cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.5hsj57
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bolitoglossa gomezi Wake, Savage & Hanken, 2007"
 ]
}

 The dataset includes 19 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028393-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/emtsv8/cg0elb
title: ngo_dum_instant.pdf
DOI: 10.7910/dvn/emtsv8/hdpibp
title: neilsen.tex
DOI: 10.7910/dvn/emtsv8/mhehl0
title: ngo_instant.pdf
DOI: 10.7910/dvn/emtsv8/ntfdkq
title: ngo-shaming.tab
DOI: 10.7910/dvn/emtsv8/xfszff
title: neilsen_instant.pdf
DOI: 10.7910/dvn/emtsv8/yyydpy
title: ngo-bipop.tex
DOI: 10.7910/dvn/ylterl/eelxil
title: Colgan and Lucas replication.do
DOI: 10.7910/dvn/ylterl/fq8uem
title: Colgan and Lucas replication - dyadic.tab
DOI: 10.7910/dvn/ylterl/gmqlt0
title: Colgan and Lucas replication - event history.tab
DOI: 10.7910/dvn/ylterl/ss1fvk
title: Codebook 1.0.pdf
DOI: 10.7910/dvn/emtsv8/1jhqfe
title: physint.tex
DOI: 10.7910/dvn/emtsv8/e15sux
title: ngo-summary.tex
DOI: 10.7910/dvn/emtsv8/ibmcki
title: ngo-tobits.tex
DOI: 10.7910/dvn/emtsv8/jnyujq
title: percapita.rtf
DOI: 10.7910/dvn/emtsv8/odx5mx
title: pubresVsNgo.eps
DOI: 10.7910/dvn/emtsv8/pcybq2
title: peru-germany.eps
DOI: 10.7910/dvn/emtsv8/z0nl4u
title: ngo-tobits.log
DOI: 10.7910/dvn/ewcnl5/3wokwz
title: Haslam_clean_ngp.fits
DOI: 10.7910/dvn/ewcnl5/9ommzs
title: Haslam_clean_sgp.fits
DOI: 10.7910/dvn/ewcnl5/hpbf1u
title: CodeIDL.tar.gz
DOI: 10.7910/dvn/ewcnl5/i0vncy
title: CodeC.tar.gz
DOI: 10.7910/dvn/ewcnl5/iv0jz5
title: FINK_Rmap_sgp.fits
DOI: 10.7910/dvn/ewcnl5/rpbpat
title: FINK_Rmap_ngp.fits
DOI: 10.7910/dvn/ylterl/hiudri
title: Colgan and Lucas replication - monadic.tab
DOI: 10.7910/dvn/ylterl/zidcbw
title: Colgan and Lucas replication_update1.do
DOI: 10.7910/dvn/emtsv8/iwdgzg
title: rwanda-uk.eps
DOI: 10.7910/dvn/emtsv8/oebgrf
title: Rplots.pdf
DOI: 10.7910/dvn/emtsv8/sqv01b
title: smallaidtrajectory.eps
DOI: 10.7910/dvn/emtsv8/xhdjn5
title: shame-donor-tobits.tex
DOI: 10.7910/dvn/emtsv8/zvt9hn
title: shame-donor-int-tobits.tex
DOI: 10.7910/dvn/ewcnl5/1knitm
title: SFD_i100_4096_sgp.fits
DOI: 10.7910/dvn/ewcnl5/jov0cm
title: SFD_dust_4096_ngp.fits
DOI: 10.7910/dvn/ewcnl5/uowkkj
title: SFD_i100_4096_ngp.fits
DOI: 10.7910/dvn/ewcnl5/wefr6q
title: SFD_i60_4096_ngp.fits
DOI: 10.7910/dvn/ewcnl5/xtlxx5
title: SFD_dust_4096_sgp.fits
DOI: 10.7910/dvn/ewcnl5/yibfxv
title: SFD_i60_4096_sgp.fits
DOI: 10.7910/dvn/emtsv8/1di2y9
title: table4.csv
DOI: 10.7910/dvn/emtsv8/6bo2xy
title: table2.csv
DOI: 10.7910/dvn/emtsv8/cwakwo
title: sublime2stata.do
DOI: 10.7910/dvn/emtsv8/kfdgp2
title: tobit_beta_ngo2.txt
DOI: 10.7910/dvn/emtsv8/of5ycq
title: table3.csv
DOI: 10.7910/dvn/emtsv8/zqkbyk
title: table1.csv
DOI: 10.7910/dvn/ewcnl5/1qpzzv
title: SFD_xmap_ngp.fits
DOI: 10.7910/dvn/ewcnl5/d52dvn
title: SFD_temp_sgp.fits
DOI: 10.7910/dvn/ewcnl5/fvle7f
title: SFD_temp_ngp.fits
DOI: 10.7910/dvn/ewcnl5/ikg5ak
title: Synch_Beta_ngp.fits
DOI: 10.7910/dvn/ewcnl5/qdo5i2
title: SFD_xmap_sgp.fits
DOI: 10.7910/dvn/ewcnl5/sgk0sj
title: SFD_mask_4096_ngp.fits
DOI: 10.7910/dvn/ewcnl5/x6p5mj
title: SFD_mask_4096_sgp.fits
DOI: 10.7910/dvn/emtsv8/dhiw4c
title: xttobit_beta.txt
DOI: 10.7910/dvn/emtsv8/h9afj4
title: tobit_VCV_ngo.txt
DOI: 10.7910/dvn/emtsv8/hdqttr
title: tobit_VCV_ngo2.txt
DOI: 10.7910/dvn/emtsv8/l3wtqz
title: tobit_beta_ngo.txt
DOI: 10.7910/dvn/emtsv8/pgwosu
title: unchr-summary.tex
DOI: 10.7910/dvn/emtsv8/wksbms
title: unchrcontinuous.rtf
DOI: 10.7910/dvn/ewcnl5/hn5xvf
title: Synch_Beta_sgp.fits
DOI: 10.7910/dvn/emtsv8/wasu8b
title: xttobit_VCV.txt
DOI: 10.7910/dvn/fchu0j/4mpuh5
title: 2_demographics_data_cleaner.R
DOI: 10.7910/dvn/fchu0j/el2vt7
title: 1_lift_data_cleaner.R
DOI: 10.7910/dvn/fchu0j/gt7jhk
title: 5_descriptives.R
DOI: 10.7910/dvn/fchu0j/ovuuaq
title: 4_demographics.R
DOI: 10.7910/dvn/fchu0j/yyzbkf
title: 3_lift_and_demo_merge_data.R
DOI: 10.7910/dvn/fchu0j/csh1ou
title: 6_NHST.R
DOI: 10.7910/dvn/fchu0j/czwyud
title: Demographics_Data_Clean.tab
DOI: 10.7910/dvn/fchu0j/hc60iu
title: fig1.R
DOI: 10.7910/dvn/fchu0j/lusihf
title: Decoy_Matrix.bmp
DOI: 10.7910/dvn/fchu0j/nfvj8v
title: drupal_survey_server.tar.gz.full
DOI: 10.7910/dvn/fchu0j/rylurg
title: Demographics_Data_All.tab
DOI: 10.15468/dl.eysksy
title: Occurrence Download

