cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.qkka3t
title: Occurrence Download

description: A dataset containing 8197 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-119.11349 33.22076,-116.74357 33.22076,-116.74357 34.72444,-119.11349 34.72444,-119.11349 33.22076))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Canis latrans"
 ]
}

 The dataset includes 8197 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011528-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25574/31433
title: Chandra X-ray Observatory ObsId 31433

description: The Long-Term X-ray Variability of Broad Absorption Line Quasars
DOI: 10.15468/dl.3vu68n
title: Occurrence Download

description: A dataset containing 1288 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Alyssum montanum, Alyssum montanum, Alyssum montanum, Alyssum montanum, Alyssum montanum, Alyssum gmelinii)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 1288 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011529-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25824/redu/tuehyd
title: Dados da pesquisa "Políticas da terra: uma etnografia da luta e da produção na Comuna da Terra Irmã Alberta (São Paulo/SP)"

description: Conjunto de dados referente à pesquisa "Políticas da terra: uma etnografia da luta e da produção na Comuna da Terra Irmã Alberta (São Paulo - SP)". A pesquisa baseou-se numa etnografia de campo junto a agricultores e agricultoras, onde foram aplicados os métodos da observação participante, realização de entrevistas, montagem de acervo de fotos e mapas pessoais e levantamento bibliográfico de outras produções acadêmicas já realizadas no território. Os dados aqui expostos compreendem o acervo de fotos e mapas e o levantamento bibliográfico realizado. O caderno de campo e os arquivos das entrevistas não compõem este conjunto por questões éticas, tendo em vista que contêm informações sensíveis associadas à identidade dos participantes da pesquisa, às quais foi garantido sigilo mediante comum acordo entre a pesquisadora, os participantes e o CEP-CHS da UNICAMP.
DOI: 10.21954/ou.rd.31156906
title: Evaluating the UK Parliament's Career Transition Scheme for Departing MPs: Dataset of transcripts from the interviews

description: This data set arises from a research project I conducted while I was a Parliamentary Academic Fellow at the Members’ and Members’ Staff Services Team at the UK Parliament in 2025. My project contains Parliamentary information licensed under the Open Parliament Licence v3.0This dataset is used in an article accepted for publication in the journal Parliamentary Affairs titled: ‘Evaluating the UK Parliament’s Career Transition Scheme for Departing MPs’. A copy of this paper can be accessed through ORO. The abstract of this paper states: 'Leaving Parliament is not easy. The way that MPs exit Parliament matters because it can deter others from being an MP. The UK Parliament introduced a Career Transition Scheme (CTS) after the 2024 general election to aid this transition. In this paper I make a theoretical contribution to the literature on exiting political office by extending a typology developed for ex-leaders to cover departing MPs more generally and make an empirical contribution by providing the first detailed empirical study into the CTS and creating an original and unique dataset on the CTS. I interviewed 16 ex-MPs eligible for the CTS and asked whether the CTS provided them with appropriate support. Overall, the CTS did provide effective support. There was a desire among interviewees for tailored support to reflect the unique challenges faced by ex-MPs'. The data set consists of anonymised transcripts of interviews I conducted with 16 ex-MPs from the UK Parliament who were eligible for the Career Transition Scheme. Interviews were conducted in the summer of 2025.The transcripts have been anonymised and all identifying marks have been removed.In the data set, the interviewees are listed numerically. Gender markers have been removed to aid de-identification.Please note that one ex-MP did not give permission for their transcript to be deposited at ORDO. Thus, Interviewee 8 is missing from this dataset.
DOI: 10.21954/ou.rd.31156906.v1
title: Evaluating the UK Parliament's Career Transition Scheme for Departing MPs: Dataset of transcripts from the interviews

