cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18739/a2zs2kf0d
title: Alaska Native Successful Aging, Northwest, Anchorage, Aleutian Pribilof Islands, 2016-2021

description: One on one, semi-structured interviews with Alaska Native (AN) Elders (Aleut, Inupiat, Central Yup'ik, Siberian Yupik) in each of the participating communities in the Norton Sound southern sub region, Aleutian Pribilof Island communities, and Anchorage. These interviews explored the meaning of successful aging, what is required to age successfully in their respective communities, and how they have maintained their health and well-being in both rural and urban settings. A second semi-structured one on one interview questionnaire was used with those Alaska Native Elders originally from the Norton Sound southern sub-region who have relocated to Anchorage. This questionnaire explored the reasons the Alaska Native Elders moved, the type and frequency of services they needed, and how their place of residence changed their definition of successful aging.
DOI: 10.6084/m9.figshare.16798177
title: HDF5 files generated during HELAO hardware in the loop runs

description: These are the HDF5 files that conatin all data handled by the orchestrator in the paper with the title Enabling modular autonomous feedback-loops in materials science through hierarchical experimental laboratory automation and orchestration authored by Fuzhan Rahmanian, Jackson Flowers, Dan Guevarra, Matthias Richter, Maximilian Fichtner, John M. Gregoire, and Helge S. Stein. The sequential and paralell run are in two separate files as these were run in different sessions.
DOI: 10.6069/9e42-3j32
title: Utilizing Pine Needles to Temporally and Spatially Profile Per- and Polyfluoroalkyl Substances (PFAS)

description: Per- and polyfluoroalkyl substances (PFAS) are a class of manmade organofluorine chemicals used in a variety of household and industrial applications. PFAS have become a global concern due to their environmental persistence, bioaccumulative nature, and associations with adverse health effects, thus monitoring both spatial and temporal PFAS presence is crucial. PFAS levels are commonly monitored in surface and groundwater, soil, and wildlife samples, however assessing atmospheric PFAS is less common due to the necessary expensive equipment which can only be placed at a limited number of sites. Recently, the passive sampling capabilities of trees have been investigated to address this challenge. For example, pine needles possess highly adsorptive wax cuticles and have been effective passive samplers for a wide variety of environmental contaminants, including legacy PFAS or PFAS compounds with long aliphatic chains. To understand PFAS contamination in North Carolina from point sources such as a fluorochemical manufacturer, military bases, and airports, we leveraged the passive sampling capabilities of pine needles. The PFAS in the needles were extracted with an optimized protocol and analyzed using a non-targeted platform coupling liquid chromatography, ion mobility spectrometry and mass spectrometry (LC-IMS-MS) separations, allowing simultaneous hydrophobicity, size, and mass evaluations. This new method resulted in the identification of over 60 PFAS compounds in the pine needles, along with the detection of more than 10 unknown PFAS species. Furthermore, the detected species covered all of the legacy compounds identified in previous studies, as well as multiple classes of emerging PFAS with shorter aliphatic chains and structural modifications such as ether linkages and branched or cyclic aliphatic chains, not previously detected in pine needles. This method was also successful in identifying multiple PFAS point sources, as well as longitudinally monitoring the levels of the diverse PFAS compounds across North Carolina to address environmental concentration changes due to introduction and remediation efforts. Additionally, archived pine needles were analyzed to assess historical PFAS levels from the 1960s-2000s.
DOI: 10.25810/9p4t-8n70
title: Quantifying microburst wind and turbulence enhancement in canyons

description: This data set contains model output from NCARS's Weather and Research Forecasting(WRF) model run in large-eddy simulation (LES) mode (WRF v3.6). A series of idealized simulations were conducted with the center of microburst downdrafts were placed 1.3 and 3.3 km upwind of a series of canyon types differing in length and slope angle. These canyon simulations are compared to microburst outflow boundary characteristics in flat terrain deriving topographic multiplier and differences in horizontal winds (wsp), upward vertical velocity (w), and turbulence kinetic energy (TKE). This data set is associated with a journal publication by  Luchetti et al. submitted to Monthly Weather Review in September 2021. The WRF model code (https://doi.org/10.5065/D6MK6B4K, Skamarock et al., 2008) is publicly available at http://www2.mmm.ucar.edu/wrf/users/ (last access: 11 June 2020). Model output data for hours 6-7 h and analysis code including namelist.input, input soundings, and auxiliary terrain netcdf files needed to run the simulations and create the figures are located at https://github.com/nluchett/wrf_canyon_experiment.
DOI: 10.5066/p9w74k0k
title: Suspended Sediment and Water Temperature Data in the Suiattle River and the Downey Creek Tributary, Washington for select time periods over 2013 - 2017

