cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28226720
title: Supplementary Material for: Development of macular atrophy after macular hole surgery in an eye with retinitis pigmentosa

description: Introduction
Macular hole is a rare complication that occurs in patients with retinitis pigmentosa, significantly reducing visual acuity. Although vitreous surgery for macular hole yields favorable outcomes, macular atrophy after surgery has been reported. We report the second case of retinitis pigmentosa in a patient who developed a 13-year progressive macular atrophy after macular hole surgery.
Case Presentation
A 64-year-old Japanese woman, who had been diagnosed with retinitis pigmentosa at 52 years of age, presented to our hospital with blurred vision in the left eye. Phacovitrectomy of the left eye was performed after a full-thickness macular hole was revealed by optical coherence tomography. We stained the internal limiting membrane during surgery using 0.05% indocyanine green and peeled it around the macular hole. Nevertheless, slight atrophy of the retinal pigment epithelium appeared in the left macula 17 days after surgery. The macular hole closed 1 year after surgery, and the macular atrophy gradually became more apparent and enlarged. Thirteen years later, atrophy had expanded to 2.5-disc diameters, and the left decimal best-corrected visual acuity was 0.1; no macular degeneration appeared in the right eye. Genetic examination revealed compound heterozygous variants in the EYS gene. 
Discussion　
Macular atrophy can develop after dye-assisted macular hole surgery for patients with retinitis pigmentosa. Potential risk factors for the development of postoperative macular atrophy include dye toxicity, light toxicity, surgical intervention in the macula, postoperative inflammation, and genotype. However, the exact cause of atrophy remains uncertain.
DOI: 10.15468/dl.7y93bc
title: Occurrence Download

description: A dataset containing 780 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dicologlossa hexophthalma (Bennett, 1831)"
 ]
}

 The dataset includes 780 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0073554-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11044037
title: Skillful bias correction of offshore near-surface wind speed and wind direction forecasting based on a multi-task machine learning model

description: Dataset

1. observation data over 14 weather stations

Variables: hourly near-surface 2-min average wind speed, wind direction 

2. ECMWF-IFS forecast data over 14 weather stations

Variables: hourly predictors at surface level and upper level in next 48 hours (shown in Table 1. and Table 2.)

Table 1. ECMWF-IFS forecast data at surface level








Predictors



Abbreviation



Unit





Temperature at 2 m



2t



℃





Sea surface temperature



sst



℃





Dewpoint temperature at 2 m



2d



℃





Convective precipitation in the past hour



cp



mm





Mean sea level pressure



msl



hPa





Zonal component of wind speed at 10 m



10u



m s-1





Meridional component of wind speed at 10 m



10v



m s-1





Wind speed at 10 m



10ws



m s-1





Wind direction at 10 m



10wd



°





Zonal component of wind speed at 100 m



100u



m s-1





Meridional component of wind speed at 100 m



100v



m s-1





Wind speed at 100 m



100ws



m s-1





Wind direction at 100 m



100wd



°






 

Table 2. ECMWF-IFS forecast data at upper level






Predictors



Abbreviation



Unit





Relative humidity at xxx hPa



r_Lxxx



%





Temperature at xxx hPa



t_Lxxx



℃





Vertical velocity of wind at xxx hPa



w_Lxxx



Pa s-1





Zonal component of wind at xxx hPa



u_Lxxx



m s-1





Meridional component of wind at xxx hPa



v_Lxxx



m s-1





Wind speed at xxx hPa



ws_Lxxx



m s-1





Wind direction at xxx hPa



wd_Lxxx



°





 

3. key variables constructed by feature engineering

(1) sort-term statistics, including maximum, minimum, mean and variance of key variables (2t, 10u, 10v and 10ws) from ECMWF-IFS model during the next 48 hours,

 (2) long-term statistics, including mean and deviation of key variables (2t, 10u, 10v and 10ws) from ECMWF-IFS model during history 3-yr period (January 2020–December 2022),

 (3) thermodynamic factors,  including the low-level wind shear between 10ws and 100ws, vertical wind shear between 200 hPa and 850 hPa, the differences between sst and 2t.

