Median (M), 75th percentile (P75) and 25th percentile (P25) for each electrophysiological descriptor analysed and for each cell density (median values include different DIV for each cell density).

This is the repository for the results of the 'expert opinion survey on environmental modeling with InVEST, Mapbiomas, and Open Street Maps'.

Note: check the most recent version in the sidebar

Current version v.0.2

Date 2024/01/10

Respondants 30

Available files:

File Type Description

responses_v01_public.csv CSV table Survey raw results (anonymous)

responses_v01_stats.csv CSV table Questions statistics

responses_v01_mean_sd.jpg JPEG Image Illustration of Stats (mean and standard deviation)

responses_v01_bands.jpg JPEG Image Illustration of Stats (uncertainty bands)

The column descriptions in the statistical table are as follows:

Prefixes:

HABITAT: habitat suitability score

WEIGHT: Threat weight

MAX_DIST: Maximum distance of negative influence (impact)

Suffixes:

mean: Average

std: Standard deviation

min: Minimum value

p05: 5th percentile

p25: 25th percentile

p50: 50th percentile (median)

p75: 75th percentile

p95: 95th percentile

max: Maximum value

These prefixes and suffixes describe various statistical measures used to analyze the environmental modeling data.

Data information

  <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2003. 
  The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. 
  The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; 
  as well as two spectral indices: NDVI and NDWI.</p>
  <h2><strong>As a part of a Data Cube</strong></h2>
  <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. 
  For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p>
  <ul>
  <li>To cite this dataset, refer to the DOI available on the landing page.</li>
  <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li>
  </ul>
  <h2><strong>Support</strong></h2>
  <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Legend: SD refers to standard deviation, MSE, to mean square error, P3 to the 3rd percentile, P10 to the 10th percentile, P25 to the 25th percentile, P50 to the 50th percentile, P75 to the 75th percentile, P90 to the 90th percentile, and P97 to the 97th percentile*Two-way ANOVA analyzing the effect of two factors, age group and hand side dominance on Ri:R0 ratios.Extracellular To Intracellular Resistance (Ri/R0) Ratios By Body Segment.

Legend: SD refers to standard deviation, P3 to the 3rd percentile, P10 to the 10th percentile, P25 to the 25th percentile, P50 to the 50th percentile, P75 to the 75th percentile, P90 to the 90th percentile, and P97 to the 97th percentile.Extracellular Resistance (R0/R0) Ratios For Arms And Legs By Age Group.

Data information

  <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2019. 
  The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. 
  The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; 
  as well as two spectral indices: NDVI and NDWI.</p>
  <h2><strong>As a part of a Data Cube</strong></h2>
  <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. 
  For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p>
  <ul>
  <li>To cite this dataset, refer to the DOI available on the landing page.</li>
  <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li>
  </ul>
  <h2><strong>Support</strong></h2>
  <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Overview

SandSnap is a collaborative project engaging citizen scientists to amass a sand beach grain size database and educating the next generation about coastal processes. See the following webpage for more details: https://sandsnap-erdcchl.hub.arcgis.com/

SandSnap is funded by the US Army Corps of Engineers through the Coastal Inlets Research Program and the Regional Sediment Management Program.

SandSnap allows anyone with a cell phone to take an image of the sand with a US coin and measure the sand’s grain size using a deep learning neural network (Buscombe, 2020; McFall et. all, 2020). This model is trained using data obtained from sieved physical samples of sand. The purpose of this data release is to document the data sets that went into the SandSnap model, trained in Aug 2021, and used between August 2021 ongoing on this date October 26 2022.

Data formats and fields

usace_1024_aug_dry_set1_2_3_4_5_aug2021.csv

This is a spreadsheet that contains inputs for training the SandSnap SediNet model. SediNet is a deep-learning-based grain size predictor, by Dr Daniel Buscombe, Marda Science, LLC (https://github.com/DigitalGrainSize/SediNet). The SediNet model behind SandSnap v1 (August, 2021) is configured to estimate the grain size in pixels. A separate model is used to detect and size the coin, to estimate image scaling for grain size estimates in millimeters.

