cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25549/inscriptifact-ouc110599518
title: Persepolis Fortification Elamite Tablet 0858, PF 0858

description: [Text Description:] Administrative text with seals PFS 0006 ❧ [Text or Publication Number Note:] PF 0858 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform, Aramaic ❧ [Physical Object Description:] Clay tablet with cuneiform text, Aramaic ink inscription. Seal impression PFS 0006 left edge. ❧ [Physical Object Note:] Some cracks, especially on reverse.
DOI: 10.15468/dl.9jgh2q
title: Occurrence Download

description: A dataset containing 4829 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Teucrium scorodonia L.",
 "Geometry POLYGON((-8.00174339329464 54.1998526673513, 0.202120938926891 54.1998526673513, 0.202120938926891 61.2628565887411, -8.00174339329464 61.2628565887411, -8.00174339329464 54.1998526673513))",
 "CoordinateUncertaintyInMeters is 0,101",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 4829 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010930-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ta8ezx
title: Occurrence Download

description: A dataset containing 182022 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is Europe",
 "TaxonKey is Vespa velutina Lepeletier, 1836",
 "Year 2003-2025"
 ]
}

 The dataset includes 182022 records from 809 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010917-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.24t7yq
title: Occurrence Download

description: A dataset containing 45 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elaeocarpus photiniifolius Hook. & Arn.",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 45 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0206325-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hu5epz
title: Occurrence Download

description: A dataset containing 78 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "InstitutionCode is one of (inpa, smf, fmnh, smns, uf, huj)",
 "TaxonKey is Astatotilapia flaviijosephi (Lortet, 1883)"
 ]
}

 The dataset includes 78 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012105-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bs66cd
title: Occurrence Download

description: A dataset containing 129609 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "Country is one of (Colombia)",
 {
 "or" : [
 "StateProvince is guaviare",
 "StateProvince is guaviare department, san jose",
 "StateProvince is guaviare department",
 "StateProvince ~guaviare",
 "StateProvince is guaviare dept."
 ]
 }
 ]
 }
 ]
}

 The dataset includes 129609 records from 158 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012085-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qjcp0v
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 32 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025732-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dysymm
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sorbus randaiensis (Hayata) Koidz.",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0206330-220831081235567/datasets/export for details.
DOI: 10.15468/dl.zbcvss
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Boehmeria hatusiimae Satake",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0206328-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.acxnsa
title: Occurrence Download

description: A dataset containing 149 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is New Zealand",
 "TaxonKey is Olearia fragrantissima Petrie"
 ]
}

 The dataset includes 149 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003056-171219132708484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a5nun9
title: Occurrence Download

description: A dataset containing 68 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Propagación de especies leñosas y herbáceas en categoría de conservación. Proyecto Desarrollo Mantoverde (RCA Nº16/2018, MMA)"
 ]
}

 The dataset includes 68 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012271-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t3jt4r
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pellionia yosiei H.Hara",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 9 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0206319-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bpqgmi
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003528-171113114016250/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.aa6evz
title: Occurrence Download

description: A dataset containing 1578327480 species occurrences available in GBIF matching the query:
{
 "Year" : [
 "1990-2021"
 ]
}

 The dataset includes 1578327480 records from 29118 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022539-210914110416597/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.28685312.v29
title: Supplementary Material for: Evidence on protective factors for dementia and cognitive impairment in older adults: an umbrella review

description: Introduction: Dementia represents a global public health challenge, and no conclusive evidence exists on how to prevent its onset or at least reduce the risk. This umbrella review aims to identify interventions or exposures that can reduce the incidence of dementia or cognitive impairment.
Methods: A search of meta-analyses and systematic reviews published from 2018 to 2025 (January) was conducted across four databases. Articles assessing associations between interventions or exposures to protective factors and incidence of dementia or cognitive decline in non-demented older adults were selected.
Results: Out of 6,324 articles, 177 underwent full-text screening, and 17 were included. They showed that psychosocial factors (social engagement, purpose in life, life satisfaction, and cognitive reserve) and influenza vaccination protect against dementia. Evidence on antihypertensives is promising, while evidence on low-dose aspirin is promising yet inconclusive. No clear results emerged for both single- and multi-domain lifestyle interventions.
Conclusions: These results offer new insight on potential targets for dementia prevention, a global public health priority. In particular, favorable psychosocial factors and influenza vaccination, undervalued in the scientific debate on dementia prevention, emerged as promising and feasible targets for preventive initiatives. Future research should focus on fine-tuning interventions to promote brain health, exploring their underlying mechanisms, and developing targeted and personalized strategies for specific population subgroups.
DOI: 10.6084/m9.figshare.28920728
title: Supplementary Material for: Exploring the Link between Hypertension and Cerebral White Matter Changes in Chronic Kidney Disease