description: This data release provides suspended-sediment (concentration and load) and water temperature data for two locations along the Suiattle River (USGS 1217900 and 12188380) and a tributary, Downey Creek (USGS 1217985) in Washington State for partial periods over 2013-2017. Suspended-sediment and water temperature data were collected over two summer seasons from May through September 2016 and 2017 at USGS 1217900. Suspended-sediment and water temperature data were collected that approximately covers the Water Year 2017, extending from October 24, 2016 to November 16, 2017 at USGS 1217985. Suspended-sediment and water temperature data were collected from December 2013 through September 2017 at USGS 12188380.
DOI: 10.5517/ccdc.csd.cc25z9gy
title: CCDC 2025120: Experimental Crystal Structure Determination

description: Related Article: Lotta Turunen, Flóra Boróka Németh, Daniel A. Decato, Imre Pápai, Orion B. Berryman, Máté Erdélyi|2020|Bull.Chem.Soc.Jpn.|94|191|doi:10.1246/bcsj.20200274
DOI: 10.5517/ccdc.csd.cc27j0f7
title: CCDC 2070968: Experimental Crystal Structure Determination

description: Related Article: Zhan-Cai Li, Yu Zhang, Bing-Xia Yan, Xiao-Ning Wang, De-Hua Zhai, Qiang Li, Hong-Xing Zheng, Chang-Qiu Zhao|2021|Org.Chem.Front.|8|5693|doi:10.1039/D1QO00925G
DOI: 10.5517/ccdc.csd.cc27j0ld
title: CCDC 2070973: Experimental Crystal Structure Determination

description: Related Article: Zhan-Cai Li, Yu Zhang, Bing-Xia Yan, Xiao-Ning Wang, De-Hua Zhai, Qiang Li, Hong-Xing Zheng, Chang-Qiu Zhao|2021|Org.Chem.Front.|8|5693|doi:10.1039/D1QO00925G
DOI: 10.5517/ccdc.csd.cc27j0qj
title: CCDC 2070977: Experimental Crystal Structure Determination

description: Related Article: Zhan-Cai Li, Yu Zhang, Bing-Xia Yan, Xiao-Ning Wang, De-Hua Zhai, Qiang Li, Hong-Xing Zheng, Chang-Qiu Zhao|2021|Org.Chem.Front.|8|5693|doi:10.1039/D1QO00925G
DOI: 10.5517/ccdc.csd.cc27kmhy
title: CCDC 2072520: Experimental Crystal Structure Determination

description: Related Article: Zhan-Cai Li, Yu Zhang, Bing-Xia Yan, Xiao-Ning Wang, De-Hua Zhai, Qiang Li, Hong-Xing Zheng, Chang-Qiu Zhao|2021|Org.Chem.Front.|8|5693|doi:10.1039/D1QO00925G
DOI: 10.5517/ccdc.csd.cc27h643
title: CCDC 2070184: Experimental Crystal Structure Determination

description: Related Article: Yifu Chen, Bo Jing, Zewei Chang, Junbo Gong|2021|CrystEngComm|23|6838|doi:10.1039/D1CE00830G
DOI: 10.5517/ccdc.csd.cc27n7sy
title: CCDC 2075040: Experimental Crystal Structure Determination

description: Related Article: Pan-Feng Yuan, Tao Huang, Jian He, Xie-Tian Huang, Xiao-Ling Jin, Chunlin Sun, Li-Zhu Wu, Qiang Liu|2021|Org.Chem.Front.|8|5785|doi:10.1039/D1QO01101D
DOI: 10.5517/ccdc.csd.cc27p93c
title: CCDC 2076042: Experimental Crystal Structure Determination

