cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15301967
title: Dataset for paper: Trade-offs among network investment, reconfiguration, and local flexibility markets in active distribution networks

description: 1. TitleDataset for paper: Trade-offs among network investment, reconfiguration, and local flexibility markets in active distribution networks

2. DescriptionThis dataset provides all the input data required to reproduce the case study and simulations presented in the associated paper. It includes detailed information on network parameters, investment candidates, yearly load and generation profiles, power loss costs, and flexibility service provider (FSP) data, including their bid information. Additional inputs and parameters, such as the selection and characteristics of the representative days, are fully described within the paper itself. All data sources referenced in this dataset are explicitly cited and discussed in the paper.

3. Folder and File Structure

- Network_and_Investment_Inputs/  - Network_Parameters_Invesment_Candidates.xlsx – Detailed parameters for all network elements including     lines, transformers, buses, switches, load, and generators. Also, economic information about investment candidates (lines and transformers) is included. 

- Load_and_Generation_Profiles_Inputs/  - Load_Generation_Yearly_Profiles.xlsx – Hourly load (residential & industrial) and generation (PV and Wind) profiles are included. The data related to 2019 is considered in the paper. 

- Flexibility_Providers_Inputs/  - FSP_Data_and_Bids.xlsx – Flexibility Providers parameters, Flexibility offers price and quantity.

- Other_Inputs/  - Power_losses_cost.xlsx – The hourly day-ahead wholesale market prices in Spain are considered (EUR/MWh) for power losses cost.

4. Software Requirements

The optimization model was implemented using Python and Gurobi. Excel or compatible software is required to view the input files.

5. Citation

If you use this dataset, please cite:

[Valarezo et. al.]. (2025). Trade-offs among network investment, reconfiguration, and local flexibility markets in active distribution networks. [Applied Energy]. DOI: [10.5281/zenodo.15301968]
DOI: 10.5281/zenodo.15301968
title: Dataset for paper: Trade-offs among network investment, reconfiguration, and local flexibility markets in active distribution networks

description: 1. TitleDataset for paper: Trade-offs among network investment, reconfiguration, and local flexibility markets in active distribution networks

2. DescriptionThis dataset provides all the input data required to reproduce the case study and simulations presented in the associated paper. It includes detailed information on network parameters, investment candidates, yearly load and generation profiles, power loss costs, and flexibility service provider (FSP) data, including their bid information. Additional inputs and parameters, such as the selection and characteristics of the representative days, are fully described within the paper itself. All data sources referenced in this dataset are explicitly cited and discussed in the paper.

3. Folder and File Structure

- Network_and_Investment_Inputs/  - Network_Parameters_Invesment_Candidates.xlsx – Detailed parameters for all network elements including     lines, transformers, buses, switches, load, and generators. Also, economic information about investment candidates (lines and transformers) is included. 

- Load_and_Generation_Profiles_Inputs/  - Load_Generation_Yearly_Profiles.xlsx – Hourly load (residential & industrial) and generation (PV and Wind) profiles are included. The data related to 2019 is considered in the paper. 

- Flexibility_Providers_Inputs/  - FSP_Data_and_Bids.xlsx – Flexibility Providers parameters, Flexibility offers price and quantity.

- Other_Inputs/  - Power_losses_cost.xlsx – The hourly day-ahead wholesale market prices in Spain are considered (EUR/MWh) for power losses cost.

4. Software Requirements

The optimization model was implemented using Python and Gurobi. Excel or compatible software is required to view the input files.

5. Citation

If you use this dataset, please cite:

[Valarezo et. al.]. (2025). Trade-offs among network investment, reconfiguration, and local flexibility markets in active distribution networks. [Applied Energy]. DOI: [10.5281/zenodo.15301968]
DOI: 10.15468/dl.n524xn
title: Occurrence Download

description: A dataset containing 1321 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Anisarchus medius (Reinhardt, 1837)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1321 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004284-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b2qspb
title: Occurrence Download

description: A dataset containing 3530 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Observation)",
 "Country is one of (Russian Federation, Mongolia, Kazakhstan, China)",
 "Geometry POLYGON((65.8628 39.41671,107.29624 39.41671,107.29624 56.82414,65.8628 56.82414,65.8628 39.41671))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Aquila chrysaetos (Linnaeus, 1758)",
 "Year 1980-2025"
 ]
}

 The dataset includes 3530 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004276-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xhbw79
title: Occurrence Download

description: A dataset containing 154 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Archidendron kerrii (Gagnep.) I.C.Nielsen"
 ]
}

