.5517/ccdc.csd.cc28z4w9
title: CCDC 2114351: Experimental Crystal Structure Determination

description: Related Article: Anna Ordyszewska, Natalia Szynkiewicz, Jarosaw Chojnacki, Rafa Grubba|2022|Inorg.Chem.|61|4361|doi:10.1021/acs.inorgchem.1c03618
DOI: 10.5517/ccdc.csd.cc28z4xb
title: CCDC 2114352: Experimental Crystal Structure Determination

description: Related Article: Anna Ordyszewska, Natalia Szynkiewicz, Jarosaw Chojnacki, Rafa Grubba|2022|Inorg.Chem.|61|4361|doi:10.1021/acs.inorgchem.1c03618
DOI: 10.5517/ccdc.csd.cc28z4yc
title: CCDC 2114353: Experimental Crystal Structure Determination

description: Related Article: Anna Ordyszewska, Natalia Szynkiewicz, Jarosaw Chojnacki, Rafa Grubba|2022|Inorg.Chem.|61|4361|doi:10.1021/acs.inorgchem.1c03618
DOI: 10.5517/ccdc.csd.cc28z4zd
title: CCDC 2114354: Experimental Crystal Structure Determination

description: Related Article: Anna Ordyszewska, Natalia Szynkiewicz, Jarosaw Chojnacki, Rafa Grubba|2022|Inorg.Chem.|61|4361|doi:10.1021/acs.inorgchem.1c03618
DOI: 10.5517/ccdc.csd.cc28z50g
title: CCDC 2114355: Experimental Crystal Structure Determination

description: Related Article: Anna Ordyszewska, Natalia Szynkiewicz, Jarosaw Chojnacki, Rafa Grubba|2022|Inorg.Chem.|61|4361|doi:10.1021/acs.inorgchem.1c03618
DOI: 10.6084/m9.figshare.17155676
title: The state of Medusozoa genomics: supplementary material

description: Supplementary Material of the review manuscript entitled "State of Medusozoa genomics: current evidence and future challanges" <br><b>Supplementary file S1. Dataset 1. </b>Genome report sheet (version 1.2022). This report presents information from public databases, project-based databases, published articles. New releases of this sheet can be published and it can be uploaded upon other author's request.<br><b>Supplementary file S2. Dataset 2.</b> Command line to retrieve data from NCBI and to generate new results. Presented as html notebooks and plain scripts. Written in bash<b>Supplementary file S3. Dataset 3. </b>Scripts used for graph construction. Presented as html notebooks, jupyter notebooks, and plain scripts. Written in python &gt;3.6<b>Supplementary file S4. Table S1. </b>Species information considering chromosome number, genome size and genomic datasets.<b>Supplementary file S5. Tables S2-8 -</b> All information used for constructing graphs presented in this work. Includes summary information of Figure 2 (table S2), genome resources used in this study (table S3), assembly statistics for Figure 3A (table S4), genome features of Figure 3B (table S5, S6) and BUSCO results for Figure 4 and Supplementary figure S1 (tables S7, S8).<b>Supplementary file S6. Figure S1. </b>BUSCO Eukaryota gene distribution in Medusozoa assemblies. Each column corresponds to a gene and each row an assembly. Information used for this graph is available at Supplementary file S4 Table S8.<b>Supplementary file S7 - Dataset 4. </b>Original metadata from NCBI.<b>Supplementary file S8 - Dataset 5. </b>Original results from AGAT and Galaxy server (BUSCO). Directory structure coincident with directories specified in scripts for graphs.<b>Supplementary file S9 - Dataset 6. </b> Figures in vectorial format
DOI: 10.6084/m9.figshare.17155676.v1
title: The state of Medusozoa genomics: supplementary material