description: A dataset containing 4520 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Plantae",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Country is Germany",
 "DecimalLongitude is greater than or equal to 7.05649618872958",
 "DecimalLongitude is less than 7.05955574191397",
 "DecimalLatitude is greater than or equal to 47.27011",
 "DecimalLatitude is less than or equal to 55.05864"
 ]
}

 The dataset includes 4520 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028392-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/fchu0j/ayp3ju
title: figP.R
DOI: 10.7910/dvn/fchu0j/isjc8z
title: Https_Async_Server_Test_Towards_Random.py
DOI: 10.7910/dvn/fchu0j/jpwowi
title: Https_Async_Server_Test_Towards_Random_Prob_Estimate.py
DOI: 10.7910/dvn/fchu0j/lgrnpb
title: fig2.R
DOI: 10.7910/dvn/fchu0j/lmnw1k
title: figS.R
DOI: 10.7910/dvn/fchu0j/zu4wbi
title: Lift_And_Demo_Data_All.tab
DOI: 10.7910/dvn/x2rgrx/laxodu
title: Replication Data Link.docx
DOI: 10.7910/dvn/fchu0j/cdvdkn
title: streamingbandit_server.zip.full
DOI: 10.7910/dvn/fchu0j/da4ast
title: Lift_Data_All.tab
DOI: 10.7910/dvn/fchu0j/raunbt
title: Lift_Data_Clean.tab
DOI: 10.7910/dvn/fchu0j/ssqkpx
title: Local_Server_Sim_ProbEstimate.py
DOI: 10.7910/dvn/fchu0j/t5vy4a
title: Live_Server_Sim_ProbEstimate.py
DOI: 10.7910/dvn/fchu0j/ybnj1a
title: streamingbandit_experiments.tar.full
DOI: 10.7910/dvn/agafaq/bjq2bg
title: country.tab
DOI: 10.7910/dvn/agafaq/0gcusx
title: prize.csv
DOI: 10.7910/dvn/agafaq/mwn73m
title: laureate.csv
DOI: 10.7910/dvn/fbxptb/pnbyzs
title: CC2015_DataPointMapping.xlsx

