cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10383909
title: ARCSV project output
DOI: 10.6084/m9.figshare.24572758
title: A bibliometric analysis of the recent achievements in pulmonary safety of nanoparticles

description: Assessing research activity is an important step for planning future initiatives oriented toward filling the remaining gaps in a field. Therefore, the objective of the current study was to review recently published research on pulmonary toxicity caused by nanomaterials. However, here, instead of reviewing possible toxic effects and discussing their mode of action, the goal was to establish trends considering for example examined so far nanomaterials or used testing strategies. A total of 2316 related articles retrieved from the three most cited databases (PubMed Scopus, Web of Science), selected based on the title and abstract requirements, were used as the source of the review. Based on the bibliometric analysis, the nano-meter metal oxides, and carbon-based nanotubes were identified as the most frequently studied nanomaterials, while quantum dots, which might induce possible harmful effects, were not considered so far. The majority of testing of pulmonary safety is based on <i>in vitro</i> studies with observed growth of the contribution of novel testing strategies, such as 3D lung model, air-liquid interface system, or omic analysis.
DOI: 10.6084/m9.figshare.24572758.v1
title: A bibliometric analysis of the recent achievements in pulmonary safety of nanoparticles

description: Assessing research activity is an important step for planning future initiatives oriented toward filling the remaining gaps in a field. Therefore, the objective of the current study was to review recently published research on pulmonary toxicity caused by nanomaterials. However, here, instead of reviewing possible toxic effects and discussing their mode of action, the goal was to establish trends considering for example examined so far nanomaterials or used testing strategies. A total of 2316 related articles retrieved from the three most cited databases (PubMed Scopus, Web of Science), selected based on the title and abstract requirements, were used as the source of the review. Based on the bibliometric analysis, the nano-meter metal oxides, and carbon-based nanotubes were identified as the most frequently studied nanomaterials, while quantum dots, which might induce possible harmful effects, were not considered so far. The majority of testing of pulmonary safety is based on <i>in vitro</i> studies with observed growth of the contribution of novel testing strategies, such as 3D lung model, air-liquid interface system, or omic analysis.
DOI: 10.6084/m9.figshare.24328745
title: Data Fission: Splitting a Single Data Point

description: Suppose we observe a random vector <i>X</i> from some distribution in a known family with unknown parameters. We ask the following question: when is it possible to split <i>X</i> into two pieces <i>f</i>(<i>X</i>) and <i>g</i>(<i>X</i>) such that neither part is sufficient to reconstruct X by itself, but both together can recover X fully, and their joint distribution is tractable? One common solution to this problem when multiple samples of X are observed is data splitting, but Rasines and Young offers an alternative approach that uses additive Gaussian noise—this enables post-selection inference in finite samples for Gaussian distributed data and asymptotically when errors are non-Gaussian. In this article, we offer a more general methodology for achieving such a split in finite samples by borrowing ideas from Bayesian inference to yield a (frequentist) solution that can be viewed as a continuous analog of data splitting. We call our method data fission, as an alternative to data splitting, data carving and <i>p</i>-value masking. We exemplify the method on several prototypical applications, such as post-selection inference for trend filtering and other regression problems, and effect size estimation after interactive multiple testing. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24328745.v2
title: Data Fission: Splitting a Single Data Point

description: Suppose we observe a random vector <i>X</i> from some distribution in a known family with unknown parameters. We ask the following question: when is it possible to split <i>X</i> into two pieces <i>f</i>(<i>X</i>) and <i>g</i>(<i>X</i>) such that neither part is sufficient to reconstruct X by itself, but both together can recover X fully, and their joint distribution is tractable? One common solution to this problem when multiple samples of X are observed is data splitting, but Rasines and Young offers an alternative approach that uses additive Gaussian noise—this enables post-selection inference in finite samples for Gaussian distributed data and asymptotically when errors are non-Gaussian. In this article, we offer a more general methodology for achieving such a split in finite samples by borrowing ideas from Bayesian inference to yield a (frequentist) solution that can be viewed as a continuous analog of data splitting. We call our method data fission, as an alternative to data splitting, data carving and <i>p</i>-value masking. We exemplify the method on several prototypical applications, such as post-selection inference for trend filtering and other regression problems, and effect size estimation after interactive multiple testing. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24328745.v1
title: Data fission: splitting a single data point

