cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26093/cds/vizier.17720013
title: VLBI observations of 3C 279 at 230GHz

description: VizieR online Data Catalogue associated with article published in journal Astronomical Journal (AAS) with title 'Fine-scale structure of the quasar 3C 279 measured with 1.3mm very long baseline interferometry.' (bibcode: 2013ApJ...772...13L)
DOI: 10.26093/cds/vizier.19100135
title: RELICS 6&lt;~z&lt;=8 galaxies with Spitzer & HST obs.

description: VizieR online Data Catalogue associated with article published in journal Astronomical Journal (AAS) with title 'RELICS: Properties of z&gt;=5.5 galaxies inferred from Spitzer and Hubble imaging, including a candidate z~6.8 strong [OIII] emitter.' (bibcode: 2021ApJ...910..135S)
DOI: 10.5066/p9kq30uk
title: Patuxent box turtle data set 2020

description: The data set consists of capture information on individual box turtles at Patuxent Research Refuge Laurel, MD. Location, date, morphometrics, and other information.
DOI: 10.21227/axff-kc36
title: Dataset for the paper "PUF for the Commons: Enhancing Embedded Security on the OS Level"

description: This dataset is supplementary material for our paper "PUF for the Commons: Enhancing Embedded Security on the OS Level".

Security is essential for the Internet of Things (IoT). Cryptographic operations for authentication and encryption commonly rely on random input of high entropy and secure, tamper-resistant identities, which are difficult to obtain on constrained embedded devices. In this paper, we design and analyze a generic integration of physically unclonable functions (PUFs) into the IoT operating system RIOT that supports about 250 platforms. Our approach leverages uninitialized SRAM to act as the digital fingerprint for heterogeneous devices. We ground our design on an extensive study of PUF performance in the wild, which involves SRAM measurements on more than 700 IoT nodes that aged naturally in the real-world. We quantify static SRAM bias, as well as the aging effects of devices and incorporate the results in our system. This work closes a previously identified gap of missing statistically significant sample sizes for testing the unpredictability of PUFs. Our experiments on COTS devices of 64 kB SRAM indicate that secure random seeds derived from the SRAM PUF provide 256 Bits-, and device unique keys provide more than 128 Bits of security. In a practical security assessment we show that SRAM PUFs resist moderate attack scenarios, which greatly improves the security of low-end IoT devices.
DOI: 10.15468/dl.sk86py
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query: { "TaxonKey" : [ "is Scirpus dichromenoides C.B.Clarke" ] } The dataset includes 0 records from 0 constituent datasets:
DOI: 10.25345/c51g0j510
title: MassIVE MSV000092547 - SaurabhPandey_WeiLu_NINDS_8LFQ_NL2
title: NL2 plays an important role in the inhibitory synapse development. To find the binding partners of the NL2 in the brain. Anti-NL2 antibody was used to pull down the NL2 from brain lysate. Samples were used to do mass spectrometry-based label-free quantitation analysis. NL2 IP samples were compared with control IgG samples.
DOI: 10.7910/dvn/9rlu6r
title: Replication Data for: Sanctuaries or Battlegrounds - State Penetration in Places of Worship, University Campuses, and State Bureaucracy for Pro-Government Mobilization Evidence from Iran (2015-2019)

description: Replication data for: Sanctuaries or Battlegrounds - State Penetration in Places of Worship, University Campuses, and State Bureaucracy for Pro-Government Mobilization Evidence from Iran (2015-2019), Comparative Political Studies
DOI: 10.5281/zenodo.8114649
title: Deep model predictive control of gene expression in thousands of single cells