description: A mapping document to map data points from 2010 - 2015
DOI: 10.7910/dvn/fbxptb/bhi0li
title: CDP 2006 Climate Change RS - CDP4_Legacy_CORE_AQ_PUBLIC.xls

description: CDP 2006 Climate Change
DOI: 10.7910/dvn/fbxptb/jx0ucv
title: CDP 2007 Climate Change.xls

description: CDP 2007 Climate Change
DOI: 10.7910/dvn/fbxptb/pog2kq
title: CDP 2004 Climate Change RS - CDP2_Legacy_Core_AQ_PUBLIC.xls

description: CDP 2004 Climate Change
DOI: 10.7910/dvn/fbxptb/qltbmu
title: CDP 2005 Climate Change RS - CDP3_Legacy_Core_AQ_PUBLIC.xls

description: CDP 2005 Climate Change
DOI: 10.7910/dvn/fbxptb/yh0e2y
title: CDP 2003 Climate Change RS - CDP1_Legacy_CORE_AQ_PUBLIC.xls

description: CDP 2003 Climate Change
DOI: 10.7910/dvn/lnusbb/7f7q8a
title: 1 main file.R
DOI: 10.7910/dvn/lnusbb/i2vjxh
title: 0 readme.txt
DOI: 10.7910/dvn/fbxptb/8ujgup
title: CDP 2012 Climate Change.tab

description: CDP 2012 Climate Change
DOI: 10.7910/dvn/fbxptb/mqrdul
title: CDP 2011 Climate Change.tab

description: CDP 2011 Climate Change
DOI: 10.7910/dvn/fbxptb/opkofe
title: CDP 2009 Climate Change.xls

description: CDP 2009 Climate Change
DOI: 10.7910/dvn/fbxptb/sbprcn
title: CDP 2013 Climate Change.tab

description: CDP 2013 Climate Change
DOI: 10.7910/dvn/fbxptb/xixcwl
title: CDP 2008 Climate Change.xls

description: CDP 2006 Climate Change
DOI: 10.7910/dvn/fbxptb/ydwrd1
title: CDP 2010 Climate Change.tab

description: CDP 2010 Climate Change
DOI: 10.7910/dvn/lnusbb/cplkd3
title: cow_trade_by_directed_dyad.tab
DOI: 10.7910/dvn/lnusbb/fetr8f
title: 3 tables and figures.R
DOI: 10.7910/dvn/lnusbb/nscjun
title: Current_Constant_DIVIDE.tab
DOI: 10.7910/dvn/lnusbb/qe3h9s
title: 2 country-year code.R
DOI: 10.7910/dvn/lnusbb/tkochx
title: 3 dyad-year code.R
DOI: 10.7910/dvn/lnusbb/zhxdqc
title: dist_cepii.tab
DOI: 10.7910/dvn/fbxptb/6ccw8f
title: CDP 2015 Climate Change.tab

description: CDP 2015 Climate Change
DOI: 10.7910/dvn/fbxptb/frzisb
title: CDP 2014 Climate Change.tab

description: CDP 2014 Climate Change
DOI: 10.7910/dvn/fbxptb/gb279v
title: CDP-global-climate-change-report-2015.pdf

description: CDP Global Climate Change Report 2015. Downloaded from CDP website 2016/10/13
DOI: 10.7910/dvn/lnusbb/hkbdec
title: .Rhistory
DOI: 10.7910/dvn/lnusbb/lx709l
title: factors.tab
DOI: 10.7910/dvn/lnusbb/qg0q6h
title: region.tab
DOI: 10.7910/dvn/lnusbb/xrnllx
title: NMC_v4_0.tab
DOI: 10.7910/dvn/iieyni/oygi3g
title: CDP 2011 Supply Chain (unique responders only).tab

description: CDP 2011 Supply Chain (unique responders only)
DOI: 10.7910/dvn/iieyni/ymkqjc
title: CDP 2010 Supply Chain (excluding responders to both programs and SM1.1).tab

description: CDP 2010 Supply Chain (excluding responders to both programs and SM1.1)
DOI: 10.15468/dl.wve8bn
title: Occurrence Download

description: A dataset containing 81 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Clupeoides borneensis Bleeker, 1851"
 ]
}

