cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/526tksfb62
title: Package A – Part 11 of 13: Replication Materials for "Maximal Extractable Value and Allocative Inefficiencies in Public Blockchains"

description: This dataset contains large data files for replicating Tables 1-4 and Figures 1 & 4 from "Maximal Extractable Value and Allocative Inefficiencies in Public Blockchains" (Journal of Financial Economics).

IMPORTANT: This is Package 11 of 13 in a multi-part dataset. You must download ALL packages (1 through 13) to complete the full replication. This package contains can NOT be used independently. This package must be used together with companion data packages 1-10,12-13, which can be found in MASTER_README.md.

IMPORTANT: Please read instructions in MASTER_README.md and readme_PkgA.md. 

Setup Instructions: 
1. Download ALL packages in this series (1 through 13)
2. Follow the reconstruction instructions in MASTER_README.md and readme_PkgA.md. 
3. Extract all data packages to the same directory to rebuild the original folder structure
DOI: 10.17632/526tksfb62.1
title: Package A – Part 11 of 13: Replication Materials for "Maximal Extractable Value and Allocative Inefficiencies in Public Blockchains"

description: This dataset contains large data files for replicating Tables 1-4 and Figures 1 & 4 from "Maximal Extractable Value and Allocative Inefficiencies in Public Blockchains" (Journal of Financial Economics).

IMPORTANT: This is Package 11 of 13 in a multi-part dataset. You must download ALL packages (1 through 13) to complete the full replication. This package contains can NOT be used independently. This package must be used together with companion data packages 1-10,12-13, which can be found in MASTER_README.md.

IMPORTANT: Please read instructions in MASTER_README.md and readme_PkgA.md. 

Setup Instructions: 
1. Download ALL packages in this series (1 through 13)
2. Follow the reconstruction instructions in MASTER_README.md and readme_PkgA.md. 
3. Extract all data packages to the same directory to rebuild the original folder structure
DOI: 10.17632/9m43svx29j
title: Package A – Part 12 of 13: Replication Materials for "Maximal Extractable Value and Allocative Inefficiencies in Public Blockchains"

description: This dataset contains large data files for replicating Tables 1-4 and Figures 1 & 4 from "Maximal Extractable Value and Allocative Inefficiencies in Public Blockchains" (Journal of Financial Economics).

IMPORTANT: This is Package 12 of 13 in a multi-part dataset. You must download ALL packages (1 through 13) to complete the full replication. This package contains can NOT be used independently. This package must be used together with companion data packages 1-11 and 13, which can be found in MASTER_README.md.

IMPORTANT: Please read instructions in MASTER_README.md and readme_PkgA.md. 

Setup Instructions: 
1. Download ALL packages in this series (1 through 13)
2. Follow the reconstruction instructions in MASTER_README.md and readme_PkgA.md. 
3. Extract all data packages to the same directory to rebuild the original folder structure
DOI: 10.17632/9m43svx29j.1
title: Package A – Part 12 of 13: Replication Materials for "Maximal Extractable Value and Allocative Inefficiencies in Public Blockchains"

description: This dataset contains large data files for replicating Tables 1-4 and Figures 1 & 4 from "Maximal Extractable Value and Allocative Inefficiencies in Public Blockchains" (Journal of Financial Economics).

IMPORTANT: This is Package 12 of 13 in a multi-part dataset. You must download ALL packages (1 through 13) to complete the full replication. This package contains can NOT be used independently. This package must be used together with companion data packages 1-11 and 13, which can be found in MASTER_README.md.

IMPORTANT: Please read instructions in MASTER_README.md and readme_PkgA.md. 

Setup Instructions: 
1. Download ALL packages in this series (1 through 13)
2. Follow the reconstruction instructions in MASTER_README.md and readme_PkgA.md. 
3. Extract all data packages to the same directory to rebuild the original folder structure
DOI: 10.17632/y2469pwkx2
title: Package A – Part 13 of 13: Replication Materials for "Maximal Extractable Value and Allocative Inefficiencies in Public Blockchains"

description: This dataset contains large data files for replicating Tables 1-4 and Figures 1 & 4 from "Maximal Extractable Value and Allocative Inefficiencies in Public Blockchains" (Journal of Financial Economics).