description: Supplementary Material of the review manuscript entitled "State of Medusozoa genomics: current evidence and future challanges" <br><b>Supplementary file S1. Dataset 1. </b>Genome report sheet (version 1.2022). This report presents information from public databases, project-based databases, published articles. New releases of this sheet can be published and it can be uploaded upon other author's request.<br><b>Supplementary file S2. Dataset 2.</b> Command line to retrieve data from NCBI and to generate new results. Presented as html notebooks and plain scripts. Written in bash<b>Supplementary file S3. Dataset 3. </b>Scripts used for graph construction. Presented as html notebooks, jupyter notebooks, and plain scripts. Written in python &gt;3.6<b>Supplementary file S4. Table S1. </b>Species information considering chromosome number, genome size and genomic datasets.<b>Supplementary file S5. Tables S2-8 -</b> All information used for constructing graphs presented in this work. Includes summary information of Figure 2 (table S2), genome resources used in this study (table S3), assembly statistics for Figure 3A (table S4), genome features of Figure 3B (table S5, S6) and BUSCO results for Figure 4 and Supplementary figure S1 (tables S7, S8).<b>Supplementary file S6. Figure S1. </b>BUSCO Eukaryota gene distribution in Medusozoa assemblies. Each column corresponds to a gene and each row an assembly. Information used for this graph is available at Supplementary file S4 Table S8.<b>Supplementary file S7 - Dataset 4. </b>Original metadata from NCBI.<b>Supplementary file S8 - Dataset 5. </b>Original results from AGAT and Galaxy server (BUSCO). Directory structure coincident with directories specified in scripts for graphs.<b>Supplementary file S9 - Dataset 6. </b> Figures in vectorial format
DOI: 10.5281/zenodo.5113055
title: Data archive for paper "Machine Learning Emulation of 3D Cloud Radiative Effects"

description: <strong>Overview</strong> This archive contains models, data, and the Singularity image to optionally rerun experiments described in "Machine Learning Emulation of 3D Cloud Radiative Effects". For the Python tool to generate synthetic data, please refer to the Synthia repository. <strong>Prerequisites</strong> Linux or macOS with Bash shell. Singularity (tested with version 3.6.3-1.el8)*. Portable Batch System (PBS) job scheduler**. *Please note that all steps require Singularity to be installed on your system. If you are looking for information on how to install or use Singularity, please refer to the Singularity documentation. **Although PBS in not a strict requirement, it is required to run all helper scripts as included in this repository. Please note that depending on your specific system settings and resource availability, you may need to modify PBS parameters at the top of submit scripts stored in the <code>hpc</code> directory (e.g. <code>#PBS -lwalltime=24:00:00</code>). <strong>Initialization</strong> Deflate the data archive with: <pre><code>./init.sh </code></pre> Compile ecRad with Singularity: <pre><code>./tools/singularity/compile_ecrad.sh </code></pre> <strong>Usage</strong> To reproduce the results as described in the paper, run the following commands from the <code>hpc</code> folder: <pre><code>qsub -v JOB_NAME=mlp_default ./submit_grid_search_default.sh qsub -v JOB_NAME=mlp_synthia ./submit_grid_search_synthia.sh qsub submit_benchmark.sh </code></pre> then, to plot stats and identify notebooks run: <pre><code>qsub submit_stats.sh </code></pre> <strong>License</strong> Paper code released under the MIT license. Data released under CC BY 4.0. ecRad released under the Apache 2.0 license.
DOI: 10.5281/zenodo.4625413
title: Data archive for paper "Machine Learning Emulation of 3D Cloud Radiative Effects"