description: Related Article: Yuming Zhao, Jinhui Liu, Sha Wang, Xu Zhai, Lei Shao, Yunling Liu, Xuemin Zhang, Junyi Chen, Fanbao Meng, Yu Fu|2021|Dalton Trans.|50|13823|doi:10.1039/D1DT02249K
DOI: 10.5517/ccdc.csd.cc27rvnj
title: CCDC 2078508: Experimental Crystal Structure Determination

description: Related Article: Yucong Sun, Cancan Feng, Peisong Wang, Fan Yang, Yangjie Wu|2021|Org.Chem.Front.|8|5710|doi:10.1039/D1QO00975C
DOI: 10.5517/ccdc.csd.cc27v6nz
title: CCDC 2080771: Experimental Crystal Structure Determination

description: Related Article: Yue Chen, Qi Yang, Guo Peng, Yi-Quan Zhang, Xiao-Ming Ren|2021|Dalton Trans.|50|13830|doi:10.1039/D1DT02148F
DOI: 10.5517/ccdc.csd.cc27vds9
title: CCDC 2080961: Experimental Crystal Structure Determination

description: Related Article: Chandan Kumar Giri, Suman Dana, Mahiuddin Baidya|2021|Chem.Commun.|57|10536|doi:10.1039/D1CC04422B
DOI: 10.5517/ccdc.csd.cc27vp1v
title: CCDC 2081217: Experimental Crystal Structure Determination

description: Related Article: Christoph Wallach, Yasmin Selic, Benedikt J. L. Witzel, Wilhelm Klein, Thomas F. Fässler|2021|Dalton Trans.|50|13671|doi:10.1039/D1DT03078G
DOI: 10.5517/ccdc.csd.cc27vp2w
title: CCDC 2081218: Experimental Crystal Structure Determination

description: Related Article: Christoph Wallach, Yasmin Selic, Benedikt J. L. Witzel, Wilhelm Klein, Thomas F. Fässler|2021|Dalton Trans.|50|13671|doi:10.1039/D1DT03078G
DOI: 10.5517/ccdc.csd.cc27vp3x
title: CCDC 2081219: Experimental Crystal Structure Determination

description: Related Article: Christoph Wallach, Yasmin Selic, Benedikt J. L. Witzel, Wilhelm Klein, Thomas F. Fässler|2021|Dalton Trans.|50|13671|doi:10.1039/D1DT03078G
DOI: 10.5517/ccdc.csd.cc27vr62
title: CCDC 2081284: Experimental Crystal Structure Determination

description: Related Article: John A. Kelly, Johannes Gramüller, Ruth M. Gschwind, Robert Wolf|2021|Dalton Trans.|50|13985|doi:10.1039/D1DT02621F
DOI: 10.5517/ccdc.csd.cc27vz7b
title: CCDC 2081502: Experimental Crystal Structure Determination

description: Related Article: Chong-Chong Yan, Si-Fu Tang|2021|Inorg.Chem.Front.|8|4448|doi:10.1039/D1QI00663K
DOI: 10.5517/ccdc.csd.cc27vz8c
title: CCDC 2081503: Experimental Crystal Structure Determination

description: Related Article: Chong-Chong Yan, Si-Fu Tang|2021|Inorg.Chem.Front.|8|4448|doi:10.1039/D1QI00663K
DOI: 10.5517/ccdc.csd.cc27vxnp
title: CCDC 2081453: Experimental Crystal Structure Determination

description: Related Article: Chaoqi Ke, Zhenzhong Liu, Sai Ruan, Xiaoming Feng, Xiaohua Liu|2021|Org.Chem.Front.|8|5705|doi:10.1039/D1QO00948F
DOI: 10.5281/zenodo.5562568
title: Estimates of high tide flooding on roadways within urban areas along the United States Atlantic coast