description: Background : Patients diagnosed with chronic kidney disease (CKD) are at a higher risk of encephalopathy, a condition exacerbated by the presence of various chronic diseases. Hypertension is a significant risk factor for brain damage in the general population but is limitedly discussed in patients with CKD. Brain Magnetic Resonance Imaging (MRI) is an excellent tool for evaluating cerebral white matter lesions. Most previous studies showed the association between hypertension and cerebral white matter lesions in the general population but were less focused on CKD patients. Therefore, the present study aims to investigate the effect of hypertension on the cerebral white matter lesions of brain MRI in patients with CKD.
Methods : In this retrospective study, we enrolled 1,749 CKD patients who underwent brain MRIs to evaluate their brain lesions in Kaohsiung Medical University Hospital. The cerebral white matter hyperintensities (WMHs) on MRI were evaluated according to the Fazekas scale, including separate periventricular and deep white matter lesions from grade 0 to grade 3. The multivariable ordinal regression model was analyzed to determine the independent association between hypertension or blood pressure and cerebral WMHs with adjustment of controlling age, sex, education, comorbidities (hyperlipidemia, cerebrovascular disease, chronic heart failure), laboratory data (hemoglobin, albumin, triglyceride, estimated glomerular filtration rate).
Results : Hypertension was associated with the Fazekas scale of periventricular lesions in multivariable-adjusted ordinal regression analysis (odds ratio [OR] 1.63, 95% confidence interval [CI] 1.15-2.30) after full adjustment. However, the hypertension comorbidities did not associate with the Fazekas scale of deep white matter lesions in the fully adjusted model (OR 1.24, 95% CI [0.89-1.75]). A positive association between blood pressure (per 10 mmHg increase) and the Fazekas scale was mainly on diastolic blood pressure (DBP) rather than systolic blood pressure (SBP). 
Conclusions : In CKD patients, hypertension was associated with brain white matter damage, in particular, Fazekas scale of periventricular lesions. Further study is needed to evaluate adequate blood pressure control to decrease the risk of brain damage in CKD patients.
DOI: 10.6084/m9.figshare.28920728.v1
title: Supplementary Material for: Exploring the Link between Hypertension and Cerebral White Matter Changes in Chronic Kidney Disease

description: Background : Patients diagnosed with chronic kidney disease (CKD) are at a higher risk of encephalopathy, a condition exacerbated by the presence of various chronic diseases. Hypertension is a significant risk factor for brain damage in the general population but is limitedly discussed in patients with CKD. Brain Magnetic Resonance Imaging (MRI) is an excellent tool for evaluating cerebral white matter lesions. Most previous studies showed the association between hypertension and cerebral white matter lesions in the general population but were less focused on CKD patients. Therefore, the present study aims to investigate the effect of hypertension on the cerebral white matter lesions of brain MRI in patients with CKD.
Methods : In this retrospective study, we enrolled 1,749 CKD patients who underwent brain MRIs to evaluate their brain lesions in Kaohsiung Medical University Hospital. The cerebral white matter hyperintensities (WMHs) on MRI were evaluated according to the Fazekas scale, including separate periventricular and deep white matter lesions from grade 0 to grade 3. The multivariable ordinal regression model was analyzed to determine the independent association between hypertension or blood pressure and cerebral WMHs with adjustment of controlling age, sex, education, comorbidities (hyperlipidemia, cerebrovascular disease, chronic heart failure), laboratory data (hemoglobin, albumin, triglyceride, estimated glomerular filtration rate).
Results : Hypertension was associated with the Fazekas scale of periventricular lesions in multivariable-adjusted ordinal regression analysis (odds ratio [OR] 1.63, 95% confidence interval [CI] 1.15-2.30) after full adjustment. However, the hypertension comorbidities did not associate with the Fazekas scale of deep white matter lesions in the fully adjusted model (OR 1.24, 95% CI [0.89-1.75]). A positive association between blood pressure (per 10 mmHg increase) and the Fazekas scale was mainly on diastolic blood pressure (DBP) rather than systolic blood pressure (SBP). 
Conclusions : In CKD patients, hypertension was associated with brain white matter damage, in particular, Fazekas scale of periventricular lesions. Further study is needed to evaluate adequate blood pressure control to decrease the risk of brain damage in CKD patients.
DOI: 10.15468/dl.ak2dxp
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is Asia",
 "Country is Philippines",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rhincodon typus Smith, 1828",
 "Year is 2024"
 ]
}