description: This data set arises from a research project I conducted while I was a Parliamentary Academic Fellow at the Members’ and Members’ Staff Services Team at the UK Parliament in 2025. My project contains Parliamentary information licensed under the Open Parliament Licence v3.0This dataset is used in an article accepted for publication in the journal Parliamentary Affairs titled: ‘Evaluating the UK Parliament’s Career Transition Scheme for Departing MPs’. A copy of this paper can be accessed through ORO. The abstract of this paper states: 'Leaving Parliament is not easy. The way that MPs exit Parliament matters because it can deter others from being an MP. The UK Parliament introduced a Career Transition Scheme (CTS) after the 2024 general election to aid this transition. In this paper I make a theoretical contribution to the literature on exiting political office by extending a typology developed for ex-leaders to cover departing MPs more generally and make an empirical contribution by providing the first detailed empirical study into the CTS and creating an original and unique dataset on the CTS. I interviewed 16 ex-MPs eligible for the CTS and asked whether the CTS provided them with appropriate support. Overall, the CTS did provide effective support. There was a desire among interviewees for tailored support to reflect the unique challenges faced by ex-MPs'. The data set consists of anonymised transcripts of interviews I conducted with 16 ex-MPs from the UK Parliament who were eligible for the Career Transition Scheme. Interviews were conducted in the summer of 2025.The transcripts have been anonymised and all identifying marks have been removed.In the data set, the interviewees are listed numerically. Gender markers have been removed to aid de-identification.Please note that one ex-MP did not give permission for their transcript to be deposited at ORDO. Thus, Interviewee 8 is missing from this dataset.
DOI: 10.25574/41752
title: Chandra X-ray Observatory ObsId 41752
DOI: 10.15468/dl.d2dtpy
title: Occurrence Download

description: A dataset containing 822 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Koenigia campanulata",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 822 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011530-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18481801
title: LINF_320028600

description: ATP-dependent RNA helicase; Leishmania infantum (strain JPCM5)
DOI: 10.5281/zenodo.18481800
title: LINF_320028600

description: ATP-dependent RNA helicase; Leishmania infantum (strain JPCM5)
DOI: 10.5281/zenodo.18439809
title: Field telemetry dataset and analysis code for decentralized PV–battery resilience in Myanmar

description: # Decentralized Solar PV–Battery Resilience in Myanmar (Field Telemetry)

This repository contains the **dataset, Python scripts, and figure generators**supporting the manuscript:

**“Decentralized solar PV systems for energy resilience: lessons from Myanmar’s post-2021 political turmoil”**

The study uses **5-minute, one-year telemetry data** from a commercial-scalePV–battery system in Myanmar to quantify energy resilience, avoided emissions,economic performance, and seasonal variability under prolonged grid instability.

---

## 1. Dataset

### File- `data/Combine_Data.xlsx`

### DescriptionTime-stamped inverter and meter telemetry exported from a SOLARMAN-type platform.

### Key Columns- `Updated Time`- `Production Power (W)`- `Consumption Power (W)`- `Charging Power (W)`- `Discharging Power (W)`- `Purchasing Power (W)`- `Feed-in Power (W)`

Sampling interval is **5 minutes** (modal timestep detected automatically).

---

## 2. Software Requirements

- Python ≥ 3.9- Required packages:  - numpy  - pandas  - matplotlib- Optional (for statistical tests):  - scipy

All scripts run on standard Python environments (tested on Windows/Linux).

---

## 3. Scripts Overview

### 3.1 Avoided CO₂ emissions**`avoided_co2_by_source_embedded.py`**- Computes avoided CO₂ from self-consumed PV- Separates grid-present vs grid-absent (diesel-displaced) hours- Outputs annual and monthly CSV summaries

---

### 3.2 Economic performance and payback**`make_table_economics.py`**- Computes LCOE, annual savings, and payback period- Uses Myanmar tariff blocks and diesel benchmarks- Exports CSV, Excel, and LaTeX tables for publication

---

### 3.3 Seasonal PV performance**`daily_pv_energy_by_season.py`**- Daily PV energy distributions for Dry vs Wet seasons- Produces publication-quality histograms

**`daily_pv_energy_by_season_stats.py`**- Bootstrap 95% confidence intervals- Effect sizes (Cliff’s delta)- Optional Welch, Mann–Whitney, and KS tests

---

### 3.4 Compact statistical summary with 95% CIs**`stat_analysis_compact.py`**- Daily PV share (Dry vs Wet)- Peak-hour coverage by PV+Battery- Battery round-trip efficiency- Outputs journal-ready figures (PDF/PNG)

---

### 3.5 Grid reliability context (Monte Carlo)**`reliability_distributions.py`**- Monte Carlo availability modeling (MTTF/MTTR)- Used for contextual comparison of pre- and post-2021 grid conditions- Does not affect PV–battery telemetry calculations