File: name of image

Latitude: WGS84 coordinate

Longitude: WGS84 coordinate

Population: an integer, identifying the site that the image came from. For internal model validation purposes (grouping error by site)

dry: 0= visibly wet sand, 1= visibly dry sand

mm_px: millimeter per pixel scaling, computed from digitizing a coin in each image, as the diameter of the coin in millimeters, divided by the number of pixels across the diameter of the coin

d10: 10th percentile of the cumulative grain size distribution, obtained by sieve analysis, in millimeters

d16: 16th percentile of the cumulative grain size distribution, obtained by sieve analysis, in millimeters

d25: 25th percentile of the cumulative grain size distribution, obtained by sieve analysis, in millimeters

d50: 50th percentile of the cumulative grain size distribution, obtained by sieve analysis, in millimeters

d65: 65th percentile of the cumulative grain size distribution, obtained by sieve analysis, in millimeters

d75: 75th percentile of the cumulative grain size distribution, obtained by sieve analysis, in millimeters

d84: 84th percentile of the cumulative grain size distribution, obtained by sieve analysis, in millimeters

d90: 90th percentile of the cumulative grain size distribution, obtained by sieve analysis, in millimeters

mean: mean grain size, obtained by sieve analysis, in millimeters

P10: 10th percentile of the cumulative grain size distribution, obtained by sieve analysis, in pixels

P16: 16th percentile of the cumulative grain size distribution, obtained by sieve analysis, in pixels

P25: 25th percentile of the cumulative grain size distribution, obtained by sieve analysis, in pixels

P50: 50th percentile of the cumulative grain size distribution, obtained by sieve analysis, in pixels

P65: 65th percentile of the cumulative grain size distribution, obtained by sieve analysis, in pixels

P75: 75th percentile of the cumulative grain size distribution, obtained by sieve analysis, in pixels

P84: 84th percentile of the cumulative grain size distribution, obtained by sieve analysis, in pixels

P90: 90th percentile of the cumulative grain size distribution, obtained by sieve analysis, in pixels

Pmean: mean grain size, obtained by sieve analysis, in pixels

GrainSizeAdditionalImagesTraining_Aug2021_latlong.xlsx assigns a coordinate to imagery and contains the following fields:

    DatabaseObjectID
    ATT ID
    Name
    Coin
    mean
    Latitude
    Longitude

*.zip format files

Zipped folders contain original images, as well as augmented and tiled images for analysis. Tiled images are patches of original images with no coin scale. Patches are 1024 x 1024 x 3 pixels. Augmented images are tiles that have been flipped in both horizontal dimensions.

*.py format files

Python code for creating tiled and augmented images

References

Buscombe, D., 2020. SediNet: A configurable deep learning model for mixed qualitative and quantitative optical granulometry. Earth Surface Processes and Landforms, 45(3), pp.638-651.

McFall, B.C., Young, D.L., Fall, K.A., Krafft, D.R., Whitmeyer, S.J., Melendez, A.E. and Buscombe, D., 2020. Technical Feasibility of Creating a Beach Grain Size Database with Citizen Scientists. ERDC Coastal and Hydraulics Laboratory.

Legend: SD refers to standard deviation, P3 to the 3rd percentile, P10 to the 10th percentile, P25 to the 25th percentile, P50 to the 50th percentile, P75 to the 75th percentile, P90 to the 90th percentile, and P97 to the 97th percentile.Difference In Circumference (In cm) For Arms And Ankles.

Data information This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2004. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI. As a part of a Data Cube This data represents a subset of the Time-series of Landsat-based Spectral Indices (EU, 30m) data cube. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. To cite this dataset, refer to the DOI available on the landing page. To access other data layers in the data cube, use the navigation catalog on the landing page as well. Support If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a Github Issue!

SD: standard deviation; Min, minimum; P25, the 25th percentile; P75, the 75th percentile; Max, maximum.AT, average temperature; MiT, minimum temperature; MaT, maximum temperature; ARH, average relative humidity; MiRH, minimum relative humidity; AAP, average air pressure; MiAP, minimum air pressure; MaAP, maximum air pressure; AWV, average wind velocity; MaWV, maximum wind velocity; RF, rainfall; AVP, average vapor pressure; SD, sunshine duration.*p

SD: standard deviation; Min, minimum; P25, the 25th percentile; P75, the 75th percentile; Max, maximum.AT, average temperature; MiT, minimum temperature; MaT, maximum temperature; ARH, average relative humidity; MiRH, minimum relative humidity; AAP, average air pressure; MiAP, minimum air pressure; MaAP, maximum air pressure; AWV, average wind velocity; MaWV, maximum wind velocity; RF, rainfall; AVP, average vapor pressure; SD, sunshine duration.*p

Data information This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2011. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI. As a part of a Data Cube This data represents a subset of the Time-series of Landsat-based Spectral Indices (EU, 30m) data cube. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. To cite this dataset, refer to the DOI available on the landing page. To access other data layers in the data cube, use the navigation catalog on the landing page as well. Support If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a Github Issue!

Data information This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2016. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI. As a part of a Data Cube This data represents a subset of the Time-series of Landsat-based Spectral Indices (EU, 30m) data cube. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. To cite this dataset, refer to the DOI available on the landing page. To access other data layers in the data cube, use the navigation catalog on the landing page as well. Support If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a Github Issue!