Scripts

1. Random Forest model training code

2. LightGBM model training code

3. XGBoost model training code

4. TabNet-MTL model training code
DOI: 10.5281/zenodo.11044038
title: Skillful bias correction of offshore near-surface wind speed and wind direction forecasting based on a multi-task machine learning model

description: Dataset

1. observation data over 14 weather stations

Variables: hourly near-surface 2-min average wind speed, wind direction 

2. ECMWF-IFS forecast data over 14 weather stations

Variables: hourly predictors at surface level and upper level in next 48 hours (shown in Table 1. and Table 2.)

Table 1. ECMWF-IFS forecast data at surface level








Predictors



Abbreviation



Unit





Temperature at 2 m



2t



℃





Sea surface temperature



sst



℃





Surface skin temperature



skt



℃





Dewpoint temperature at 2 m



2d



℃





Convective precipitation in the past hour



cp



mm





Surface pressure



sp



hPa





Mean sea level pressure



msl



hPa





Zonal component of wind speed at 10 m



10u



m s-1





Meridional component of wind speed at 10 m



10v



m s-1





Wind speed at 100 m



10ws



m s-1





Zonal component of wind speed at 100 m



100u



m s-1





Meridional component of wind speed at 100 m



100v



m s-1





Wind speed at 100 m



100ws



m s-1






Table 2. ECMWF-IFS forecast data at upper level








Predictors



Abbreviation



Unit





Relative humidity at xxx hPa



r_Lxxx



%





Temperature at xxx hPa



t_Lxxx



℃





Vertical velocity of wind at xxx hPa



w_Lxxx



Pa s-1





Zonal component of wind at xxx hPa



u_Lxxx



m s-1





Meridional component of wind at xxx hPa



v_Lxxx



m s-1





Wind speed at xxx hPa



ws_Lxxx



m s-1





Wind direction at xxx hPa



wd_Lxxx



°






3. key variables constructed by feature engineering

① statistics values (maximum, minimum, mean and variance) of key variables (2t, 10u, 10v and 10ws) from ECMWF-IFS model during the next 48 hours (short-term) and history 3-yr period (January 2020–December 2022, long-term)

② short-term variance of msl and sp

③ short-term and long-term deviations of key variables (2t, 10u, 10v and 10ws)

Model

1. RF_model (Single-Task Learning Model)

2. LighGBM_model (Single-Task Learning Model)

3. XGBoost_model (Single-Task Learning Model)

4. TabNet_STL_model (Single-Task Learning Model)

5. TabNet_MTL_model (Multi-Task Learning Model)

Scripts

Python program used for 1. Model training on training datasets2. Model evaluation on test datasets

Figures

Figures corresponding to Python program output
DOI: 10.5281/zenodo.11072565
title: Skillful bias correction of offshore near-surface wind speed and wind direction forecasting based on a multi-task machine learning model

description: Dataset

1. observation data over 14 weather stations

Variables: hourly near-surface 2-min average wind speed, wind direction 

2. ECMWF-IFS forecast data over 14 weather stations

Variables: hourly predictors at surface level and upper level in next 48 hours (shown in Table 1. and Table 2.)