 The dataset includes 21 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011473-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xx8njg
title: Occurrence Download

description: A dataset containing 92870 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Dryas octopetala L."
 ]
}

 The dataset includes 92870 records from 386 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011440-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mh6nu4
title: Occurrence Download

description: A dataset containing 44980 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Zoopagomycetes"
 ]
}

 The dataset includes 44980 records from 218 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011472-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10814243
title: AMIRIS Examples

description: Example configurations built for AMIRIS 2.0.0 See the individual licence information in the respective scenarios.

How-to

Follow these steps to start using AMIRIS:



Clone AMIRIS.

Clone this project to the input folder in your AMIRIS project repository.

Compile the binary input file by using FAME-Io (makeFameRunConfig -f <./path/to/scenario.yaml>). Alternatively, you may download pre-compiled .pb input files here.

Execute AMIRIS.

Convert the binary results file by using FAME-Io (convertFameResults -f <./path/to/protobuf_file.pb>).


Sample scenarios



Simple: simple scenario running for one day with dummy data

Austria2019: historical simulation of Austrian day-ahead market in 2019

Germany2015: historical simulation of German day-ahead market in 2015

Germany2016: historical simulation of German day-ahead market in 2016

Germany2017: historical simulation of German day-ahead market in 2017

Germany2018: historical simulation of German day-ahead market in 2018

Germany2019: historical simulation of German day-ahead market in 2019


Citing AMIRIS Examples

If you use AMIRIS Examples in your scientific work please cite:

Kristina Nienhaus, Christoph Schimeczek, Ulrich Frey, Evelyn Sperber, Seyedfarzad Sarfarazi, Felix Nitsch, Johannes Kochems & A. Achraf El Ghazi (2023). AMIRIS Examples. Zenodo. doi: 10.5281/zenodo.7789049 

Acknowledgments

We thank those who contributed to enhancing the AMIRIS examples:



v1.1 Dr. Tom Bauermann, for pointing out errors in historical fuel prices
DOI: 10.5281/zenodo.10817787
title: AMIRIS Examples

description: Example configurations built for AMIRIS 2.0.0 See the individual licence information in the respective scenarios.

How-to

Follow these steps to start using AMIRIS:



Clone AMIRIS.

Clone this project to the input folder in your AMIRIS project repository.

Compile the binary input file by using FAME-Io (makeFameRunConfig -f <./path/to/scenario.yaml>). Alternatively, you may download pre-compiled .pb input files here.

Execute AMIRIS.

Convert the binary results file by using FAME-Io (convertFameResults -f <./path/to/protobuf_file.pb>).


Sample scenarios



Simple: simple scenario running for one day with dummy data

Austria2019: historical simulation of Austrian day-ahead market in 2019

Germany2015: historical simulation of German day-ahead market in 2015

Germany2016: historical simulation of German day-ahead market in 2016

Germany2017: historical simulation of German day-ahead market in 2017

Germany2018: historical simulation of German day-ahead market in 2018

Germany2019: historical simulation of German day-ahead market in 2019


Citing AMIRIS Examples

If you use AMIRIS Examples in your scientific work please cite:

Kristina Nienhaus, Christoph Schimeczek, Ulrich Frey, Evelyn Sperber, Seyedfarzad Sarfarazi, Felix Nitsch, Johannes Kochems & A. Achraf El Ghazi (2023). AMIRIS Examples. Zenodo. doi: 10.5281/zenodo.7789049 

Acknowledgments

We thank those who contributed to enhancing the AMIRIS examples:



v1.1 Dr. Tom Bauermann, for pointing out errors in historical fuel prices
DOI: 10.5281/zenodo.13881479
title: AMIRIS Examples

description: Examples

Example configurations built for AMIRIS 3.0.0 See the individual licence information in the respective scenarios.