 The dataset includes 154 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004290-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57703/ce-b5dn7
title: A68 processed data

description: Atomic Force Microscopy (AFM) topographic images of sample A68 (processed data). A Flatten filter was applied (subtracting a linear function to each horizontal line of the image) to remove irregular distributed low frequency noise.
DOI: 10.57760/sciencedb.psych.00340
title: Visual Awareness Sharpens and and Accelerates Attentional Sampling
title: Visual Awareness Sharpens and and Accelerates Attentional Sampling

description: This dataset contains the data and data analysis codes of Experiments 1 and 2 in the article "Visual Awareness Sharpens and Accelerates Attentional Sampling Through Enhancing Inhibitory Neural Response in the Attention Network".
description: This dataset contains the data and data analysis codes of Experiments 1 and 2 in the article "Visual Awareness Sharpens and Accelerates Attentional Sampling Through Enhancing Inhibitory Neural Response in the Attention Network".
DOI: 10.15468/dl.cppya6
title: Occurrence Download

description: A dataset containing 174897 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Harmonised freshwater fish occurrence and abundance data for 12 federal states in Germany"
 ]
}

 The dataset includes 174897 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004291-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4121/09f3a6d1-ceac-4b09-b0bb-a8fdd9f67a1a
title: Dataset underlying the publication Not fitting in? The Dutch moped as a residual transport category

description: This dataset corresponds to the study titled Not fitting in? The Dutch moped as a residual transport category, authored by Clara Glachant and Henk-Jan Dekker. The question guiding the study is: What do evolving Dutch moped policies and public perceptions reveal about the challenges of categorising emerging transport modes? Our study combines historical and contemporary perspectives, through the analysis of readers’ letters to newspapers from the 1950s-1970s – introduction of mopeds in the Netherlands – to the 2010s-2020s – popularity of rental schemes and associated debates. To study how moped-related policies and perceptions evolved over time, we provide an archive detailing the extensive history of mopeds in the Netherlands, including (public debates about) policies. Our findings hold significance beyond the Netherlands, as they can inform policymakers on how to address other modes that straddle established categories, such as e-scooters or fatbikes, considering their potential for the mobility transition. The dataset contains the list of newspaper articles used as research material for this study.
DOI: 10.4121/09f3a6d1-ceac-4b09-b0bb-a8fdd9f67a1a.v1
title: Dataset underlying the publication Not fitting in? The Dutch moped as a residual transport category

description: This dataset corresponds to the study titled Not fitting in? The Dutch moped as a residual transport category, authored by Clara Glachant and Henk-Jan Dekker. The question guiding the study is: What do evolving Dutch moped policies and public perceptions reveal about the challenges of categorising emerging transport modes? Our study combines historical and contemporary perspectives, through the analysis of readers’ letters to newspapers from the 1950s-1970s – introduction of mopeds in the Netherlands – to the 2010s-2020s – popularity of rental schemes and associated debates. To study how moped-related policies and perceptions evolved over time, we provide an archive detailing the extensive history of mopeds in the Netherlands, including (public debates about) policies. Our findings hold significance beyond the Netherlands, as they can inform policymakers on how to address other modes that straddle established categories, such as e-scooters or fatbikes, considering their potential for the mobility transition. The dataset contains the list of newspaper articles used as research material for this study.
DOI: 10.34740/kaggle/dsv/11610960
title: World Weather Repository ( Daily Updating )
DOI: 10.5281/zenodo.15293156
title: The Coherence Evolution Model
title: CEM

description: This paper introduces the Coherence Evolution Model (CEM), a novel framework positing that prime numbers and their arithmetic correlations govern universal coherence across scales. Empirical validation of the CEM is presented through fractal analysis of the Cosmic Microwave Background (CMB), revealing a fractal dimension matching the golden ratio and excess power at prime-indexed multipoles. These findings challenge the standard inflationary paradigm. The paper addresses critiques, outlines falsifiable predictions across cosmology, biophysics, and materials science, and invites independent verification of this unifying paradigm.

* Prime Basis: The paper proposes that physical systems occupy orthogonal states indexed by primes, minimizing entropic interference. This suggests a fundamental connection between prime numbers and the building blocks of reality.

 * Zeta-Regulated Dynamics: The Riemann zeta function, which is closely related to prime numbers, governs long-range order via arithmetic quantum chaos.