description: Experimental data, training datasets, and trained models for our study on deep model predictive control of gene expression in bacteria. This data can be used to reproduce our results and figures. See our preprint here: biorxiv.org/content/10.1101/2022.10.28.514305 And our code repository here: gitlab.com/dunloplab/deepcellcontrol <strong>Contents:</strong> <em>datasets.zip</em>: Formatted experimental data used to train and validate fluorescence forecasting models. <em>experiments.zip:</em> Processed data for all control experiments. <em>misc.zip</em>: Files necessary to reproduce some figures or to exactly reproduce some of our results. <em>models.zip</em>: Trained neural network models and associated files.
DOI: 10.5281/zenodo.8114648
title: Deep model predictive control of gene expression in thousands of single cells

description: Experimental data, training datasets, and trained models for our study on deep model predictive control of gene expression in bacteria. This data can be used to reproduce our results and figures. See our preprint here: biorxiv.org/content/10.1101/2022.10.28.514305 And our code repository here: gitlab.com/dunloplab/deepcellcontrol <strong>Contents:</strong> <em>datasets.zip</em>: Formatted experimental data used to train and validate fluorescence forecasting models. <em>experiments.zip:</em> Processed data for all control experiments. <em>misc.zip</em>: Files necessary to reproduce some figures or to exactly reproduce some of our results. <em>models.zip</em>: Trained neural network models and associated files.
DOI: 10.5281/zenodo.7866646
title: Bicycle Tyre Data - Lateral Characteristics

description: Measurement of lateral characteristics of bicycle tyres performed through indoor test-rig VeTyT (Department of Mechanical Engineering, Politecnico di Milano, Milan IT). The test-rig for bicycle tyres complies with the standard ISO 9001-2015. Specifically, we measured lateral force and self-aligning torque varying vertical load, inflation pressure, camber angles, for a batch of bicycle tyres. <strong>Authors’ disclaimer</strong> <br> The data produced in this paper are not -and cannot be- related in any way on the quality of the products that have been tested. Actually, only one single tyre per type has been tested and the data have been acquired in a laboratory, which is not the real environment. Relevant factors defining the quality of tyres are not -and cannot be- addressed in this paper.
description: {"references": ["Dell'Orto, G., Ballo, F. M., Mastinu, G. (2022). Experimental methods to measure the lateral characteristics of bicycle tyres \u2013 a review. Vehicle System Dynamics, 1\u201323. \tdoi.org/10.1080/00423114.2022.214488", "Dell'Orto, G., Ballo, F. M., Mastinu, G., Gobbi, M. (2022). Bicycle tyres \u2013 development of a new test-rig to measure mechanical characteristics. Measurement, 202, 111813. \tdoi.org/10.1016/j.measurement.2022.111813"]}
DOI: 10.5281/zenodo.7897198
title: Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) I: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for H2O

description: We present all of the data across our SNR and abundance study for the molecule H2O for an exoEarth twin. The wavelength range is from 0.515-1 micron, with 25 evenly spaced 20% bandpasses in this range. The SNR ranges from 3-16, and the abundance values range from log10(VMR) = -3.5 to -1.5 in steps of 0.5 and 0.25 (all presented in VMR in the associated table). We present the lower and upper wavelength per bandpass, the input H2O value (abundance case), the retrieved H2O value (presented as the log10(VMR)), the lower and upper limits of the 68% credible region (presented as the log10(VMR)), and the log-Bayes factor for H2O. For more information about how these were calculated, please see Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) I: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for H2O, accepted and currently available on arXiv. To open this csv as a Pandas dataframe, use the following command: your_dataframe_name = pd.read_csv(f'zenodo_table.csv', dtype={'Input H2O': str})
DOI: 10.6084/m9.figshare.23796387.v1
title: Datos y código R para paper "<b>Propuesta metodológica de modelación estadística para el rendimiento y contenido de carbono de biomasas agroindustriales tratadas mediante carbonización hidrotermal.</b>"

description: Aquí se incorporan los datos y el código de R para la modelación del contenido de carbono y el rendimiento del proceso respecto a factores que pueden influir en el proceso.
DOI: 10.5281/zenodo.8189450
title: OpenAlex Authors and Affiliations, V2

