cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.mykpag
title: Occurrence Download

description: A dataset containing 26575 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Material sample, Material citation, Fossil, Specimen, Living Specimen, Occurrence evidence, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Aquila spilogaster (Bonaparte, 1850)"
 ]
}

 The dataset includes 26575 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025265-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y92ybu
title: Occurrence Download

description: A dataset containing 1211 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Palaemon vulgaris Say, 1818",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is Human Observation",
 "Year is greater than or equal to 2005"
 ]
}

 The dataset includes 1211 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025266-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26030/60zg-2n28
title: Exposure to elevated relative humidity in laboratory chambers alters fungal gene expression in dust from the International Space Station (ISS)

description: Microbes are present in all occupied indoor environments, including the International Space Station (ISS). An unexpected ventilation system failure may lead to microbial and fungal growth that is associated with material degradation or negative health effects. There is a need for improved understanding of fungal function when exposed to moisture in indoor spaces. A de novo metatranscriptomic study was performed using dust collected from within the ISS. After returning to Earth, dust was incubated in laboratory chambers to simulate relative humidity conditions of 50%, 85%, or 100% equilibrium relative humidity (ERH) for 1-week. Changes in fungal function (gene expression) were associated with moisture exposure. Genes associated with secondary metabolism and fungal growth were significantly upregulated (p ≤ 0.001, log2FC ≥ 2) at elevated RH conditions (85% or 100% ERH). Elevated moisture conditions showed an increased expression of mycotoxin and fungal allergen genes such as Asp f 4 and Alt a 7 (log2FC ≥ 5). These results demonstrate that understanding microbial gene expression in response to elevated moisture will help improve microbial monitoring standards for confined spacecraft environments and help protect astronaut health and spacecraft integrity.
DOI: 10.15468/dl.uutce4
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Labeo capensis (Smith, 1841)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((35.6306 -23.844,35.6007 -23.8125,35.6007 -24.0157,35.6306 -23.844))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025267-250525065834625/datasets/export for details.
DOI: 10.17632/p3d54v5s7n.1
title: Flow past a swirler grid - PIV data from wind tunnel at Re 1.3 × 10^4 and 2.7 × 10^4

description: Particle Image Velocimetry (PIV) measurement of flow past a swirler grid. 

This grid is designed to produce a regular system of clock-wise and anti-clock-wise vortices, which alternate in a same way as black and white squares on a chessboard. These longitudinal vortices are relatively stable and they play as a energy source at larger distances past the grid. Therefore, the maximum of turbulent kinetic energy (TKE) lies in the distance of ~ 10 × M (the mesh parameter). 

- File "MeasuredPlanes.png" for general overview. The PIV is in Stereo configuration (so called 2D 3C). 

- File "SwirlerGrid.scad" is the geometry (CAD), to open and modify that file, You will need open-source program OpenSCAD, https://openscad.org/. The geometry is described in more details in a function sample: https://www.kke.zcu.cz/cs/Research/function_samples (in Czech language)

- File "StereoPIV.zip" contains the measured data. They are organized in folders named e.g. "t05msX02", where "t05ms" means wind tunnel velocity of 5 m/s and "X02M" signifies the stream-wise distance in multiplies of M. Each such folder contains about 500 individual snapshots in ".csv" files, which are exported from Dantec Dynamic Studio. The data start at line no. 10. The column description is in line no. 9, velocity data are in columns 5 - 7, where 5th column (headed "U") represents the velocity in horizontal direction, i.e. y-axis in Figure "MeasuredPlanes.png"; 6th column (headed "V") is vertical z-velocity and 7th column (headed "W") is the stream-wise x-component.
DOI: 10.17632/p3d54v5s7n
title: Flow past a swirler grid - PIV data from wind tunnel at Re 1.3 × 10^4 and 2.7 × 10^4

description: Particle Image Velocimetry (PIV) measurement of flow past a swirler grid. 

This grid is designed to produce a regular system of clock-wise and anti-clock-wise vortices, which alternate in a same way as black and white squares on a chessboard. These longitudinal vortices are relatively stable and they play as a energy source at larger distances past the grid. Therefore, the maximum of turbulent kinetic energy (TKE) lies in the distance of ~ 10 × M (the mesh parameter). 

- File "MeasuredPlanes.png" for general overview. The PIV is in Stereo configuration (so called 2D 3C). 