---

### 3.6 Daily power flow visualization**`daily_power_flows_best_low.py`**- High-PV vs low-PV day comparison- Renewable Energy–style figures (vector PDF + 600 dpi raster)

---

## 4. How to Run

From the repository root:

```bashpython scripts/avoided_co2_by_source_embedded.pypython scripts/make_table_economics.pypython scripts/daily_pv_energy_by_season_stats.pypython scripts/stat_analysis_compact.py
DOI: 10.5281/zenodo.18439810
title: Field telemetry dataset and analysis code for decentralized PV–battery resilience in Myanmar

description: # Decentralized Solar PV–Battery Resilience in Myanmar (Field Telemetry)

This repository contains the **dataset, Python scripts, and figure generators**supporting the manuscript:

**“Decentralized solar PV systems for energy resilience: lessons from Myanmar’s post-2021 political turmoil”**

The study uses **5-minute, one-year telemetry data** from a commercial-scalePV–battery system in Myanmar to quantify energy resilience, avoided emissions,economic performance, and seasonal variability under prolonged grid instability.

---

## 1. Dataset

### File- `data/Combine_Data.xlsx`

### DescriptionTime-stamped inverter and meter telemetry exported from a SOLARMAN-type platform.

### Key Columns- `Updated Time`- `Production Power (W)`- `Consumption Power (W)`- `Charging Power (W)`- `Discharging Power (W)`- `Purchasing Power (W)`- `Feed-in Power (W)`

Sampling interval is **5 minutes** (modal timestep detected automatically).

---

## 2. Software Requirements

- Python ≥ 3.9- Required packages:  - numpy  - pandas  - matplotlib- Optional (for statistical tests):  - scipy

All scripts run on standard Python environments (tested on Windows/Linux).

---

## 3. Scripts Overview

### 3.1 Avoided CO₂ emissions**`avoided_co2_by_source_embedded.py`**- Computes avoided CO₂ from self-consumed PV- Separates grid-present vs grid-absent (diesel-displaced) hours- Outputs annual and monthly CSV summaries

---

### 3.2 Economic performance and payback**`make_table_economics.py`**- Computes LCOE, annual savings, and payback period- Uses Myanmar tariff blocks and diesel benchmarks- Exports CSV, Excel, and LaTeX tables for publication

---

### 3.3 Seasonal PV performance**`daily_pv_energy_by_season.py`**- Daily PV energy distributions for Dry vs Wet seasons- Produces publication-quality histograms

**`daily_pv_energy_by_season_stats.py`**- Bootstrap 95% confidence intervals- Effect sizes (Cliff’s delta)- Optional Welch, Mann–Whitney, and KS tests

---

### 3.4 Compact statistical summary with 95% CIs**`stat_analysis_compact.py`**- Daily PV share (Dry vs Wet)- Peak-hour coverage by PV+Battery- Battery round-trip efficiency- Outputs journal-ready figures (PDF/PNG)

---

### 3.5 Grid reliability context (Monte Carlo)**`reliability_distributions.py`**- Monte Carlo availability modeling (MTTF/MTTR)- Used for contextual comparison of pre- and post-2021 grid conditions- Does not affect PV–battery telemetry calculations

---

### 3.6 Daily power flow visualization**`daily_power_flows_best_low.py`**- High-PV vs low-PV day comparison- Renewable Energy–style figures (vector PDF + 600 dpi raster)

---

## 4. How to Run

From the repository root:

```bashpython scripts/avoided_co2_by_source_embedded.pypython scripts/make_table_economics.pypython scripts/daily_pv_energy_by_season_stats.pypython scripts/stat_analysis_compact.py
DOI: 10.5281/zenodo.18221563
title: Detectability of multi-centennial AMOC variations

description: Climate model output of the iLOVECLIM model. The repository contains:

 



Data on 2000-yr long PI control experiment and 5 1000yr long PI hosing experiments used in the publication:

1: PI

2: PI_hosing_1000yrs_0.03Sv_10yr_ave

3: PI_hosing_1000yrs_0.05Sv_10yr_ave

4: PI_hosing_1000yrs_0.08Sv_10yr_ave

5: PI_hosing_1000yrs_0.1Sv_10yr_ave

6: PI_hosing_1000yrs_0.14Sv_10yr_ave

 