description: This is the old, deprecated author data for OpenAlex. In July, 2023, the OpenAlex dataset switched to a new author name disambiguation (V3), and deprecated all old author IDs. This is a data dump of those old author IDs. - Author metadata are included in `authors/**/*.gz` files. - Affiliations---mapping of OpenAlex work IDs to (old) Author IDs---are in the `affiliations_export_20230719T1259/*.gz` files. The files are all gzipped JSON-lines files. See the OpenAlex documentation for more information about OpenAlex and how you can use these files.
DOI: 10.5281/zenodo.8189449
title: OpenAlex Authors and Affiliations, V2

description: This is the old, deprecated author data for OpenAlex. In July, 2023, the OpenAlex dataset switched to a new author name disambiguation (V3), and deprecated all old author IDs. This is a data dump of those old author IDs. - Author metadata are included in `authors/**/*.gz` files. - Affiliations---mapping of OpenAlex work IDs to (old) Author IDs---are in the `affiliations_export_20230719T1259/*.gz` files. The files are all gzipped JSON-lines files. See the OpenAlex documentation for more information about OpenAlex and how you can use these files.
DOI: 10.6084/m9.figshare.23796387
title: Datos y código R para paper "<b>Propuesta metodológica de modelación estadística para el rendimiento y contenido de carbono de biomasas agroindustriales tratadas mediante carbonización hidrotermal.</b>"

description: Aquí se incorporan los datos y el código de R para la modelación del contenido de carbono y el rendimiento del proceso respecto a factores que pueden influir en el proceso.
DOI: 10.5281/zenodo.7806918
title: Data required to make figures showing asymmetric seafloor depth across the Juan de Fuca plate and its cause.

description: Scripts for making figure can be found here: 10.5281/zenodo.7806914
DOI: 10.5281/zenodo.7806917
title: Data required to make figures showing asymmetric seafloor depth across the Juan de Fuca plate and its cause.

description: Scripts for making figure can be found here: 10.5281/zenodo.7806913
DOI: 10.5281/zenodo.8161750
title: Data required to make figures showing asymmetric seafloor depth across the Juan de Fuca plate and its cause.

description: Scripts for making figure can be found here: 10.5281/zenodo.7806913
DOI: 10.5281/zenodo.8190811
title: Abundance, Biovolume, and Biomass of Synechococcus and Eukaryote Pico- and Nano- Plankton From Continuous Underway Flow Cytometry During NES-LTER Transect Cruises, Ongoing Since 2018

description: "These data represent the abundance, biovolume, and biomass of prokaryotic and eukaryotic picoplankton and nanoplankton sampled continuously underway during Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) Transect cruises, ongoing since 2018. Samples were obtained with an Attune NxT Flow Cytometer sampling at approximately 2-min intervals from the underway science seawater. Cells were identified and enumerated from the flow cytometry data files based on their scattering, phycoerythrin (575 nm) and chlorophyll (680 nm) fluorescence signals." Time is in UTC. https://portal.edirepository.org/nis/metadataviewer?packageid=knb-lter-nes.17.1
DOI: 10.5281/zenodo.8190809
title: Evaluation of reconstructed multi-decadal lake levels using ICESat, ICESat-2, and Landsat satellites

description: Overview: This dataset contains lake hypsometry and time-varying lake levels (1992-2018) for 342 lakes worldwide with sizes ranging from 1 to 81,844 km<sup>2</sup>. The method is described in detail in Yao et al., <em>Water Resources Research</em> (in review), but a quick overview is provided here. 1. We developed a proxy-based approach to reconstruct global-scale, multi-decadal lake levels using water areas and hypsometric curves. 2. We depended on elevation measurements from laser altimeters ICESat and ICESat-2 and water areas from Landsat imagery to construct lake hypsometry. 3. We used dynamic water masks to reduce the uncertainties in water levels derived from ICESat and ICESat-2. 4. We refined water areas mapped from Landsat based on topographic constraints on lake growth and shrinkage and levels from ICESat and ICESat-2. 5. We validated the method on 342 lakes against observed long-term water levels from in-situ gauges or satellite radar altimeters. This dataset includes: 1. Lake hypsometry from Landsat, ICESat, and ICESat-2 satellites 2. Reconstructed water levels using Landsat-derived water areas and hypsometric curves 3. Reference level data
DOI: 10.5281/zenodo.8190810
title: Abundance, Biovolume, and Biomass of Synechococcus and Eukaryote Pico- and Nano- Plankton From Continuous Underway Flow Cytometry During NES-LTER Transect Cruises, Ongoing Since 2018