- File "SwirlerGrid.scad" is the geometry (CAD), to open and modify that file, You will need open-source program OpenSCAD, https://openscad.org/. The geometry is described in more details in a function sample: https://www.kke.zcu.cz/cs/Research/function_samples (in Czech language)

- File "StereoPIV.zip" contains the measured data. They are organized in folders named e.g. "t05msX02", where "t05ms" means wind tunnel velocity of 5 m/s and "X02M" signifies the stream-wise distance in multiplies of M. Each such folder contains about 500 individual snapshots in ".csv" files, which are exported from Dantec Dynamic Studio. The data start at line no. 10. The column description is in line no. 9, velocity data are in columns 5 - 7, where 5th column (headed "U") represents the velocity in horizontal direction, i.e. y-axis in Figure "MeasuredPlanes.png"; 6th column (headed "V") is vertical z-velocity and 7th column (headed "W") is the stream-wise x-component.
DOI: 10.17632/p3d54v5s7n.2
title: Flow past a swirler grid - PIV data from wind tunnel at Re 1.3 × 10^4 and 2.7 × 10^4

description: Particle Image Velocimetry (PIV) measurement of flow past a swirler grid. 

This grid is designed to produce a regular system of clock-wise and anti-clock-wise vortices, which alternate in a same way as black and white squares on a chessboard. These longitudinal vortices are relatively stable and they play as a energy source at larger distances past the grid. Therefore, the maximum of turbulent kinetic energy (TKE) lies in the distance of ~ 10 × M (the mesh parameter). 

- File "MeasuredPlanes.png" for general overview. The PIV is in Stereo configuration (so called 2D 3C). 

- File "SwirlerGrid.scad" is the geometry (CAD), to open and modify that file, You will need open-source program OpenSCAD, https://openscad.org/. The geometry is described in more details in a function sample: https://www.kke.zcu.cz/cs/Research/function_samples (in Czech language)

- File "StereoPIV.zip" contains the measured data. They are organized in folders named e.g. "t05msX02", where "t05ms" means wind tunnel velocity of 5 m/s and "X02M" signifies the stream-wise distance in multiplies of M. Each such folder contains about 500 individual snapshots in ".csv" files, which are exported from Dantec Dynamic Studio. The data start at line no. 10. The column description is in line no. 9, velocity data are in columns 5 - 7, where 5th column (headed "U") represents the velocity in horizontal direction, i.e. y-axis in Figure "MeasuredPlanes.png"; 6th column (headed "V") is vertical z-velocity and 7th column (headed "W") is the stream-wise x-component.
DOI: 10.17632/cm8hg76r45.1
title: PIV data near hub of VT-400 axial turbine

description: Stereo Particle Image Velocimetry (PIV a standard method see e.g. https://link.springer.com/article/10.1007/s00348-005-0991-7) measurement inside an axial turbine VT-400 (see e.g. https://doi.org/10.1051/epjconf/201611402143). The measured area is oriented in axial × radial direction near the hub between the stator and rotor wheel. Therefore it covers the wake past stator wheel and secondary structures near the hub. Due to limited optical instrumentation, the data cover lot of surroundings, therefore a massive cut-off is needed (see the attached presentation "duda-ES2022.pptx" from conference PSE 2022). The main data are organized in the zip file "ExportedVectorFields.zip": there are 5 folders for studied regimes. Each contain several snapshots in form of csv table. It is a standard export from Dantec Dynamic Software, therefore it should be openable in TecPlot (But I have used my own codes to open, cut, filter and to calculate the mean characteristics). The overview can be found in the file "Figures.svg"; use Inkscape to open it and look around for all figures. Two attached movies show the opened turbine with laser alignment (no measuring during the movie) and a fog generator fogging into the inlet.
DOI: 10.17632/cm8hg76r45
title: PIV data near hub of VT-400 axial turbine

description: Stereo Particle Image Velocimetry (PIV a standard method see e.g. https://link.springer.com/article/10.1007/s00348-005-0991-7) measurement inside an axial turbine VT-400 (see e.g. https://doi.org/10.1051/epjconf/201611402143). The measured area is oriented in axial × radial direction near the hub between the stator and rotor wheel. Therefore it covers the wake past stator wheel and secondary structures near the hub. Due to limited optical instrumentation, the data cover lot of surroundings, therefore a massive cut-off is needed (see the attached presentation "duda-ES2022.pptx" from conference PSE 2022). The main data are organized in the zip file "ExportedVectorFields.zip": there are 5 folders for studied regimes. Each contain several snapshots in form of csv table. It is a standard export from Dantec Dynamic Software, therefore it should be openable in TecPlot (But I have used my own codes to open, cut, filter and to calculate the mean characteristics). The overview can be found in the file "Figures.svg"; use Inkscape to open it and look around for all figures. Two attached movies show the opened turbine with laser alignment (no measuring during the movie) and a fog generator fogging into the inlet.
DOI: 10.17632/cm8hg76r45.2
title: PIV data near hub of VT-400 axial turbine