Table 1. ECMWF-IFS forecast data at surface level








Predictors



Abbreviation



Unit





Temperature at 2 m



2t



℃





Sea surface temperature



sst



℃





Surface skin temperature



skt



℃





Dewpoint temperature at 2 m



2d



℃





Convective precipitation in the past hour



cp



mm





Surface pressure



sp



hPa





Mean sea level pressure



msl



hPa





Zonal component of wind speed at 10 m



10u



m s-1





Meridional component of wind speed at 10 m



10v



m s-1





Wind speed at 100 m



10ws



m s-1





Zonal component of wind speed at 100 m



100u



m s-1





Meridional component of wind speed at 100 m



100v



m s-1





Wind speed at 100 m



100ws



m s-1






Table 2. ECMWF-IFS forecast data at upper level








Predictors



Abbreviation



Unit





Relative humidity at xxx hPa



r_Lxxx



%





Temperature at xxx hPa



t_Lxxx



℃





Vertical velocity of wind at xxx hPa



w_Lxxx



Pa s-1





Zonal component of wind at xxx hPa



u_Lxxx



m s-1





Meridional component of wind at xxx hPa



v_Lxxx



m s-1





Wind speed at xxx hPa



ws_Lxxx



m s-1





Wind direction at xxx hPa



wd_Lxxx



°






3. key variables constructed by feature engineering

① statistics values (maximum, minimum, mean and variance) of key variables (2t, 10u, 10v and 10ws) from ECMWF-IFS model during the next 48 hours (short-term) and history 3-yr period (January 2020–December 2022, long-term)

② short-term variance of msl and sp

③ short-term and long-term deviations of key variables (2t, 10u, 10v and 10ws)

Model

1. RF_model (Single-Task Learning Model)

2. LighGBM_model (Single-Task Learning Model)

3. XGBoost_model (Single-Task Learning Model)

4. TabNet_STL_model (Single-Task Learning Model)

5. TabNet_MTL_model (Multi-Task Learning Model)

Scripts

Python program scripts used for 1. Model training on training datasets2. Model evaluation on test datasets

Figures

Figures corresponding to Python program output
DOI: 10.5281/zenodo.12516630
title: Skillful bias correction of offshore near-surface wind speed and wind direction forecasting based on a multi-task machine learning model

description: Dataset

1. observation data over 14 weather stations

Variables: hourly near-surface 2-min average wind speed, wind direction 

2. ECMWF-IFS forecast data over 14 weather stations

Variables: hourly predictors at surface level and upper level in next 48 hours (shown in Table 1. and Table 2.)

Table 1. ECMWF-IFS forecast data at surface level








Predictors



Abbreviation



Unit





Temperature at 2 m



2t



℃





Sea surface temperature



sst



℃





Dewpoint temperature at 2 m



2d



℃





Convective precipitation in the past hour



cp



mm





Mean sea level pressure



msl



hPa





Zonal component of wind speed at 10 m



10u



m s-1





Meridional component of wind speed at 10 m



10v



m s-1





Wind speed at 10 m



10ws



m s-1





Wind direction at 10 m



10wd



°





Zonal component of wind speed at 100 m



100u



m s-1





Meridional component of wind speed at 100 m



100v



m s-1





Wind speed at 100 m



100ws



m s-1





Wind direction at 100 m



100wd



°






 

Table 2. ECMWF-IFS forecast data at upper level






Predictors



Abbreviation



Unit





Relative humidity at xxx hPa



r_Lxxx



%





Temperature at xxx hPa



t_Lxxx



℃





Vertical velocity of wind at xxx hPa



w_Lxxx



Pa s-1





Zonal component of wind at xxx hPa



u_Lxxx



m s-1





Meridional component of wind at xxx hPa



v_Lxxx



m s-1





Wind speed at xxx hPa



ws_Lxxx



m s-1





Wind direction at xxx hPa



wd_Lxxx



°





 

3. key variables constructed by feature engineering

(1) sort-term statistics, including maximum, minimum, mean and variance of key variables (2t, 10u, 10v and 10ws) from ECMWF-IFS model during the next 48 hours,

 (2) long-term statistics, including mean and deviation of key variables (2t, 10u, 10v and 10ws) from ECMWF-IFS model during history 3-yr period (January 2020–December 2022),

 (3) thermodynamic factors,  including the low-level wind shear between 10ws and 100ws, vertical wind shear between 200 hPa and 850 hPa, the differences between sst and 2t.

Scripts

1. Random Forest model training code

2. LightGBM model training code

3. XGBoost model training code

4. TabNet-MTL model training code
DOI: 10.5281/zenodo.12527537
title: Skillful bias correction of offshore near-surface wind speed and wind direction forecasting based on a multi-task machine learning model

description: Dataset

1. observation data over 14 weather stations

Variables: hourly near-surface 2-min average wind speed, wind direction 

2. ECMWF-IFS forecast data over 14 weather stations

Variables: hourly predictors at surface level and upper level in next 48 hours (shown in Table 1. and Table 2.)