description: "These data represent the abundance, biovolume, and biomass of prokaryotic and eukaryotic picoplankton and nanoplankton sampled continuously underway during Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) Transect cruises, ongoing since 2018. Samples were obtained with an Attune NxT Flow Cytometer sampling at approximately 2-min intervals from the underway science seawater. Cells were identified and enumerated from the flow cytometry data files based on their scattering, phycoerythrin (575 nm) and chlorophyll (680 nm) fluorescence signals." Time is in UTC. https://portal.edirepository.org/nis/metadataviewer?packageid=knb-lter-nes.17.1
DOI: 10.5281/zenodo.8190808
title: Evaluation of reconstructed multi-decadal lake levels using ICESat, ICESat-2, and Landsat satellites

description: Overview: This dataset contains lake hypsometry and time-varying lake levels (1992-2018) for 342 lakes worldwide with sizes ranging from 1 to 81,844 km<sup>2</sup>. The method is described in detail in Yao et al., <em>Water Resources Research</em> (in review), but a quick overview is provided here. 1. We developed a proxy-based approach to reconstruct global-scale, multi-decadal lake levels using water areas and hypsometric curves. 2. We depended on elevation measurements from laser altimeters ICESat and ICESat-2 and water areas from Landsat imagery to construct lake hypsometry. 3. We used dynamic water masks to reduce the uncertainties in water levels derived from ICESat and ICESat-2. 4. We refined water areas mapped from Landsat based on topographic constraints on lake growth and shrinkage and levels from ICESat and ICESat-2. 5. We validated the method on 342 lakes against observed long-term water levels from in-situ gauges or satellite radar altimeters. This dataset includes: 1. Lake hypsometry from Landsat, ICESat, and ICESat-2 satellites 2. Reconstructed water levels using Landsat-derived water areas and hypsometric curves 3. Reference level data
DOI: 10.5281/zenodo.8186417
title: Disruption of an ant-plant mutualism shapes interactions between lions and their primary prey