description: Suppose we observe a random vector <i>X</i> from some distribution in a known family with unknown parameters. We ask the following question: when is it possible to split <i>X</i> into two pieces <i>f</i>(<i>X</i>) and <i>g</i>(<i>X</i>) such that neither part is sufficient to reconstruct X by itself, but both together can recover X fully, and their joint distribution is tractable? One common solution to this problem when multiple samples of X are observed is data splitting, but Rasines and Young (2022) offers an alternative approach that uses additive Gaussian noise — this enables post-selection inference in finite samples for Gaussian distributed data and asymptotically for non-Gaussian additive models. In this paper, we offer a more general methodology for achieving such a split in finite samples by borrowing ideas from Bayesian inference to yield a (frequentist) solution that can be viewed as a continuous analog of data splitting. We call our method data fission, as an alternative to data splitting, data carving and p-value masking. We exemplify the method on several prototypical applications, such as post-selection inference for trend filtering and other regression problems, and effect size estimation after interactive multiple testing.
DOI: 10.6084/m9.figshare.24808563
title: Diretrizes para o Apoio ao Planejamento de Atividades Gamificadas

description: <b>Acess: gamificador.vercel.app</b><br>
DOI: 10.6084/m9.figshare.24808563.v1
title: Diretrizes para o Apoio ao Planejamento de Atividades Gamificadas

description: <b>ACESS: gamificador.vercel.app</b>
DOI: 10.25380/iastate.14394455.v1
title: Physical, Chemical and Biological Data from the Grosses Heiliges Meer, Germany

description: Dataset comprised of chemical
and biological data collected from a single site in Grosses Heiliges Meer,
Germany from September 2014 through September 2018. <br>
DOI: 10.25380/iastate.14394455.v2
title: Physical, Chemical and Biological Data from the Grosses Heiliges Meer, Germany

description: Dataset comprised of chemical and biological data collected from a single site in Grosses Heiliges Meer, Germany from September 2014 through September 2018.Change log: paper publication added December 2023. <br>
DOI: 10.25380/iastate.14394455
title: Physical, Chemical and Biological Data from the Grosses Heiliges Meer, Germany

description: Dataset comprised of chemical and biological data collected from a single site in Grosses Heiliges Meer, Germany from September 2014 through September 2018.Change log: paper publication added December 2023. <br>
DOI: 10.6084/m9.figshare.24812055
title: 8482-P3-00-01_S1_L001_R2_001.fastq.gz

description: Passive eDNA sampling onboard Antarctic research vessels
DOI: 10.6084/m9.figshare.24808572.v1
title: Qualitative Data File

description: This dataset includes a list of text segments from focus group participants, related to the manuscript's research. The text segments are sorted into categories (i.e., uses, weaknesses, and recommendations) and sub-categories. The data is de-indentified. All personably-identifiable information was redacted or removed.
DOI: 10.6084/m9.figshare.24812052
title: 8482-P3-00-01_S1_L001_R1_001.fastq.gz

description: Passive eDNA sampling onboard Antarctic research vessels
DOI: 10.6084/m9.figshare.24812052.v1
title: 8482-P3-00-01_S1_L001_R1_001.fastq.gz

description: Passive eDNA sampling onboard Antarctic research vessels
DOI: 10.6084/m9.figshare.24812064
title: metadata_coi_8482P3.fasta

description: Passive eDNA sampling onboard Antarctic research vessels
DOI: 10.6084/m9.figshare.24812064.v1
title: metadata_coi_8482P3.fasta

description: Passive eDNA sampling onboard Antarctic research vessels
DOI: 10.6084/m9.figshare.24808335
title: J_dense_0-150.npy

description: J Matrix for Wigner Rotations.
DOI: 10.6084/m9.figshare.24808335.v2
title: J_dense_0-150.npy

description: J Matrix for Wigner Rotations.
DOI: 10.6084/m9.figshare.24808335.v1
title: J_dense_0-150.npy

description: J Matrix for Wigner Rotations.
DOI: 10.6084/m9.figshare.24808572
title: Qualitative Data File

description: This dataset includes a list of text segments from focus group participants, related to the manuscript's research. The text segments are sorted into categories (i.e., uses, weaknesses, and recommendations) and sub-categories. The data is de-indentified. All personably-identifiable information was redacted or removed.
DOI: 10.3886/icpsr38402.v1
title: Survey of Consumer Attitudes and Behavior, July 2019
title: Version 1