 The dataset includes 81 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057570-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tsd3ma
title: Occurrence Download

description: A dataset containing 6485299 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is United States of America",
 "TaxonKey is Insecta"
 ]
}

 The dataset includes 6485299 records from 533 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012758-190415153152247/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.spvr9p
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Wallenia lepperi Panfet & Ventosa"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005256-240425142415019/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uazjs7
title: Occurrence Download

description: A dataset containing 3100 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Halichoeres biocellatus Schultz, 1960"
 ]
}

 The dataset includes 3100 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057578-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wmtutp
title: Occurrence Download

description: A dataset containing 33 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "TaxonKey is Odocoileus virginianus ustus Trouessart, 1910"
 ]
}

 The dataset includes 33 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0080355-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d26mze
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cylindrolobus lactiflorus (Aver.) Vuong, Aver. & V.C.Nguyen"
 ]
}

 The dataset includes 3 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018422-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v3xpgr
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "CoordinateUncertaintyInMeters is less than or equal to 10000.0",
 "TaxonKey is Anthophyllum hystrix Dana, 1846"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0094387-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7f82yk
title: Occurrence Download

description: A dataset containing 44 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Uncaria lancifolia Hutch."
 ]
}

 The dataset includes 44 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023476-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ysddqz
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Uzbekistan",
 "TaxonKey is Panthera uncia (Schreber, 1775)"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0096590-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4ffpm9
title: Occurrence Download

description: A dataset listing the 112051 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Argentina",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil)"
 ]
 }
 }
 ]
}

 The dataset's 112051 records were derived from 2290 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014774-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/dans-x76-66j5
title: Sluis Groenevelt II, Burg. Aernoudtsweg Sluis Groenevelt II, Burg. Aernoudtsweg. Gemeente Sluis. Inventariserend Veldonderzoek door middel van Proefsleuven. Karterende & waarderende fase.

description: &lt;p&gt;Op een deel van het plangebied Groenevelt II, gelegen aan de Burgemeester Aernoudtsweg te Sluis, is een proefsleuvenonderzoek uitgevoerd. Dit onderzoek kadert in de geplande ontwikkeling van het ruim 1,3 ha grote plangebied tot een nieuwe verkaveling met woningen. Het onderzoeksgebied, 1808 m2 groot, omvat het grootste gedeelte van het kadastraal perceel dat bij de gemeente Sluis bekend staat onder Sectie M, Perceel 362.&lt;/p&gt;&lt;p&gt;In 2019 is hiervoor reeds een archeologisch bureauonderzoek met verkennend boringen uitgevoerd. Daaruit bleek dat het grootste deel van het plangebied enkel nog een archeologische verwachting aanwezig was in de top van het pleistocene dekzand. Dit niveau bevindt zich op een diepte van 2,60 m -mv (0,89 m -NAP) en wordt niet bedreigd door de voorgenomen bodemingrepen. In het uiterst oostelijke deel van het plangebied werden echter wel indicaties vastgesteld die wijzen in de richting van een laatmiddeleeuwse baksteenoven of veldoven. Voor deze zone werd in dit bureauonderzoek een beperking ingesteld op eventuele graafwerkzaamheden dieper dan 0,35 m -mv (1,35 m +NAP).&lt;/p&gt;&lt;p&gt;Met de geplande werkzaamheden in het onderzoeksgebied (t.b.v. de bouw van huizen, de aanleg van wegen, riolering, kabels en leidingen) wordt deze dieptevrijstellingsgrens overschreden, waardoor het potentiële bodemarchief wordt bedreigd. Daarom besloot de gemeente Sluis, bevoegde overheid inzake archeologie, dat nader onderzoek in de vorm van een inventariserend veldonderzoek door middel van proefsleuven (protocol 4003 – IVO-P: karterende en waarderende fase) noodzakelijk was.&lt;/p&gt;&lt;p&gt;Tijdens het proefsleuvenonderzoek werd de vooropgestelde verwachting bevestigd en werden inderdaad resten van een laatmiddeleeuwse veldoven gevonden, die diende voor de productie van baksteen. Resten van deze oven komen voor vanaf een diepte van circa 1,05 meter +NAP (0,65 meter -mv).&lt;/p&gt;&lt;p&gt;Deze vindplaats werd in overeenstemming met de KNA 4.1, bijlage IV Waarderen van vindplaatsen, gewaardeerd en behoudenswaardig bevonden.&lt;/p&gt;&lt;p&gt;Rond de behoudenswaardige resten is daarom nu een zone afgebakend waar geadviseerd wordt geen graafwerkzaamheden uit te laten voeren die dieper gaan dan 1,25 meter +NAP (0,45 meter -mv). Dit is de minimale diepte van de gevonden resten plus een buffer van 20 cm.&lt;/p&gt;
DOI: 10.5281/zenodo.15743799
title: Evaluation of the Applicability of AEOLUS Satellite Wind Products in Antarctica