IMPORTANT: This is Package 13 of 13 in a multi-part dataset. You must download ALL packages (1 through 13) to complete the full replication. This package contains can NOT be used independently. This package must be used together with companion data packages 1-12, which can be found in MASTER_README.md.

IMPORTANT: Please read instructions in MASTER_README.md and readme_PkgA.md. 

Setup Instructions: 
1. Download ALL packages in this series (1 through 13)
2. Follow the reconstruction instructions in MASTER_README.md and readme_PkgA.md. 
3. Extract all data packages to the same directory to rebuild the original folder structure
DOI: 10.17632/y2469pwkx2.1
title: Package A – Part 13 of 13: Replication Materials for "Maximal Extractable Value and Allocative Inefficiencies in Public Blockchains"

description: This dataset contains large data files for replicating Tables 1-4 and Figures 1 & 4 from "Maximal Extractable Value and Allocative Inefficiencies in Public Blockchains" (Journal of Financial Economics).

IMPORTANT: This is Package 13 of 13 in a multi-part dataset. You must download ALL packages (1 through 13) to complete the full replication. This package contains can NOT be used independently. This package must be used together with companion data packages 1-12, which can be found in MASTER_README.md.

IMPORTANT: Please read instructions in MASTER_README.md and readme_PkgA.md. 

Setup Instructions: 
1. Download ALL packages in this series (1 through 13)
2. Follow the reconstruction instructions in MASTER_README.md and readme_PkgA.md. 
3. Extract all data packages to the same directory to rebuild the original folder structure
DOI: 10.17632/bf7fzxjbnm.2
title: Schoeberl_Fitz_vonderLinde_et_al_Multiway-chromatin-conformat-on-analyses-at-the-human-immunoglobulin-heavy-chain-locus-reveal-regulatory-principles-underlying-somatic-hypermutation

description: Supplementary imaging repository for Schoeberl, Fitz, von der Linde et al., 'Multiway chromatin conformation analyses at the human immunoglobulin heavy chain locus reveal regulatory principles underlying somatic hypermutation'. The pdf has western blots for Fig. S3A and FACS images for Fig. S1D.
DOI: 10.15468/dl.scr7eb
title: Occurrence Download

description: A dataset containing 832578 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is FRA.6_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Mammalia"
 ]
}

 The dataset includes 832578 records from 859 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048608-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/scpmfsttrv
title: Influence of cardiorespiratory fitness and MRI measures of neuroinflammation on hippocampal volume in multiple sclerosis

description: This study investigated the impact of neuroinflammation and cardiorespiratory fitness (CRF) on hippocampal volume in patients with multiple sclerosis (MS), with a focus on differences between clinical phenotypes. A total of 81 MS patients were included, of whom 27 had relapsing–remitting MS (RRMS) and 54 had progressive MS (PMS). An additional 45 age- and sex-matched healthy controls were enrolled for structural brain imaging comparisons. All participants underwent high-resolution 3T MRI to assess normalized brain, gray matter, hippocampal, and thalamic volumes. In MS patients, aerobic capacity was assessed via maximum oxygen consumption (VO₂max) using cardiopulmonary exercise testing, while T2-hyperintense lesion volume (T2-LV) and choroid plexus volume (CPV) were quantified as MRI markers of neuroinflammation.

Compared to healthy controls, both RRMS and PMS patients exhibited significantly lower brain and regional volumes, including the hippocampus, and higher T2-LV and CPV, reflecting increased neuroinflammation. Regression analyses revealed that T2-LV and CPV were the most influential predictors of global and regional brain atrophy in both MS phenotypes. Notably, VO₂max was identified as a significant predictor of hippocampal volume only in RRMS patients, explaining 16.9% of the variance and retained as a relevant predictor alongside T2-LV in multivariate models. In contrast, VO₂max had no significant explanatory value for hippocampal or other volumetric outcomes in PMS patients.