Three different data files for every experiment

amoc: 10yr-mean maximum meridional overturning streamfunction in the Atlantic below 500m

CLIO3: 10yr-mean 4D ocean output (temperature (temp; degrees C), zonal velocity (u; m/s), meridional velocity (v; m/s), Protactinium particle values (papart), Thorium particle values (thpart)

d13C: 10yr-mean 4D ocean output (oc13  and odic)

 

Calculate d13C using: oc13/odic

Calculate Pa/Th using: papart/thpart
DOI: 10.5281/zenodo.18221564
title: Detectability of multi-centennial AMOC variations

description: Climate model output of the iLOVECLIM model. The repository contains:

 



Data on 2000-yr long PI control experiment and 5 1000yr long PI hosing experiments used in the publication:

1: PI

2: PI_hosing_1000yrs_0.03Sv_10yr_ave

3: PI_hosing_1000yrs_0.05Sv_10yr_ave

4: PI_hosing_1000yrs_0.08Sv_10yr_ave

5: PI_hosing_1000yrs_0.1Sv_10yr_ave

6: PI_hosing_1000yrs_0.14Sv_10yr_ave

 

Three different data files for every experiment

amoc: 10yr-mean maximum meridional overturning streamfunction in the Atlantic below 500m

CLIO3: 10yr-mean 4D ocean output (temperature (temp; degrees C), zonal velocity (u; m/s), meridional velocity (v; m/s), Protactinium particle values (papart), Thorium particle values (thpart)

d13C: 10yr-mean 4D ocean output (oc13  and odic)

 

Calculate d13C using: oc13/odic

Calculate Pa/Th using: papart/thpart
DOI: 10.5281/zenodo.18481812
title: Jarrita C Tipo 1 - BAYRA

description: Jarrita (23-1291-P.50) proveniente de la excavación de Bayra (Vera, Almería). Modelada a torno y cocida en un ambiente oxidante. Presenta una forma cerrada de tamaño medio. Tiene labio redondeado y borde recto que continua en un cuello corto. Presenta un hombro marcado y un cuerpo globular que remata con una base convexa, la cuál tiene un ligero repié. Conserva ambas asas, de sección triangular. Presenta una decoración dáctil en color negro en cuello y hombro.



Cronología: Siglos XII - XIII


Modelo creado y estudiado como parte del TFM de Antonio Ibarra Medina
DOI: 10.5281/zenodo.18481811
title: Jarrita C Tipo 1 - BAYRA

description: Jarrita (23-1291-P.50) proveniente de la excavación de Bayra (Vera, Almería). Modelada a torno y cocida en un ambiente oxidante. Presenta una forma cerrada de tamaño medio. Tiene labio redondeado y borde recto que continua en un cuello corto. Presenta un hombro marcado y un cuerpo globular que remata con una base convexa, la cuál tiene un ligero repié. Conserva ambas asas, de sección triangular. Presenta una decoración dáctil en color negro en cuello y hombro.



Cronología: Siglos XII - XIII


Modelo creado y estudiado como parte del TFM de Antonio Ibarra Medina
DOI: 10.5281/zenodo.18335350
title: Exploration of Pareto-preserving search-space transformations in multi-objective test functions - Reproducibility

description: This repository contains the reproducibility files for the paper "Exploration of Pareto-preserving search-space transformations in multi-objective test functions"


It is structured as follows:

- basefunctions: File containing the definitions of the transformations, used across the different parts of data collection and analysis.

- Visualize_transforms: A notebook which can be used to visualize the transformations, used to generate figures 1 and 2 from the paper

- Data_collection: This contains the code used to run the experiments (running the algorithms on the different problem instances).

- outrot, outab, outab_obj: These 3 folders contain the full set of trajectory data from the experiments. To use them during the processing and visualization, place them in a single folder, making sure to keep their names.