description: # README

 

## Data and Code Description

 

This package supports the paper **"Evaluation of the Applicability of AEOLUS Satellite Wind Products in Antarctica"**. It contains all data and code needed to reproduce every analysis and figure presented in the manuscript.

 

### Folder Structure

 

- **data/**: Processed data files for analysis and plotting.

- **code/**: Python scripts for data processing, analysis, and visualization.

- **README.md**: This documentation file.

 

---

 

## System Requirements

 

- **Operating System:** Windows 10 (or equivalent)

- **Language & Dependencies:**

  - Python 3.9 or higher

  - See `requirements.txt` for a full list (e.g., `numpy`, `pandas`, `scipy`, `matplotlib`).

 

---

 

## Usage Instructions

 

1. **Unpack** the archive to your working directory.

2. **(Optional, recommended)** Create a Python virtual environment and install dependencies:

   ```bash

   python -m venv venv

   source venv/bin/activate   # Linux/macOS

   venv\Scripts\activate    # Windows

   pip install -r requirements.txt

   ```

3. **Run the analysis scripts** in the `/code` directory **in the following order**:

  - **01_rs_binavg_to_aeolus.py**  

  Perform vertical bin-averaging of radiosonde data to match Aeolus vertical resolution.

 

- **02_era5_binavg_to_aeolus_strict.py**  

  Perform strict vertical bin-averaging of ERA5 reanalysis data to match Aeolus profiles.

 

- **03_merge_rs_era5_binavg.py**  

  Merge radiosonde and ERA5 bin-averaged datasets into unified collocation files.

 

- **04_ee_threshold_sensitivity.py**  

  Sensitivity analysis for EE (estimated error) thresholds applied to Aeolus observations.

 

- **05_outlier_removal.py**  

  Apply Modified Z-score and other criteria to remove outliers from Aeolus–RS/ERA5 collocations.

 

- **06_station_statistics.py**  

  Compute collocation statistics grouped by station (Rothera, Mawson, Davis).

 

- **07_plot_scatter.py**  

  Generate scatterplots comparing Aeolus vs. RS/ERA5 winds.

 

- **08_plot_meteorology.py**  

  Generate meteorological context figures (e.g., ERA5 composites, cyclone cases).

 

---

 

## Data Source

 

All raw Aeolus Level-2B data and ERA5 reference data were obtained from the Copernicus Climate Data Store:

 

> https://aeolus-ds.eo.esa.int/oads/access/collection

>https://cds.climate.copernicus.eu/datasets

>https://weather.uwyo.edu/upperair/seasia.html

 

 

---

 

*Last updated: Sep 2025*
DOI: 10.5281/zenodo.15743887
title: Evaluation of the Applicability of AEOLUS Satellite Wind Products in Antarctica

description: # README

 

## Data and Code Description

 

This package supports the paper **"Evaluation of the Applicability of AEOLUS Satellite Wind Products in Antarctica"**. It contains all data and code needed to reproduce every analysis and figure presented in the manuscript.

 

### Folder Structure

 

- **data/**: Processed data files for analysis and plotting.

- **code/**: Python scripts for data processing, analysis, and visualization.

- **README.md**: This documentation file.

 

---

 

## System Requirements

 

- **Operating System:** Windows 10 (or equivalent)

- **Language & Dependencies:**

  - Python 3.9 or higher

  - See `requirements.txt` for a full list (e.g., `numpy`, `pandas`, `scipy`, `matplotlib`).

 

---

 

## Usage Instructions

 

1. **Unpack** the archive to your working directory.