Table 1. ECMWF-IFS forecast data at surface level








Predictors



Abbreviation



Unit





Temperature at 2 m



2t



℃





Sea surface temperature



sst



℃





Dewpoint temperature at 2 m



2d



℃





Convective precipitation in the past hour



cp



mm





Mean sea level pressure



msl



hPa





Zonal component of wind speed at 10 m



10u



m s-1





Meridional component of wind speed at 10 m



10v



m s-1





Wind speed at 10 m



10ws



m s-1





Wind direction at 10 m



10wd



°





Zonal component of wind speed at 100 m



100u



m s-1





Meridional component of wind speed at 100 m



100v



m s-1





Wind speed at 100 m



100ws



m s-1





Wind direction at 100 m



100wd



°






 

Table 2. ECMWF-IFS forecast data at upper level






Predictors



Abbreviation



Unit





Relative humidity at xxx hPa



r_Lxxx



%





Temperature at xxx hPa



t_Lxxx



℃





Vertical velocity of wind at xxx hPa



w_Lxxx



Pa s-1





Zonal component of wind at xxx hPa



u_Lxxx



m s-1





Meridional component of wind at xxx hPa



v_Lxxx



m s-1





Wind speed at xxx hPa



ws_Lxxx



m s-1





Wind direction at xxx hPa



wd_Lxxx



°





 

3. key variables constructed by feature engineering

(1) sort-term statistics, including maximum, minimum, mean and variance of key variables (2t, 10u, 10v and 10ws) from ECMWF-IFS model during the next 48 hours,

 (2) long-term statistics, including mean and deviation of key variables (2t, 10u, 10v and 10ws) from ECMWF-IFS model during history 3-yr period (January 2020–December 2022),

 (3) thermodynamic factors,  including the low-level wind shear between 10ws and 100ws, vertical wind shear between 200 hPa and 850 hPa, the differences between sst and 2t.

Scripts

1. Random Forest model training code

2. LightGBM model training code

3. XGBoost model training code

4. TabNet-MTL model training code
DOI: 10.15468/dl.yek55z
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Diodon hystrix Bloch, 1785"
 ]
}

 The dataset includes 13 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0073555-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kz7khr
title: Occurrence Download

description: A dataset containing 27086 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Diodon hystrix Linnaeus, 1758"
 ]
}

 The dataset includes 27086 records from 308 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0073556-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28226727
title: a video of the PIV experiment for the robot from Upstroke wing clapping in bats and bat-inspired robots offers efficient lift generation.

description: Supplementary.zip
DOI: 10.15468/dl.pcpx52
title: Occurrence Download

description: A dataset containing 2156 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Diploprion bifasciatum Cuvier, 1828"
 ]
}

 The dataset includes 2156 records from 85 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0073557-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b8zp78
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Afrotyphlops obtusus (Peters, 1865)"
 ]
}

 The dataset includes 25 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0276937-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xxcd6m
title: Occurrence Download

description: A dataset containing 703 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gymnocoronis spilanthoides DC.",
 "HasCoordinate is true",
 "Year is greater than or equal to 1900",
 "BasisOfRecord is one of (Human Observation, Specimen, Unknown evidence)"
 ]
}

 The dataset includes 703 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0134874-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z7kukd
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Flexanthera Rusby"
 ]
}

 The dataset includes 9 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0216954-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qus8qk
title: Occurrence Download

description: A dataset containing 680 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Gymnocoronis spilanthoides DC.)",
 "BasisOfRecord is one of (Observation, Human Observation, Material sample, Literature Occurrence, Specimen, Unknown evidence, Machine Observation)",
 "Year is greater than or equal to 1976",
 "Year is less than or equal to 2020",
 "HasCoordinate is true"
 ]
}