description: The Survey of Consumer Attitudes and Behavior series (also known as the Surveys of Consumers) was undertaken to measure changes in consumer attitudes and expectations, to understand why such changes occur, and to evaluate how they relate to consumer decisions to save, borrow, or make discretionary purchases. The data regularly include the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations. Since the 1940s, these surveys have been produced quarterly through 1977 and monthly thereafter.
The surveys conducted in 2019 focused on topics such as evaluations and expectations about personal finances, employment, price changes, and the national business situation. Opinions were collected regarding respondents' appraisals of present market conditions for purchasing houses, automobiles, computers, and other durables. Also explored in this survey, were respondents' types of savings and financial investments, loan use, family income, and retirement planning.
Other topics explored in this survey were respondents' economic attitudes, home price expectations, savings and financial investments, political affiliation and opinion about the Trump administration. Demographic information includes ethnic origin, sex, age, marital status, and education.
description: The purpose of this survey series is to forecast changes in aggregate consumer behavior.
description: This survey is a rotating panel design. For each monthly sample, an independent cross-section sample of households is drawn. The respondents chosen in this drawing are then reinterviewed six months later. The result is a rotating panel design, and the total sample for any one survey is normally made up of 60 percent new respondents with 40 percent being interviewed for the second time.
description: The data are not weighted, however, this collection contains three weight variables; <b>WT</b> (Household weight (WT_HH renamed)), <b>WT_HH</b> (Household weight), and <b>WT_AD</b> (Adult weight) that must be used in any analysis. For more information on weights and sampling, please refer to the documentation and/or visit the Survey of Consumers Website.
description: National sample of telephone numbers from cell (RDD) sampling frame.
description: Datasets: 
DS0: Study-Level Files
DS1: Public-Use Data
DS2: Restricted-Use Data
description: Persons aged 18 years or older living in households with telephones in the coterminous United States. Smallest Geographic Unit: FIPS code
description: computer-assisted telephone interview (CATI)Information on the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations and how they were created can be found in the P.I. Codebook.Additional information on the Surveys of Consumers can be found by visiting the Survey of Consumers Website.
DOI: 10.3886/icpsr38402
title: Survey of Consumer Attitudes and Behavior, July 2019
title: Archival Version

description: The Survey of Consumer Attitudes and Behavior series (also known as the Surveys of Consumers) was undertaken to measure changes in consumer attitudes and expectations, to understand why such changes occur, and to evaluate how they relate to consumer decisions to save, borrow, or make discretionary purchases. The data regularly include the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations. Since the 1940s, these surveys have been produced quarterly through 1977 and monthly thereafter.
The surveys conducted in 2019 focused on topics such as evaluations and expectations about personal finances, employment, price changes, and the national business situation. Opinions were collected regarding respondents' appraisals of present market conditions for purchasing houses, automobiles, computers, and other durables. Also explored in this survey, were respondents' types of savings and financial investments, loan use, family income, and retirement planning.
Other topics explored in this survey were respondents' economic attitudes, home price expectations, savings and financial investments, political affiliation and opinion about the Trump administration. Demographic information includes ethnic origin, sex, age, marital status, and education.
description: The purpose of this survey series is to forecast changes in aggregate consumer behavior.
description: This survey is a rotating panel design. For each monthly sample, an independent cross-section sample of households is drawn. The respondents chosen in this drawing are then reinterviewed six months later. The result is a rotating panel design, and the total sample for any one survey is normally made up of 60 percent new respondents with 40 percent being interviewed for the second time.
description: The data are not weighted, however, this collection contains three weight variables; <b>WT</b> (Household weight (WT_HH renamed)), <b>WT_HH</b> (Household weight), and <b>WT_AD</b> (Adult weight) that must be used in any analysis. For more information on weights and sampling, please refer to the documentation and/or visit the Survey of Consumers Website.
description: National sample of telephone numbers from cell (RDD) sampling frame.
description: Datasets: 
DS0: Study-Level Files
DS1: Public-Use Data
DS2: Restricted-Use Data
description: Persons aged 18 years or older living in households with telephones in the coterminous United States. Smallest Geographic Unit: FIPS code
description: computer-assisted telephone interview (CATI)Information on the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations and how they were created can be found in the P.I. Codebook.Additional information on the Surveys of Consumers can be found by visiting the Survey of Consumers Website.
DOI: 10.5281/zenodo.10205099
title: BST/NOAA PSL Level 2 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH

description: This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA).  These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023.  Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies LLC.  
 