2. **(Optional, recommended)** Create a Python virtual environment and install dependencies:

   ```bash

   python -m venv venv

   source venv/bin/activate   # Linux/macOS

   venv\Scripts\activate    # Windows

   pip install -r requirements.txt

   ```

3. **Run the analysis scripts** in the `/code` directory **in the following order**:

  - **01_rs_binavg_to_aeolus.py**  

  Perform vertical bin-averaging of radiosonde data to match Aeolus vertical resolution.

 

- **02_era5_binavg_to_aeolus_strict.py**  

  Perform strict vertical bin-averaging of ERA5 reanalysis data to match Aeolus profiles.

 

- **03_merge_rs_era5_binavg.py**  

  Merge radiosonde and ERA5 bin-averaged datasets into unified collocation files.

 

- **04_ee_threshold_sensitivity.py**  

  Sensitivity analysis for EE (estimated error) thresholds applied to Aeolus observations.

 

- **05_outlier_removal.py**  

  Apply Modified Z-score and other criteria to remove outliers from Aeolus–RS/ERA5 collocations.

 

- **06_station_statistics.py**  

  Compute collocation statistics grouped by station (Rothera, Mawson, Davis).

 

- **07_plot_scatter.py**  

  Generate scatterplots comparing Aeolus vs. RS/ERA5 winds.

 

- **08_plot_meteorology.py**  

  Generate meteorological context figures (e.g., ERA5 composites, cyclone cases).

 

---

 

## Data Source

 

All raw Aeolus Level-2B data and ERA5 reference data were obtained from the Copernicus Climate Data Store:

 

> https://aeolus-ds.eo.esa.int/oads/access/collection

>https://cds.climate.copernicus.eu/datasets

>https://weather.uwyo.edu/upperair/seasia.html

 

 

---

 

*Last updated: Sep 2025*
DOI: 10.17026/dans-x76-7g7g
title: Strabrechtse Heide, gemeenten Someren en Heeze-Leende RA2902_HEEST3_compleet

description: &lt;p&gt;In opdracht van Waterschap De Dommel heeft RAAP Archeologisch Adviesbureau B.V. een karterend veldonderzoek en een archeologische begeleiding uitgevoerd op de Strabrechtse Heide in de gemeenten Someren en Heeze-Leende. In het kader van de Archeologische MonumentenZorg was eerder al een bureauonderzoek uitgevoerd, later aangevuld met een Plan van Aanpak om te bepalen wat de invloed van de geplande ingrepen op de verwachte archeologie zou zijn en hoe verder met de archeologische resten omgegaan zou moeten worden. Uit het PvA is gebleken dat de geplande werkzaamheden plaatselijk overlappen met (verwachte) archeologische waarden. In de betreffende zones zijn de werken opgevolgd door archeologisch onderzoek. Het doel daarvan was het verkrijgen van inzicht in de archeologische resten die in het plangebied aanwezig zijn en in de fysieke kwaliteit daarvan.&lt;/p&gt;&lt;p&gt;Een oppervlaktekartering is uitgevoerd in geplagde zones. Daarnaast is een begeleiding uitgevoerd van het herstel van enkele beekmeanders. Bij de begeleiding zijn geen archeologische resten aangetroffen. Tijdens de oppervlaktekartering zijn echter 40 vindplaatsen ontdekt. Het gaat om 22 vindplaatsen met vuursteenvondsten uit de Steentijd, en 17 plekken met vondsten uit de Nieuwe tijd, 1 vindplaats met materiaal uit beide perioden. De archeologische indicatoren geven een goed beeld van de vindplaatsen. Duidelijk is dat twee groepen vindplaatsen aanwezig zijn: vindplaatsen uit de Steentijd en vindplaatsen uit de Nieuwe tijd. In beide gevallen gaat het vermoedelijk vooral om kleine vindplaatsen. De vindplaatsen uit de Steentijd bestaan uit losse vondsten of clusters van enkele vierkante meters, mogelijk tot ca. 25 m². De vindplaatsen uit de Nieuwe tijd bestaan ook uit losse vondsten of clusters van enkele vierkante meters. De bodemopbouw is grotendeels intact, wat betekent dat de vindplaatsen ook grotendeels intact (gaaf) zijn. Op beide groepen vindplaatsen kunnen grondsporen goed bewaard zijn. De conservering van de archeologische resten is verschillend. Met betrekking tot de Steentijdvindplaatsen is de conservering meestal gemiddeld: op het goed ontwaterde dekzand is alleen lithisch materiaal bewaard.&lt;/p&gt;