description: Estimates of high tide flooding (HTF) on roadways in urban areas along the US Atlantic Coast. These estimates were calculated using NOAA HTF areal extent estimates, OpenStreetMap roadway data, and 2010 census-designated urban area and census block data. See the corresponding manuscript (Gold et al., 2021 - link coming soon) and GitHub repository for additional information about these data.
DOI: 10.5281/zenodo.5562567
title: Estimates of high tide flooding on roadways within urban areas along the United States Atlantic coast

description: Estimates of high tide flooding (HTF) on roadways in urban areas along the US Atlantic Coast. These estimates were calculated using NOAA HTF areal extent estimates, OpenStreetMap roadway data, and 2010 census-designated urban area and census block data. See the corresponding manuscript (Gold et al., 2021 - link coming soon) and GitHub repository for additional information about these data.
DOI: 10.5063/f1sb445f
title: A GBIF endangered species diversity analysis of burn sites in the Southwestern, USA

description: The purpose of this dataset is to evaluate the impact of fires on endangered species biodiversity in California's southwest desert. Species data was downloaded from the Global Diversity Information Facility (GBIF).
DOI: 10.6084/m9.figshare.16799380
title: Figure 7

description: These files contain the data for Figure 7 in the paper.
DOI: 10.6084/m9.figshare.16799380.v1
title: Figure 7

description: These files contain the data for Figure 7 in the paper.
DOI: 10.5281/zenodo.5565192
title: Garbage Dataset

description: 1000 images of randomly generated trashes!
DOI: 10.5281/zenodo.5564665
title: Arius madagascariensis SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5564664
title: Arius madagascariensis SPAdes preassembly

description: .
DOI: 10.5281/zenodo.4930054
title: CIOA Results

description: <pre>These files contain the results obtained from the CIOA tests and analyses. </pre>
DOI: 10.5281/zenodo.4930053
title: CIOA Results

description: <pre>These files contain the results obtained from the CIOA tests and analyses. </pre>
DOI: 10.5281/zenodo.5542323
title: Geoscience OER Environmental Scan and Data

description: <strong>Title: </strong>Geoscience OER Environmental Scan and Data, from <em>A Review of Open Educational Resources in the Geosciences: Guidelines and Criteria</em> <strong>Authors: </strong> <strong>Samantha Teplitzky</strong>*, Open Science Librarian, Earth Sciences & Map Library, University of California, Berkeley, 50 McCone Hall, Berkeley, CA 94720, steplitz@berkeley.edu, ORCID: 0000-0001-7071-332X <strong>Shaun Hardy</strong>, Librarian, Earth & Planets Laboratory, Carnegie Institution for Science, Washington, DC 20015, shardy@carnegiescience.edu, ORCID: 0000-0002-7259-3330 <strong>Kay Johnson</strong>, Head of Collection and Technical Services, Radford University, 801 E. Main St., Radford, VA 24142-6881, kjohnson497@radford.edu, ORCID: 0000-0001-6472-345X <strong>Robert Tolliver</strong>, Sciences Librarian, North Dakota State University, Fargo, ND 58108-6050, robert.tolliver@ndsu.edu, ORCID: 0000-0003-1584-433X <strong>Lori Tschirhart</strong>, Assistant Director, STEM, University of Michigan Library, Ann Arbor, MI 48109-1190, ltz@umich.edu, ORCID: 0000-0001-9322-3171 <strong>Ifigenia Vardakosta</strong>, Phd. Head Librarian, Harokopio University, Library & Information Centre, Athens, Greece, ifigenia@hua.gr, ORCID: 0000-0001-9465-8476 <strong>Keywords:</strong><br> Open Educational Resources, OER, Geoscience Education This repository documents the background information to the following report: Teplitzky, S., Hardy, S., Johnson, K., Tolliver, R., Tschirhart, L., & Vardakosta, I. (2021). <em>A Review of Open Educational Resources in the Geosciences: Guidelines and Criteria. </em>https://doi.org/10.31223/X5J90G <strong>Report</strong> This study examines the creation and adoption of OER in the Geosciences with a goal of providing guidance for institutions, libraries and librarians who support and fund OER initiatives. An environmental scan, employed to identify and characterize available college-level OER in this discipline, provided a baseline for the study. Analysis of the scan along with a review of OER, textbook and field guide standards informs a new set of proposed guidelines for Geoscience OER. This work will describe these guidelines and offer a call for community feedback. <strong>Data</strong> Appendix 1: Geoscience OER Environmental Scan and Data, 10.5281/zenodo.5542323 <strong>Research Products</strong> Guidelines Table, 10.5281/zenodo.5542306 <em>This work is a collaboration of members of the GeoScience Information Society (GSIS) which emerged from the Professional Issues Roundtable at the 2020 Annual Meeting (virtually on October 30, 2020).</em>
DOI: 10.5281/zenodo.5542322
title: Geoscience OER Environmental Scan and Data