 The dataset includes 680 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0134868-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.594dva
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cheirostylis grandiflora Blume"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0134875-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kzju55
title: Occurrence Download

description: A dataset containing 164541 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Croton abutilifolius Croizat, Croton abutiloides Kunth, Croton adenophyllum Spreng., Croton adipatus Kunth, Croton adspersus Benth., Croton aequatoris Croizat, Croton alchorneicarpus Croizat, Croton alnifolius Lam., Croton bonplandianus Baill., Croton caboensis Croizat, Croton L., Croton celtidifolius Baill., Croton chichenensis Lundell, Croton chilensis Müll.Arg., Croton chimboracensis P.E.Berry & Riina, Croton ciliatoglandulifer Ortega, Croton conduplicatus Kunth, Croton coriaceus Kunth, Croton craspedotrichus Griseb., Croton curiosus Croizat, Croton discolor Willd., Croton disjunctus V.W.Steinm., Croton draco Schltdl. & Cham., Croton L., Croton echioides Baill., Croton ehrenbergii Schltdl., Croton elegans Kunth, Croton emporiorum Croizat, Croton flavens L., Croton floccosus B.A.Sm., Croton francoanus Müll.Arg., Croton fruticulosus Torr., Croton gossypiifolius Vahl, Croton gracilipes Baill., Croton heterochrous Müll.Arg., Croton hibiscifolius Kunth ex Spreng., Croton huberi Steyerm., Croton humilis L., Croton hypoleucus Schltdl., Croton impressus Urb., Croton jimenezii Standl. & Valerio, Croton laceratoglandulosus Caruzo & Cordeiro, Croton lagoensis Müll.Arg., Croton lasiopetaloides Croizat, Croton lechleri Müll.Arg., Croton leptostachyus Kunth, Croton limnocharis Croizat, Croton linearis Jacq., Croton macrobothrys Baill., Croton magdalenae Millsp., Croton magdalenensis Müll.Arg., Croton medusae Müll.Arg., Croton menthodorus Benth., Croton michaelii V.W.Steinm., Croton monogynus Urb., Croton nephrophyllus Urb. & Ekman, Croton payaquensis Standl., Croton peltoideus Kunth, Croton peraeruginosus Croizat, Croton perspeciosus Croizat, Croton piluliferus Rusby, Croton pulcher Müll.Arg., Croton pungens Jacq., Croton purdiei Müll.Arg., Croton redolens Pittier, Croton rimbachii Croizat, Croton roxanae Croizat, Croton ruizianus Müll.Arg., Croton saltensis Griseb., Croton sancti-lazari Croizat, Croton sarcopetalus Müll.Arg., Croton scouleri Hook.f., Croton soliman Schltdl. & Cham., Croton sonorae Torr., Croton soratensis Müll.Arg., Croton speciosus Müll.Arg., Croton stenosepalus Müll.Arg., Croton stipulaceus Kunth, Croton suaveolens Torr., Croton suberosus Kunth, Croton suyapensis Ant.Molina, Croton thurifer Kunth, Croton torreyanus Müll.Arg., Croton urucurana Baill., Croton viminalis Griseb., Croton virletianus Müll.Arg., Croton vitifolius Lundell, Croton websteri M.J.Martinez Gordillo & J.Jiménez Ram., Croton wilsonii Griseb., Croton xalapensis Kunth, Croton yecorensis V.W.Steinmann & Felger, Croton ynesae Croizat)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 164541 records from 920 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0134870-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dkhmm8
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cheirostylis monteiroi S.Y.Hu & Barretto"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0134918-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8apazz
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cheirostylis goldschmidtiana Schltr."
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0134873-230224095556074/datasets/export for details.
DOI: 10.15468/dl.m68nym
title: Occurrence Download

description: A dataset listing the 2130310 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Animalia"
 ]
}

 The dataset's 2130310 records were derived from 32700 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0216947-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-233128
title: United National Auxiliary Convention, COGIC, syllabus, 1990

description: [description english/roman)] United National Auxiliary Convention, Church of God in Christ (COGIC), syllabus, "UNAC in training, vo. VI", San Francisco, California, 1990 July 2-7.
DOI: 10.25582/data.2023-04.2786551/2338641
title: Data for EMSL Project 51287 from April 2023