Data information

  <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2018. 
  The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. 
  The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; 
  as well as two spectral indices: NDVI and NDWI.</p>
  <h2><strong>As a part of a Data Cube</strong></h2>
  <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. 
  For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p>
  <ul>
  <li>To cite this dataset, refer to the DOI available on the landing page.</li>
  <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li>
  </ul>
  <h2><strong>Support</strong></h2>
  <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Data information This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2007. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI. As a part of a Data Cube This data represents a subset of the Time-series of Landsat-based Spectral Indices (EU, 30m) data cube. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. To cite this dataset, refer to the DOI available on the landing page. To access other data layers in the data cube, use the navigation catalog on the landing page as well. Support If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a Github Issue!

IL, interleukins (pg/ml); TNF tumor necrosis factor (pg/ml); P75, percentile 75; P25, percentile 25.aAdjusted for age, time since menopause, body mass index, smoking and physical activity.bSignificant difference p

ABSTRACT Introduction Aerobic fitness is an important predictor that contributes to the preservation of functional independence during the aging process. Its measurement represents a fundamental tool in the identification of multiple health problems. Objective To compare the aerobic capacity of adults and elderly subjects through international studies and to develop percentiles by age group using the LMS method. Methods A cross-sectional descriptive study was conducted with 1146 subjects (437 men and 709 women). The age group of the sample ranged from 50 to 84 years. The subjects evaluated came from the physical activity programs offered by the National Sports Institute (IND) and by the city council of Talca (Chile). Body mass, stature, oxygen saturation (SatO2), six-minute walk test, and systolic and diastolic blood pressure were assessed. Body Mass Index (BMI) was calculated for both sexes. The LMS method was used to propose the percent distribution. Results Aerobic capacity decreases with age (28.5% for men and 29.9% for women). There was a negative relationship between age and the six-minute walk test (men r = -0.13 and women r = -0.39). There was a discrepancy between the elderly subjects in the current study and those from international studies. The normative data for the classification of aerobic fitness were expressed in percentiles (p3, p5, p10, p15, p25, p50, p75, p85, p90, p95 and p97). Conclusion The aerobic performance of elderly subjects diminishes as they age; in addition, the current results differ from international studies, which motivated the development of percentiles to classify aerobic fitness in everyday situations, especially in places with few resources and particularly where field tests are considered a priority for large-scale physical evaluation. Level of evidence II; Diagnostic studies – investigation of diagnostic test.

Salario mensile lordo standardizzato (in fr.) e altri indicatori nel settore privato, secondo la divisione economica (NOGA 2008), il sesso, la posizione nella professione, il grado di formazione e la residenza (residenti, frontalieri), in Ticino, dal 2008 al 2022

Fonte: Rilevazione della struttura dei salari (RSS), Ufficio federale di statistica, Neuchâtel
Elaborazione: Ufficio di statistica (Ustat), Giubiasco

Ultima modifica: 14.11.2024

Versione dei dati: 05.11.2024

Variabili presenti nel cubo di dati:
anno: l'anno dell'inchiesta
noga08_2_descr: la divisione economica (NOGA 2008)
sesso: il sesso
posizione: la posizione nella professione
formazione2: il grado di formazione
residenti_2: la residenza (residenti, frontalieri)

Descrizione delle statistiche:
AD_salariati: addetti ai sensi della RSS
ETP_salariati: addetti ETP ai sensi della RSS
p10: decimo percentile del salario mensile lordo standardizzato (primo decile, in franchi)
p25: venticinquesimo percentile del salario mensile lordo standardizzato (primo quartile, in franchi)
p50: cinquantesimo percentile del salario mensile lordo standardizzato (mediana, in franchi)
p75: settantacinquesimo percentile del salario mensile lordo standardizzato (terzo quartile, in franchi)
p90: novantesimo percentile del salario mensile lordo standardizzato (nono decile, in franchi)

La colonna "info" riporta una delle informazioni seguenti:
ok: esistono delle stime con le caratteristiche descritte in quella riga
…: dato non disponibile
X: dato non pubblicato per motivi legati alla protezione dei dati
( ): coefficiente di variazione superiore a 5% (valore incerto a livello statistico)

Segni, simboli, abbreviazioni, sigle e concetti statistici usati nei prodotti dell'Ustat

Glossario:
Salario mensile lordo standardizzato
Addetti ai sensi della RSS
Addetti ETP ai sensi della RSS

SAFA: saturated fatty acids, TFA: trans fatty acids, IQR: inter quartile range (P75 - P25).

p25∶25th percentile.p75∶75th percentile.SD: standard deviation.