description: <strong>Data and file overview:</strong> Kamaru_Path_Analysis_Data.csv Kamaru_Path_Analysis.R Kamaru_Zebra_RSF_Data.csv Kamaru_Zebra_RSF.R <strong>Layers used to build Zebra RSF:</strong> Kamaru_DWater: distance to water Kamaru_DGlade: distance to glade Kamaru_DSettlement: distance to human settlement Kamaru_OPC_Veg: vegetation layer (classes: <em>V. drepanolobium</em>, <em>E. divinorum, </em>others) <strong>SPECIFIC INFORMATION FOR: Kamaru_Path_Analysis_Data.csv</strong> Number of variables: 11 Description: This data file includes 105 zebra kill sites and paired random locations from June 2019 to August 2020. It also includes: (A) monthly utilization distributions of lion prides associated with each kill site and paired point; and (B) zebra densities estimated from resource selection functions, associated with each kill site, and paired random location. Please see our supplementary materials for more details on data and methods. Variable list: (A) rsf.block: Resource Selection Function blocks (block 1: Jan-Apr 2019, block 2: May-Sep 2019, block 3: Oct 2019 – Jan 2020, block 4: Feb-May 2020, block 5: Jun-Sep 2020) (B) Kill_ID: kill identifier. (C) Lion_ID: individual lion pride identifier. (D) Date (Day, Month, Year) when a specific kill occurred. (E) Zebra_kill (1 = kill site, 0 = paired random location). (F). Species: Zebra. (G) Visibility: openness measurement using a rangefinder in (m). (H) Lion_activity: Utilization distributions (UD) of lions. (I) Invasion (1 = invaded by big-headed ants, 0 = uninvaded by big-headed ants). (J) zeb.rsf: resource selection function value. (K) zeb.density: zebra density estimated from resource selection functions. <strong>SPECIFIC INFORMATION FOR: Kamaru_Zebra_RSF_Data.csv</strong> Number of variables: 10 Description: This data file includes 182 zebra sightings, paired with 10 random points created for each sighting/used point. Also, the data includes actual GPS locations of each sighting and the total number of zebras in each sighting. Please see our supplementary materials for more details on data and methods. Variable list: (A) Species: Zebra. (B) Date (Day, Month, Year) for that sighting. (C) Survey: count identifier (Survey 2 to 21). (D) GPS location (X and Y), longitude and latitude of that sighting location. (E) Transect: Transect number. (F) Used: (1= zebra sighting, 0 = paired point). (G) zebra.ct: total number of zebras in each sighting. <strong>R CODE</strong> <strong>SPECIFIC INFORMATION FOR: Kamaru_Path_Analysis.R</strong> Description: Apply this code to Kamaru_Path_Analysis_Data.csv to build nested path models. <strong>SPECIFIC INFORMATION FOR: Kamaru_Zebra_RSF.R</strong> Description: Apply this code to Kamaru_Zebra_RSF_Data.csv to build resource selection functions for zebra. Use the following layers: Kamaru_DWater, Kamaru_DGlade, Kamaru_DSettlement and Kamaru_OPC_Veg to build the Zebra RSF.
DOI: 10.5281/zenodo.8190744
title: Supplementary Datasets for: 'A processing and analytics system for microscopy data workflows: the Pycroscopy ecosystem of packages'

description: The repository contains four independent datasets that are a part of the publication (arXiv:2302.14629), which delineates the capabilities of the Pycroscopy ecosystem of packages. The details of the individual datasets can be found below. 1) bfo_iv_final.hf5: Dataset of I-V curves captured by conductive atomic force microscopy on a BiFeO3 sample. The data has been transformed so that we plot not the log of the current density (J) as a function of the square root of the electric field. The dataset was originally presented in the paper 10.1038/s41467-017-01334-5 2) bto_atomic.dm3: Atomically resolved data BaTiO3 thin film acquired with scanning transmission electron microscopy. These were originally captured in the dm3 file format. This dataset was a part of the publication: doi.org/10.1002/adma.202106426 3) EELS_STO.dm3: Scanning transmission electron microscope (STEM)-Electron energy loss spectroscopy (EELS) dataset of SrTiO3. 4) STO-stack.h5: High-angle annular dark-field imaging (HAADF) scanning transmission electron microscope (STEM) image stack of SrTiO3. This image stack contains 25 images.
DOI: 10.5281/zenodo.8186416
title: Disruption of an ant-plant mutualism shapes interactions between lions and their primary prey