description: Stereo Particle Image Velocimetry (PIV a standard method see e.g. https://link.springer.com/article/10.1007/s00348-005-0991-7) measurement inside an axial turbine VT-400 (see e.g. https://doi.org/10.1051/epjconf/201611402143). The measured area is oriented in axial × radial direction near the hub between the stator and rotor wheel. Therefore it covers the wake past stator wheel and secondary structures near the hub. Due to limited optical instrumentation, the data cover lot of surroundings, therefore a massive cut-off is needed (see the attached presentation "duda-ES2022.pptx" from conference PSE 2022). The main data are organized in the zip file "ExportedVectorFields.zip": there are 5 folders for studied regimes. Each contain several snapshots in form of csv table. It is a standard export from Dantec Dynamic Software, therefore it should be openable in TecPlot (But I have used my own codes to open, cut, filter and to calculate the mean characteristics). The overview can be found in the file "Figures.svg"; use Inkscape to open it and look around for all figures. Two attached movies show the opened turbine with laser alignment (no measuring during the movie) and a fog generator fogging into the inlet.
DOI: 10.15468/dl.3ztgex
title: Occurrence Download

description: A dataset containing 1515 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Lycaon pictus (Temminck, 1820)",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 1515 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025268-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/znkr2dm8fb.1
title: Tesorai Search

description: Processed data outputs for Tesorai Search manuscript:
https://www.biorxiv.org/content/10.1101/2024.08.19.606805v2.full
https://github.com/tesorai/tesorai_search/
DOI: 10.7294/29241962
title: Total Water Storage Change in the Lake Mead Region

description: The dataset provides estimates of Total Water Storage (TWS) change in the Lake Mead region from 2020 to 2022, as shown in Figure 6 of Khorrami et al. (under review). The estimates are derived from an inversion of InSAR-observed vertical deformation of the ground surface using a half-space elastic load model. For further details, please refer to the accompanying README file included with the downloadable data package.
DOI: 10.7294/29241962.v1
title: Total Water Storage Change in the Lake Mead Region

description: The dataset provides estimates of Total Water Storage (TWS) change in the Lake Mead region from 2020 to 2022, as shown in Figure 6 of Khorrami et al. (under review). The estimates are derived from an inversion of InSAR-observed vertical deformation of the ground surface using a half-space elastic load model. For further details, please refer to the accompanying README file included with the downloadable data package.
DOI: 10.17632/8zfbfj4t7m.1
title: Dataset of On-tube local condensation heat transfer coefficient of R134a, R513A and R450A on smooth stainless-steel tube at saturation temperature of 35 and 40℃

description: These data contain local condensation heat transfer coefficients of three refrigerants (i.e., R134a, R4513A and R450A) on a single, horizontal, smooth stainless tube at saturation temperatures of 35 and 40℃. A custom experimental apparatus was constructed to measure on-tube condensation heat transfer coefficients which consisted of two main parts, the chamber and the condensation tube. Refrigerant was vaporized in the experimental chamber and then condensed on the condensation tube. Two recirculating chillers were connected to the experimental chamber. One supplied cooling water through the condensation tube and the other supplied water used to boil the liquid refrigerant in order to achieve saturation temperatures of 35 or 40℃ . The refrigerant boiled and subsequently condensed on the smooth condensation tube. Thermocouples were installed in the flow to measure temperature at the inlet and outlet of the tube. The condensation tube was machined using electron discharge machining and 1.016-mm-diameter thermocouples(T1-T8) were installed in the tube wall in order to measure heat flux through the wall at two radial positions, β=11o from the vertical and β=109o from the vertical. Four thermocouples (T1-T4) were used to compute heat flux at β=11o, and an additional four thermocouples (T5-T8) were used to compute heat flux at β=109o.
Saturation pressures equivalent to 35 ± 0.5℃ and 40±0.5℃ were monitored in LabVIEW. Steady state was attained when the vapor pressure and temperature variation across the wall were in the range of ±0.34 kPa and ±0.05℃. After steady state was reached, experimental data were recorded for five minutes. Each experimental datapoint is the average of data collected within five minutes, and they include the eight thermocouple wall temperatures, mass flow rate of cooling water, temperature inlet and outlet of cooling water, saturation temperature and pressure of refrigerants. The outer wall temperature varied around the tube, and the measured wall temperatures were used to compute the outer wall temperatures and temperature gradients at radial positions around the tube using a natural logarithm curve fit with the measured wall temperatures as input. These values were used to compute heat fluxes and local heat transfer coefficients were computed using thermal conductivity, heat flux, saturation temperature (determined from the measured saturation pressure) and wall temperatures. Uncertainties of local heat transfer coefficients for each experimental trial were calculated using the propagation of uncertainty method and uncertainties of the instrumentation.
DOI: 10.17632/8zfbfj4t7m
title: Dataset of On-tube local condensation heat transfer coefficient of R134a, R513A and R450A on smooth stainless-steel tube at saturation temperature of 35 and 40℃