- Data_extraction: This is the file used to process the raw data into the used hypervolume measures

- Processed_data: This is the processed version of the trajectory data, used for the visualizations

- Visualization: This notebook is used to create the figures as shown in the paper, as well as similar figures for other settings

- Figures: This contains alternative versions of the figures shown in the paper (e.g. for other functions)


# Reproduction steps


## Data collection


To re-run the data collection, you should make use of the code in the corresponding directory. You should install the listed requirements, and then use the 'experiment.py' file to run the scripts. Note: since rpy2 is one of the used packages, this requires R to be installed. Within this R environment, the package 'smoof' needs to be available, which can be installed using (in the R-terminal):

`install.packages('smoof')`


'experiment.py' uses the RunExperiment package, so it can be run in parallel or sequentially based on the chosen settings (see the readme in the RunExperiments subfolder for more information). By default, it will run the BetaCDF based settings, change the main-function to run the rotation version instead.

Running the data collection will save the results to the folder specified in 'experiment.py', so this should be set to the desired location.


## Data processing


Since large parts of our analysis use the hypervolume measure rather than the trajectory itself, we provide a script for the processing of the data files. Note that the used directories are set as global variables at the start of the file, so these should be modified to the respective folders.


## Visualization


For the visualization, there are two parts:

- For the first 2 figures showing the transformation on a simple 2D grid, you can use the Vizualize_transforms notebook

- For the remaining figures (including additional versions), you can use the Visualizations notebook. This notebook is anotated to provide further instructions.


## Questions


In case of questions, please dont hesitate to contact us at: Anoynmized during review process
DOI: 10.5281/zenodo.18335349
title: Exploration of Pareto-preserving search-space transformations in multi-objective test functions - Reproducibility

description: This repository contains the reproducibility files for the paper "Exploration of Pareto-preserving search-space transformations in multi-objective test functions"


It is structured as follows:

- basefunctions: File containing the definitions of the transformations, used across the different parts of data collection and analysis.

- Visualize_transforms: A notebook which can be used to visualize the transformations, used to generate figures 1 and 2 from the paper

- Data_collection: This contains the code used to run the experiments (running the algorithms on the different problem instances).

- outrot, outab, outab_obj: These 3 folders contain the full set of trajectory data from the experiments. To use them during the processing and visualization, place them in a single folder, making sure to keep their names.

- Data_extraction: This is the file used to process the raw data into the used hypervolume measures

- Processed_data: This is the processed version of the trajectory data, used for the visualizations

- Visualization: This notebook is used to create the figures as shown in the paper, as well as similar figures for other settings

- Figures: This contains alternative versions of the figures shown in the paper (e.g. for other functions)


# Reproduction steps


## Data collection


To re-run the data collection, you should make use of the code in the corresponding directory. You should install the listed requirements, and then use the 'experiment.py' file to run the scripts. Note: since rpy2 is one of the used packages, this requires R to be installed. Within this R environment, the package 'smoof' needs to be available, which can be installed using (in the R-terminal):

`install.packages('smoof')`


'experiment.py' uses the RunExperiment package, so it can be run in parallel or sequentially based on the chosen settings (see the readme in the RunExperiments subfolder for more information). By default, it will run the BetaCDF based settings, change the main-function to run the rotation version instead.

Running the data collection will save the results to the folder specified in 'experiment.py', so this should be set to the desired location.


## Data processing


Since large parts of our analysis use the hypervolume measure rather than the trajectory itself, we provide a script for the processing of the data files. Note that the used directories are set as global variables at the start of the file, so these should be modified to the respective folders.


## Visualization


For the visualization, there are two parts:

- For the first 2 figures showing the transformation on a simple 2D grid, you can use the Vizualize_transforms notebook

- For the remaining figures (including additional versions), you can use the Visualizations notebook. This notebook is anotated to provide further instructions.


## Questions


In case of questions, please dont hesitate to contact us at: Anoynmized during review process
DOI: 10.15468/dl.rsbr5a
title: Occurrence Download

description: A dataset containing 2600358212 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Animalia",
 "BasisOfRecord is Human Observation",
 "Year is greater than or equal to 1990",
 "Year is less than or equal to 2026"
 ]
}

 The dataset includes 2600358212 records from 29761 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011013-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/8y9-xbz-3jh
title: Pseudoniphargus Chevreux, 1901

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ww8-5hk-n9x
title: Gammarus duebeni Lilljeborg, 1852

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.