 * Empirical Validation through CMB Fractality: The paper claims that the fractal dimension of the Cosmic Microwave Background (CMB) is related to the golden ratio, which arises from entanglement entropy calculated based on the distribution of prime numbers.

 * Prime-Indexed Coherence and the Horizon Problem: The uniformity of the CMB across causally disconnected regions is explained by "prime-indexed coherence," where primes carry non-local correlations.

 * Excess Power at Prime Multipoles: The paper also claims to find excess power in the CMB's harmonic spectrum at multipole moments that correspond to prime numbers.

 * Prime-Energy Correspondence: The paper explores the relationship E_p = \log p, linking prime numbers to energy scales in physical systems.

 * Mathematical Necessity: Primes are linked to the zeros of the Riemann zeta function, and the von Mangoldt function, \Lambda(n) = \log p for n = p^k, encodes the density of primes.

 * Physical Justification: The logarithm converts multiplicative primes into additive energy scales, mirroring how entropy converts multiplicative microstate counts into additive quantities.

 * Holographic Scaling: Primes are posited to label Planck-scale "atoms" of spacetime, with their spacing in energy reflecting holographic discreteness.

In essence, the paper proposes a model where prime numbers are not just mathematical abstractions but are fundamental to the structure and behavior of the universe at various scales.
DOI: 10.5281/zenodo.15293157
title: The Coherence Evolution Model
title: CEM

description: This paper introduces the Coherence Evolution Model (CEM), a novel framework positing that prime numbers and their arithmetic correlations govern universal coherence across scales. Empirical validation of the CEM is presented through fractal analysis of the Cosmic Microwave Background (CMB), revealing a fractal dimension matching the golden ratio and excess power at prime-indexed multipoles. These findings challenge the standard inflationary paradigm. The paper addresses critiques, outlines falsifiable predictions across cosmology, biophysics, and materials science, and invites independent verification of this unifying paradigm.

* Prime Basis: The paper proposes that physical systems occupy orthogonal states indexed by primes, minimizing entropic interference. This suggests a fundamental connection between prime numbers and the building blocks of reality.

 * Zeta-Regulated Dynamics: The Riemann zeta function, which is closely related to prime numbers, governs long-range order via arithmetic quantum chaos.

 * Empirical Validation through CMB Fractality: The paper claims that the fractal dimension of the Cosmic Microwave Background (CMB) is related to the golden ratio, which arises from entanglement entropy calculated based on the distribution of prime numbers.

 * Prime-Indexed Coherence and the Horizon Problem: The uniformity of the CMB across causally disconnected regions is explained by "prime-indexed coherence," where primes carry non-local correlations.

 * Excess Power at Prime Multipoles: The paper also claims to find excess power in the CMB's harmonic spectrum at multipole moments that correspond to prime numbers.

 * Prime-Energy Correspondence: The paper explores the relationship E_p = \log p, linking prime numbers to energy scales in physical systems.

 * Mathematical Necessity: Primes are linked to the zeros of the Riemann zeta function, and the von Mangoldt function, \Lambda(n) = \log p for n = p^k, encodes the density of primes.

 * Physical Justification: The logarithm converts multiplicative primes into additive energy scales, mirroring how entropy converts multiplicative microstate counts into additive quantities.

 * Holographic Scaling: Primes are posited to label Planck-scale "atoms" of spacetime, with their spacing in energy reflecting holographic discreteness.

In essence, the paper proposes a model where prime numbers are not just mathematical abstractions but are fundamental to the structure and behavior of the universe at various scales.
DOI: 10.6084/m9.figshare.24585158
title: Bootstrap Inference in the Presence of Bias

description: We consider bootstrap inference for estimators which are (asymptotically) biased. We show that, even when the bias term cannot be consistently estimated, valid inference can be obtained by proper implementations of the bootstrap. Specifically, we show that the prepivoting approach of Beran, originally proposed to deliver higher-order refinements, restores bootstrap validity by transforming the original bootstrap <i>p</i>-value into an asymptotically uniform random variable. We propose two different implementations of prepivoting (plug-in and double bootstrap), and provide general high-level conditions that imply validity of bootstrap inference. To illustrate the practical relevance and implementation of our results, we discuss five examples: (i) inference on a target parameter based on model averaging; (ii) ridge-type regularized estimators; (iii) nonparametric regression; (iv) a location model for infinite variance data; and (v) dynamic panel data models. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24585158.v1
title: Bootstrap Inference in the Presence of Bias