description: These data contain local condensation heat transfer coefficients of three refrigerants (i.e., R134a, R4513A and R450A) on a single, horizontal, smooth stainless tube at saturation temperatures of 35 and 40℃. A custom experimental apparatus was constructed to measure on-tube condensation heat transfer coefficients which consisted of two main parts, the chamber and the condensation tube. Refrigerant was vaporized in the experimental chamber and then condensed on the condensation tube. Two recirculating chillers were connected to the experimental chamber. One supplied cooling water through the condensation tube and the other supplied water used to boil the liquid refrigerant in order to achieve saturation temperatures of 35 or 40℃ . The refrigerant boiled and subsequently condensed on the smooth condensation tube. Thermocouples were installed in the flow to measure temperature at the inlet and outlet of the tube. The condensation tube was machined using electron discharge machining and 1.016-mm-diameter thermocouples(T1-T8) were installed in the tube wall in order to measure heat flux through the wall at two radial positions, β=11o from the vertical and β=109o from the vertical. Four thermocouples (T1-T4) were used to compute heat flux at β=11o, and an additional four thermocouples (T5-T8) were used to compute heat flux at β=109o.
Saturation pressures equivalent to 35 ± 0.5℃ and 40±0.5℃ were monitored in LabVIEW. Steady state was attained when the vapor pressure and temperature variation across the wall were in the range of ±0.34 kPa and ±0.05℃. After steady state was reached, experimental data were recorded for five minutes. Each experimental datapoint is the average of data collected within five minutes, and they include the eight thermocouple wall temperatures, mass flow rate of cooling water, temperature inlet and outlet of cooling water, saturation temperature and pressure of refrigerants. The outer wall temperature varied around the tube, and the measured wall temperatures were used to compute the outer wall temperatures and temperature gradients at radial positions around the tube using a natural logarithm curve fit with the measured wall temperatures as input. These values were used to compute heat fluxes and local heat transfer coefficients were computed using thermal conductivity, heat flux, saturation temperature (determined from the measured saturation pressure) and wall temperatures. Uncertainties of local heat transfer coefficients for each experimental trial were calculated using the propagation of uncertainty method and uncertainties of the instrumentation.
DOI: 10.17632/8zfbfj4t7m.2
title: Dataset of On-tube local condensation heat transfer coefficient of R134a, R513A and R450A on smooth stainless-steel tube at saturation temperature of 35 and 40℃

description: These data contain local condensation heat transfer coefficients of three refrigerants (i.e., R134a, R4513A and R450A) on a single, horizontal, smooth stainless tube at saturation temperatures of 35 and 40℃. A custom experimental apparatus was constructed to measure on-tube condensation heat transfer coefficients which consisted of two main parts, the chamber and the condensation tube. Refrigerant was vaporized in the experimental chamber and then condensed on the condensation tube. Two recirculating chillers were connected to the experimental chamber. One supplied cooling water through the condensation tube and the other supplied water used to boil the liquid refrigerant in order to achieve saturation temperatures of 35 or 40℃ . The refrigerant boiled and subsequently condensed on the smooth condensation tube. Thermocouples were installed in the flow to measure temperature at the inlet and outlet of the tube. The condensation tube was machined using electron discharge machining and 1.016-mm-diameter thermocouples(T1-T8) were installed in the tube wall in order to measure heat flux through the wall at two radial positions, β=11o from the vertical and β=109o from the vertical. Four thermocouples (T1-T4) were used to compute heat flux at β=11o, and an additional four thermocouples (T5-T8) were used to compute heat flux at β=109o.
Saturation pressures equivalent to 35 ± 0.5℃ and 40±0.5℃ were monitored in LabVIEW. Steady state was attained when the vapor pressure and temperature variation across the wall were in the range of ±0.34 kPa and ±0.05℃. After steady state was reached, experimental data were recorded for five minutes. Each experimental datapoint is the average of data collected within five minutes, and they include the eight thermocouple wall temperatures, mass flow rate of cooling water, temperature inlet and outlet of cooling water, saturation temperature and pressure of refrigerants. The outer wall temperature varied around the tube, and the measured wall temperatures were used to compute the outer wall temperatures and temperature gradients at radial positions around the tube using a natural logarithm curve fit with the measured wall temperatures as input. These values were used to compute heat fluxes and local heat transfer coefficients were computed using thermal conductivity, heat flux, saturation temperature (determined from the measured saturation pressure) and wall temperatures. Uncertainties of local heat transfer coefficients for each experimental trial were calculated using the propagation of uncertainty method and uncertainties of the instrumentation.