Each zip file contains a set of four Level 2 NetCDF files which provides the highest spatial resolution available for each of four products for a given flight location.  With the Level 2 data, each flight location and variable can have different spatial resolutions depending on the sensor type, retrieval algorithm, and flight altitude.  The file name convention for the zip files is as follows.
 
uas_L2_yyyymmdd_hhmmss.zip 
where
L2 = Level 2 data 
yyyymmdd = year,month,day
hhmmss = hour,minute,second
Time is the flight start time in UTC.
 
The NetCDF file format contained in the zip files has a similar format to the zip files with convention
 
uas_<var>_L2_yyyymmdd_hhmmss.nc 
where
<var> = vsm, dem, ndvi, or stmp
vsm = volumetric soil moisture
dem = digital elevation
ndvi = normalized difference vegetation index
stmp = surface temperature
 
Note that each flight location using the E2 aerial platform required two flights so starting flight times for the soil moisture NetCDF files are different from the other three products.
November 2023 update: Version 2.0 added which has flight data from 2022 and 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data and a mask was applied to the soil moisture retrieval that masks data over water surfaces.
description: Funding provided by the NOAA Weather Program Office with award number NA21OAR4590363.
DOI: 10.5281/zenodo.10380444
title: BST/NOAA PSL Level 2 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH

description: This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA).  These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023.  Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies LLC.  

 

Each zip file contains a set of four Level 2 NetCDF files which provides the highest spatial resolution available for each of four products for a given flight location.  With the Level 2 data, each flight location and variable can have different spatial resolutions depending on the sensor type, retrieval algorithm, and flight altitude.  The file name convention for the zip files is as follows.

 

uas_L2_yyyymmdd_hhmmss_vX.X.zip 

where

L2 = Level 2 data 

yyyymmdd = year,month,day

hhmmss = hour,minute,second

vX.X = version number

Time is the flight start time in UTC.

 

The NetCDF file format contained in the zip files has a similar format to the zip files with convention

 

uas_<var>_L2_yyyymmdd_hhmmss.nc 

where

<var> = vsm, dem, ndvi, or stmp

vsm = volumetric soil moisture

dem = digital elevation

ndvi = normalized difference vegetation index

stmp = surface temperature

 

Note that each flight location using the E2 aerial platform required two flights so starting flight times for the soil moisture NetCDF files are different from the other three products.

November 2023 update: Version 2.0 added flight data from 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data, and a mask was applied to the soil moisture retrieval over water surfaces for both 2022 and 2023 data.

December 2023 update: Version 2.1 updated soil moisture data with a wet bias in v2.0 for flights #2 (17:40:35 UTC) and #3 (19:24:45 UTC) on July 27, 2022.
description: Funding provided by the NOAA Weather Program Office with award number NA21OAR4590363.
DOI: 10.5281/zenodo.7622354
title: BST/NOAA PSL Level 2 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH

description: This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA).  These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023.  Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies LLC.  

 

Each zip file contains a set of four Level 2 NetCDF files which provides the highest spatial resolution available for each of four products for a given flight location.  With the Level 2 data, each flight location and variable can have different spatial resolutions depending on the sensor type, retrieval algorithm, and flight altitude.  The file name convention for the zip files is as follows.

 

uas_L2_yyyymmdd_hhmmss_vX.X.zip 

where

L2 = Level 2 data 

yyyymmdd = year,month,day

hhmmss = hour,minute,second

vX.X = version number

Time is the flight start time in UTC.

 

The NetCDF file format contained in the zip files has a similar format to the zip files with convention

 

uas_<var>_L2_yyyymmdd_hhmmss.nc 

where

<var> = vsm, dem, ndvi, or stmp

vsm = volumetric soil moisture

dem = digital elevation

ndvi = normalized difference vegetation index

stmp = surface temperature

 

Note that each flight location using the E2 aerial platform required two flights so starting flight times for the soil moisture NetCDF files are different from the other three products.

November 2023 update: Version 2.0 added flight data from 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data, and a mask was applied to the soil moisture retrieval over water surfaces for both 2022 and 2023 data.

December 2023 update: Version 2.1 updated soil moisture data with a wet bias in v2.0 for flights #2 (17:40:35 UTC) and #3 (19:24:45 UTC) on July 27, 2022.
description: Funding provided by the NOAA Weather Program Office with award number NA21OAR4590363.