How-to

Please use AMIRIS-Py to run AMIRIS-Examples. Follow these very few instructions in its README to have AMIRIS running in no time.

Sample scenarios



Simple: simple scenario running for one day with dummy data

SimpleCoupled: simple scenario coupling two markets running for one day with dummy data

Austria2019: historical simulation of Austrian day-ahead market in 2019

Germany2015: historical simulation of German day-ahead market in 2015

Germany2016: historical simulation of German day-ahead market in 2016

Germany2017: historical simulation of German day-ahead market in 2017

Germany2018: historical simulation of German day-ahead market in 2018

Germany2019: historical simulation of German day-ahead market in 2019


Citing AMIRIS Examples

If you use AMIRIS Examples in your scientific work please cite:

Kristina Nienhaus, Christoph Schimeczek, Ulrich Frey, Evelyn Sperber, Seyedfarzad Sarfarazi, Felix Nitsch, Johannes Kochems & A. Achraf El Ghazi (2023). AMIRIS Examples. Zenodo. doi: 10.5281/zenodo.7789049 

Acknowledgments

We thank those who contributed to enhancing the AMIRIS examples:



v1.1 Dr. Tom Bauermann, for pointing out errors in historical fuel prices
DOI: 10.5281/zenodo.14765836
title: AMIRIS Examples

description: Example configurations built for AMIRIS 3.3.0 See the individual licence information in the respective scenarios.

How-to

Please use AMIRIS-Py to run AMIRIS-Examples. Follow these very few instructions in its README to have AMIRIS running in no time.

Sample scenarios



Simple: simple scenario running for one day with dummy data

SimpleCoupled: simple scenario coupling two markets running for one day with dummy data

Austria2019: historical simulation of Austrian day-ahead market in 2019

Germany2015: historical simulation of German day-ahead market in 2015

Germany2016: historical simulation of German day-ahead market in 2016

Germany2017: historical simulation of German day-ahead market in 2017

Germany2018: historical simulation of German day-ahead market in 2018

Germany2019: historical simulation of German day-ahead market in 2019


Citing AMIRIS Examples

If you use AMIRIS Examples in your scientific work please cite:

Kristina Nienhaus, Christoph Schimeczek, Ulrich Frey, Evelyn Sperber, Seyedfarzad Sarfarazi, Felix Nitsch, Johannes Kochems & A. Achraf El Ghazi (2023). AMIRIS Examples. Zenodo. doi: 10.5281/zenodo.7789049 

Acknowledgments

We thank those who contributed to enhancing the AMIRIS examples:



v1.1 Dr. Tom Bauermann, for pointing out errors in historical fuel prices
DOI: 10.5281/zenodo.14906793
title: AMIRIS Examples

description: Example configurations built for AMIRIS 3.4.0 See the individual licence information in the respective scenarios.

How-to

Please use AMIRIS-Py to run AMIRIS-Examples. Follow these very few instructions in its README to have AMIRIS running in no time.

Sample scenarios



Simple: simple scenario running for one day with dummy data

SimpleCoupled: simple scenario coupling two markets running for one day with dummy data

Austria2019: historical simulation of Austrian day-ahead market in 2019

Germany2015: historical simulation of German day-ahead market in 2015

Germany2016: historical simulation of German day-ahead market in 2016

Germany2017: historical simulation of German day-ahead market in 2017

Germany2018: historical simulation of German day-ahead market in 2018

Germany2019: historical simulation of German day-ahead market in 2019


Citing AMIRIS Examples

If you use AMIRIS Examples in your scientific work please cite:

Kristina Nienhaus, Christoph Schimeczek, Ulrich Frey, Evelyn Sperber, Seyedfarzad Sarfarazi, Felix Nitsch, Johannes Kochems & A. Achraf El Ghazi (2023). AMIRIS Examples. Zenodo. doi: 10.5281/zenodo.7789049 

Acknowledgments

We thank those who contributed to enhancing the AMIRIS examples:



v1.1 Dr. Tom Bauermann, for pointing out errors in historical fuel prices