description: <strong>Title: </strong>Geoscience OER Environmental Scan and Data, from <em>A Review of Open Educational Resources in the Geosciences: Guidelines and Criteria</em> <strong>Authors: </strong> <strong>Samantha Teplitzky</strong>*, Open Science Librarian, Earth Sciences & Map Library, University of California, Berkeley, 50 McCone Hall, Berkeley, CA 94720, steplitz@berkeley.edu, ORCID: 0000-0001-7071-332X <strong>Shaun Hardy</strong>, Librarian, Earth & Planets Laboratory, Carnegie Institution for Science, Washington, DC 20015, shardy@carnegiescience.edu, ORCID: 0000-0002-7259-3330 <strong>Kay Johnson</strong>, Head of Collection and Technical Services, Radford University, 801 E. Main St., Radford, VA 24142-6881, kjohnson497@radford.edu, ORCID: 0000-0001-6472-345X <strong>Robert Tolliver</strong>, Sciences Librarian, North Dakota State University, Fargo, ND 58108-6050, robert.tolliver@ndsu.edu, ORCID: 0000-0003-1584-433X <strong>Lori Tschirhart</strong>, Assistant Director, STEM, University of Michigan Library, Ann Arbor, MI 48109-1190, ltz@umich.edu, ORCID: 0000-0001-9322-3171 <strong>Ifigenia Vardakosta</strong>, Phd. Head Librarian, Harokopio University, Library & Information Centre, Athens, Greece, ifigenia@hua.gr, ORCID: 0000-0001-9465-8476 <strong>Keywords:</strong><br> Open Educational Resources, OER, Geoscience Education This repository documents the background information to the following report: Teplitzky, S., Hardy, S., Johnson, K., Tolliver, R., Tschirhart, L., & Vardakosta, I. (2021). <em>A Review of Open Educational Resources in the Geosciences: Guidelines and Criteria. </em>https://doi.org/10.31223/X5J90G <strong>Report</strong> This study examines the creation and adoption of OER in the Geosciences with a goal of providing guidance for institutions, libraries and librarians who support and fund OER initiatives. An environmental scan, employed to identify and characterize available college-level OER in this discipline, provided a baseline for the study. Analysis of the scan along with a review of OER, textbook and field guide standards informs a new set of proposed guidelines for Geoscience OER. This work will describe these guidelines and offer a call for community feedback. <strong>Data</strong> Appendix 1: Geoscience OER Environmental Scan and Data, 10.5281/zenodo.5542323 <strong>Research Products</strong> Guidelines Table, 10.5281/zenodo.5542306 <em>This work is a collaboration of members of the GeoScience Information Society (GSIS) which emerged from the Professional Issues Roundtable at the 2020 Annual Meeting (virtually on October 30, 2020).</em>
DOI: 10.15482/usda.adc/1523364
title: Data From: Long-lasting insecticide treated netting affects reproductive output and mating behavior in Tribolium castaneum (Coleoptera: Tenebrionidae) and Trogoderma variabile (Coleoptera: Dermestidae)
DOI: 10.5281/zenodo.5564663
title: SSH Username Enumeration Attack Detection Dataset