description: <strong>Data and file overview:</strong> Kamaru_Path_Analysis_Data.csv Kamaru_Path_Analysis.R Kamaru_Zebra_RSF_Data.csv Kamaru_Zebra_RSF.R <strong>Layers used to build Zebra RSF:</strong> Kamaru_DWater: distance to water Kamaru_DGlade: distance to glade Kamaru_DSettlement: distance to human settlement Kamaru_OPC_Veg: vegetation layer (classes: <em>V. drepanolobium</em>, <em>E. divinorum, </em>others) <strong>SPECIFIC INFORMATION FOR: Kamaru_Path_Analysis_Data.csv</strong> Number of variables: 11 Description: This data file includes 105 zebra kill sites and paired random locations from June 2019 to August 2020. It also includes: (A) monthly utilization distributions of lion prides associated with each kill site and paired point; and (B) zebra densities estimated from resource selection functions, associated with each kill site, and paired random location. Please see our supplementary materials for more details on data and methods. Variable list: (A) rsf.block: Resource Selection Function blocks (block 1: Jan-Apr 2019, block 2: May-Sep 2019, block 3: Oct 2019 – Jan 2020, block 4: Feb-May 2020, block 5: Jun-Sep 2020) (B) Kill_ID: kill identifier. (C) Lion_ID: individual lion pride identifier. (D) Date (Day, Month, Year) when a specific kill occurred. (E) Zebra_kill (1 = kill site, 0 = paired random location). (F). Species: Zebra. (G) Visibility: openness measurement using a rangefinder in (m). (H) Lion_activity: Utilization distributions (UD) of lions. (I) Invasion (1 = invaded by big-headed ants, 0 = uninvaded by big-headed ants). (J) zeb.rsf: resource selection function value. (K) zeb.density: zebra density estimated from resource selection functions. <strong>SPECIFIC INFORMATION FOR: Kamaru_Zebra_RSF_Data.csv</strong> Number of variables: 10 Description: This data file includes 182 zebra sightings, paired with 10 random points created for each sighting/used point. Also, the data includes actual GPS locations of each sighting and the total number of zebras in each sighting. Please see our supplementary materials for more details on data and methods. Variable list: (A) Species: Zebra. (B) Date (Day, Month, Year) for that sighting. (C) Survey: count identifier (Survey 2 to 21). (D) GPS location (X and Y), longitude and latitude of that sighting location. (E) Transect: Transect number. (F) Used: (1= zebra sighting, 0 = paired point). (G) zebra.ct: total number of zebras in each sighting. <strong>R CODE</strong> <strong>SPECIFIC INFORMATION FOR: Kamaru_Path_Analysis.R</strong> Description: Apply this code to Kamaru_Path_Analysis_Data.csv to build nested path models. <strong>SPECIFIC INFORMATION FOR: Kamaru_Zebra_RSF.R</strong> Description: Apply this code to Kamaru_Zebra_RSF_Data.csv to build resource selection functions for zebra. Use the following layers: Kamaru_DWater, Kamaru_DGlade, Kamaru_DSettlement and Kamaru_OPC_Veg to build the Zebra RSF.
DOI: 10.5281/zenodo.8190743
title: Supplementary Datasets for: 'A processing and analytics system for microscopy data workflows: the Pycroscopy ecosystem of packages'

description: The repository contains four independent datasets that are a part of the publication (arXiv:2302.14629), which delineates the capabilities of the Pycroscopy ecosystem of packages. The details of the individual datasets can be found below. 1) bfo_iv_final.hf5: Dataset of I-V curves captured by conductive atomic force microscopy on a BiFeO3 sample. The data has been transformed so that we plot not the log of the current density (J) as a function of the square root of the electric field. The dataset was originally presented in the paper 10.1038/s41467-017-01334-5 2) bto_atomic.dm3: Atomically resolved data BaTiO3 thin film acquired with scanning transmission electron microscopy. These were originally captured in the dm3 file format. This dataset was a part of the publication: doi.org/10.1002/adma.202106426 3) EELS_STO.dm3: Scanning transmission electron microscope (STEM)-Electron energy loss spectroscopy (EELS) dataset of SrTiO3. 4) STO-stack.h5: High-angle annular dark-field imaging (HAADF) scanning transmission electron microscope (STEM) image stack of SrTiO3. This image stack contains 25 images.
DOI: 10.5281/zenodo.8190899
title: melanodrymia_brightae_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.8190898
title: melanodrymia_brightae_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.8168551
title: M3-Seq imaging data set (Wang, et. al 2023)

description: Raw image files for M3-Seq paper.
DOI: 10.5281/zenodo.8168550
title: M3-Seq imaging data set (Wang, et. al 2023)

description: Raw image files for M3-Seq paper.