description: <strong>Overview</strong> This archive contains models, data, and the Singularity image to optionally rerun experiments described in "Machine Learning Emulation of 3D Cloud Radiative Effects". For the Python tool to generate synthetic data, please refer to the Synthia repository. <strong>Prerequisites</strong> Linux or macOS with Bash shell. Singularity (tested with version 3.6.3-1.el8)*. Portable Batch System (PBS) job scheduler**. *Please note that all steps require Singularity to be installed on your system. If you are looking for information on how to install or use Singularity, please refer to the Singularity documentation. **Although PBS in not a strict requirement, it is required to run all helper scripts as included in this repository. Please note that depending on your specific system settings and resource availability, you may need to modify PBS parameters at the top of submit scripts stored in the <code>hpc</code> directory (e.g. <code>#PBS -lwalltime=24:00:00</code>). <strong>Initialization</strong> Deflate the data archive with: <pre><code>./init.sh </code></pre> Compile ecRad with Singularity: <pre><code>./tools/singularity/compile_ecrad.sh </code></pre> <strong>Usage</strong> To reproduce the results as described in the paper, run the following commands from the <code>hpc</code> folder: <pre><code>qsub -v JOB_NAME=mlp_default ./submit_grid_search_default.sh qsub -v JOB_NAME=mlp_synthia ./submit_grid_search_synthia.sh qsub submit_benchmark.sh </code></pre> then, to plot stats and identify notebooks run: <pre><code>qsub submit_stats.sh </code></pre> <strong>License</strong> Paper code released under the MIT license. Data released under CC BY 4.0. ecRad released under the Apache 2.0 license.
DOI: 10.5281/zenodo.5142960
title: Data archive for paper "Machine Learning Emulation of Urban Land Surface Processes"

description: This archive contains models, data* (Overview), as well as the Singularity image to optionally rerun experiments described in "Machine Learning Emulation of Urban Land Surface Processes". <strong>Prerequisites</strong> Linux or macOS with Bash shell. Singularity (tested with version 3.6.3-1.el8) Please note that all steps require Singularity to be installed on your system. If you are looking for information on how to install or use Singularity, please refer to the Singularity documentation. <strong>Overview</strong> A general overview of the repository structure is given below. Due to licensing restrictions analysis and forcing data (*) cannot be included and need to be requested separately (see Initialization). Data derivatives (**) from either analysis or forcing, as well as intermediary data (***), are not included as they can be generated by rerunning experiments (see Usage). <pre><code>. ├── data │ ├── analysis* │ ├── forcing* │ ├── teb │ ├── utils │ ├── wps │ └── wrf ├── hpc ├── models │ ├── teb │ ├── unn │ ├── wps │ └── wrf-unn ├── notebooks ├── outputs │ ├── analysis** │ ├── benchmark*** │ ├── forcing** │ ├── kerastuner*** │ ├── notebooks │ ├── tabular │ ├── teb** │ ├── unn** │ ├── wps*** │ └── wrf ├── paper │ └── figures ├── singularity └── tools </code></pre> <strong>Initialization</strong> Forcing and analysis data need to be requested separately. The following directories should map to their respective data archives: <code>./data/analysis</code> -&gt; Grimmond et al. (2013) <code>./data/forcing</code> -&gt; Grimmond et al. (2021) <strong>Usage</strong> To rerun all experiments and reproduce results, run <code>tools/run_all.sh</code> from your command prompt. After completion, all results are saved in the <code>outputs</code> directory. Note that WRF simulations require high CPU time and may take hours or days to complete. Alternatively, if Portable Batch System (PBS) is available on your system, the following helpers may be used instead: <pre><code>qsub hpc/submit_init.pbs qsub hpc/submit_tuner.pbs qsub hpc/submit_unn.pbs qsub hpc/submit_find_median_unn.pbs qsub hpc/submit_wrf.pbs qsub hpc/submit_postprocess.pbs qsub hpc/submit_benchmark.pbs </code></pre> Note that you may need to modify PBS helper scripts to suit your specific environment. <strong>Development notes</strong> See DEVELOP.md. <strong>License</strong> The source code developed for this work is licensed under MIT (<code>LICENSE_CODE.txt</code>). For licensing information of third-party software see licenses under the <code>models</code> directory. Data files in this archive, including the initial and boundary condition data from the European Centre for Medium-Range Weather Forecasts (<code>data/wps/ungrib</code>), are licensed under CC BY-NC 4.0 (<code>LICENSE_DATA.txt</code>).