description: The dataset is collected from a closed-environment network using network monitoring tools installed in the data collection point. The dataset generation was achieved through the use of common vulnerabilities and exposures (CVE) with the identification number CVE-2018-15473 retrieved from the public exploits database and pcap file of normal traffic obtained from public training repository. A total of 36,273 instances were collected with two classes <em>“username enumeration attack”</em> and “<em>non-username enumeration</em>”. We chose the terms <em>“username enumeration attack”</em> and “<em>non-username enumeration</em>” instead of the traditional <em>“attack”</em> and <em>“normal”</em> label notations since <em>“</em>normal<em>”</em> traffic data could contain attacks other than username enumeration attack. The username enumeration attack corresponds to the attack traffic while non-username enumeration traffic corresponds to the normal traffic. This traffic reflects different services including emails, DNS, HTTP, web, few to mention. Several data preprocessing techniques were carried out including categorical encoding. Both label encoding and one hot encoding techniques were used to transform categorical feature values into numerical feature values. Hence, two types of datasets were generated.
DOI: 10.5281/zenodo.5564662
title: SSH Username Enumeration Attack Detection Dataset

description: The dataset is collected from a closed-environment network using network monitoring tools installed in the data collection point. The dataset generation was achieved through the use of common vulnerabilities and exposures (CVE) with the identification number CVE-2018-15473 retrieved from the public exploits database and pcap file of normal traffic obtained from public training repository. A total of 36,273 instances were collected with two classes <em>“username enumeration attack”</em> and “<em>non-username enumeration</em>”. We chose the terms <em>“username enumeration attack”</em> and “<em>non-username enumeration</em>” instead of the traditional <em>“attack”</em> and <em>“normal”</em> label notations since <em>“</em>normal<em>”</em> traffic data could contain attacks other than username enumeration attack. The username enumeration attack corresponds to the attack traffic while non-username enumeration traffic corresponds to the normal traffic. This traffic reflects different services including emails, DNS, HTTP, web, few to mention. Several data preprocessing techniques were carried out including categorical encoding. Both label encoding and one hot encoding techniques were used to transform categorical feature values into numerical feature values. Hence, two types of datasets were generated.
DOI: 10.5281/zenodo.5101394
title: Example MT data acquired on a Philips scanner

description: Example MT data acquired on a Philips scanner
DOI: 10.5281/zenodo.5094638
title: Example MT data acquired on a Philips scanner

description: Example MT data acquired on a Philips scanner
DOI: 10.3886/e100521v2-98660
title: American Community Survey 2011-2015 ACS 5-Year PUMS File

description: The Public Use Microdata Sample (PUMS) contains a sample of actual responses to the American Community Survey (ACS). The PUMS dataset includes variables for nearly every question on the survey, as well as many new variables that were derived after the fact from multiple survey responses (such as poverty status). Each record in the file represents a single person, or--in the household-level dataset--a single housing unit. In the person-level file, individuals are organized into households, making possible the study of people within the contexts of their families and other household members. PUMS files for an individual year, such as 2015, contain records of data from approximately one percent of the United States population. As such, PUMS files covering a five-year period, such as 2011-2015, contain records of data from approximately five percent of the United States population.<br>The PUMS files are much more flexible than the aggregate data available on American FactFinder, though the PUMS also tend to be more complicated to use. Working with PUMS data generally involves downloading large datasets onto a local computer and analyzing the data using statistical software such as R, SPSS, Stata, or SAS.<br>Since all ACS responses are strictly confidential, many variables in the PUMS file have been modified in order to protect the confidentiality of survey respondents. For instance, particularly high incomes are "top-coded," uncommon birthplace or ancestry responses are grouped into broader categories, and the PUMS file provides a very limited set of geographic variables.<br><br>NOTE: The current archive only contains the files for the entire US. The original website contains state-by-state files as well. The overall content is the same.<br>
description: All persons and housing units in the United States.
description: For additional years of ACS data, please see ICPSR's American Community Survey (ACS) Series, which is available at www.icpsr.umich.edu/icpsrweb/ICPSR/series/205 or the Census Bureau's ACS PUMS page at https://www.census.gov/programs-surveys/acs/technical-documentation/pums/about.html.
DOI: 10.5281/zenodo.5564815
title: MS2Query pre-trained datasets needed for library matching

description: The models, embeddings, sqlite file with metadata and classifiers identifiers needed for running MS2Query (https://github.com/iomega/ms2query)