description: We consider bootstrap inference for estimators which are (asymptotically) biased. We show that, even when the bias term cannot be consistently estimated, valid inference can be obtained by proper implementations of the bootstrap. Specifically, we show that the prepivoting approach of Beran, originally proposed to deliver higher-order refinements, restores bootstrap validity by transforming the original bootstrap <i>p</i>-value into an asymptotically uniform random variable. We propose two different implementations of prepivoting (plug-in and double bootstrap), and provide general high-level conditions that imply validity of bootstrap inference. To illustrate the practical relevance and implementation of our results, we discuss five examples: (i) inference on a target parameter based on model averaging; (ii) ridge-type regularized estimators; (iii) nonparametric regression; (iv) a location model for infinite variance data; and (v) dynamic panel data models. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24962436
title: Policy Learning with Asymmetric Counterfactual Utilities

description: Data-driven decision making plays an important role even in high stakes settings like medicine and public policy. Learning optimal policies from observed data requires a careful formulation of the utility function whose expected value is maximized across a population. Although researchers typically use utilities that depend on observed outcomes alone, in many settings the decision maker’s utility function is more properly characterized by the joint set of potential outcomes under all actions. For example, the Hippocratic principle to “do no harm” implies that the cost of causing death to a patient who would otherwise survive without treatment is greater than the cost of forgoing life-saving treatment. We consider optimal policy learning with asymmetric counterfactual utility functions of this form that consider the joint set of potential outcomes. We show that asymmetric counterfactual utilities lead to an unidentifiable expected utility function, and so we first partially identify it. Drawing on statistical decision theory, we then derive minimax decision rules by minimizing the maximum expected utility loss relative to different alternative policies. We show that one can learn minimax loss decision rules from observed data by solving intermediate classification problems, and establish that the finite sample excess expected utility loss of this procedure is bounded by the regret of these intermediate classifiers. We apply this conceptual framework and methodology to the decision about whether or not to use right heart catheterization for patients with possible pulmonary hypertension. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24962436.v3
title: Policy Learning with Asymmetric Counterfactual Utilities

description: Data-driven decision making plays an important role even in high stakes settings like medicine and public policy. Learning optimal policies from observed data requires a careful formulation of the utility function whose expected value is maximized across a population. Although researchers typically use utilities that depend on observed outcomes alone, in many settings the decision maker’s utility function is more properly characterized by the joint set of potential outcomes under all actions. For example, the Hippocratic principle to “do no harm” implies that the cost of causing death to a patient who would otherwise survive without treatment is greater than the cost of forgoing life-saving treatment. We consider optimal policy learning with asymmetric counterfactual utility functions of this form that consider the joint set of potential outcomes. We show that asymmetric counterfactual utilities lead to an unidentifiable expected utility function, and so we first partially identify it. Drawing on statistical decision theory, we then derive minimax decision rules by minimizing the maximum expected utility loss relative to different alternative policies. We show that one can learn minimax loss decision rules from observed data by solving intermediate classification problems, and establish that the finite sample excess expected utility loss of this procedure is bounded by the regret of these intermediate classifiers. We apply this conceptual framework and methodology to the decision about whether or not to use right heart catheterization for patients with possible pulmonary hypertension. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24353032
title: A Projection Space-Filling Criterion and Related Optimality Results

description: Computer experiments call for space-filling designs. Recently, a minimum aberration type space-filling criterion was proposed to rank and assess a family of space-filling designs including Latin hypercubes and strong orthogonal arrays. It aims at capturing the space-filling properties of a design when projected onto subregions of various sizes. In this article, we also consider the dimension aside from the sizes of subregions by proposing first an expanded space-filling hierarchy principle and then a projection space-filling criterion as per the new principle. When projected onto subregions of the specific size, the proposed criterion ranks designs via sequentially maximizing the space-filling properties on equally sized subregions in lower dimensions to higher dimensions, while the minimum aberration type space-filling criterion compares designs by maximizing the aggregate space-filling properties on multidimensional subregions of the same size. We present illustrative examples to demonstrate two criteria and conduct simulations as evidence of the utility of our criterion in terms of selecting efficient space-filling designs to build statistical surrogate models. We further consider the construction of the optimal space-filling designs under the proposed criterion. Although many algorithms have been proposed for generating space-filling designs, it is well-known that they often deteriorate rapidly in performance for large designs. In this article, we develop some theoretical optimality results and characterize several classes of strong orthogonal arrays of strength three that are the most space-filling. Supplementary materials for this article are available online.