These findings suggest a selective and clinically relevant association between aerobic fitness and hippocampal integrity in the early, relapsing phase of MS, possibly mediated by mechanisms such as neurogenesis or neuroprotection. In more advanced stages of the disease, such as PMS, widespread neurodegeneration may overshadow the potential benefits of aerobic capacity. The results underscore the importance of promoting CRF early in the disease course as a strategy to preserve hippocampal structure and potentially mitigate cognitive decline.
DOI: 10.17632/scpmfsttrv.1
title: Influence of cardiorespiratory fitness and MRI measures of neuroinflammation on hippocampal volume in multiple sclerosis

description: This study investigated the impact of neuroinflammation and cardiorespiratory fitness (CRF) on hippocampal volume in patients with multiple sclerosis (MS), with a focus on differences between clinical phenotypes. A total of 81 MS patients were included, of whom 27 had relapsing–remitting MS (RRMS) and 54 had progressive MS (PMS). An additional 45 age- and sex-matched healthy controls were enrolled for structural brain imaging comparisons. All participants underwent high-resolution 3T MRI to assess normalized brain, gray matter, hippocampal, and thalamic volumes. In MS patients, aerobic capacity was assessed via maximum oxygen consumption (VO₂max) using cardiopulmonary exercise testing, while T2-hyperintense lesion volume (T2-LV) and choroid plexus volume (CPV) were quantified as MRI markers of neuroinflammation.

Compared to healthy controls, both RRMS and PMS patients exhibited significantly lower brain and regional volumes, including the hippocampus, and higher T2-LV and CPV, reflecting increased neuroinflammation. Regression analyses revealed that T2-LV and CPV were the most influential predictors of global and regional brain atrophy in both MS phenotypes. Notably, VO₂max was identified as a significant predictor of hippocampal volume only in RRMS patients, explaining 16.9% of the variance and retained as a relevant predictor alongside T2-LV in multivariate models. In contrast, VO₂max had no significant explanatory value for hippocampal or other volumetric outcomes in PMS patients.

These findings suggest a selective and clinically relevant association between aerobic fitness and hippocampal integrity in the early, relapsing phase of MS, possibly mediated by mechanisms such as neurogenesis or neuroprotection. In more advanced stages of the disease, such as PMS, widespread neurodegeneration may overshadow the potential benefits of aerobic capacity. The results underscore the importance of promoting CRF early in the disease course as a strategy to preserve hippocampal structure and potentially mitigate cognitive decline.
DOI: 10.17632/sb6nwdvvnw
title: Raw Data Survey Sht

description: This dataset supports a quantitative study that investigates how social influence impacts customer behavior in adopting smart home technology in Indonesia, mediated by artificial intelligence (AI) perception and user acceptance. The research employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the relationships among the four core constructs: Social Influence, User Acceptance, Artificial Intelligence Perception, and Customer Behaviour.

The data were collected via an online questionnaire distributed across various regions in Indonesia using purposive sampling. A total of 401 valid responses were obtained from individuals with prior experience using smart home technology. Each item in the instrument was measured using a nine-point Likert scale, capturing user attitudes and behavioral patterns.

Indicators include variables related to:

Social Influence (e.g., peer recommendations, community support),
User Acceptance (e.g., perceived usefulness and intention to continue use),
Artificial Intelligence (e.g., trust in AI features, decision-making assistance),
Customer Behaviour (e.g., usage frequency).
The dataset includes cleaned and anonymized responses and is intended to facilitate further analysis, replication, and model validation in the context of technology adoption. It can be used by researchers interested in smart home adoption, AI perception, behavioral modeling, and digital consumer behavio
DOI: 10.17632/sb6nwdvvnw.1
title: Raw Data Survey Sht

description: This dataset supports a quantitative study that investigates how social influence impacts customer behavior in adopting smart home technology in Indonesia, mediated by artificial intelligence (AI) perception and user acceptance. The research employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the relationships among the four core constructs: Social Influence, User Acceptance, Artificial Intelligence Perception, and Customer Behaviour.

The data were collected via an online questionnaire distributed across various regions in Indonesia using purposive sampling. A total of 401 valid responses were obtained from individuals with prior experience using smart home technology. Each item in the instrument was measured using a nine-point Likert scale, capturing user attitudes and behavioral patterns.

Indicators include variables related to:

Social Influence (e.g., peer recommendations, community support),
User Acceptance (e.g., perceived usefulness and intention to continue use),
Artificial Intelligence (e.g., trust in AI features, decision-making assistance),
Customer Behaviour (e.g., usage frequency).
The dataset includes cleaned and anonymized responses and is intended to facilitate further analysis, replication, and model validation in the context of technology adoption. It can be used by researchers interested in smart home adoption, AI perception, behavioral modeling, and digital consumer behavio
DOI: 10.17632/4xjm23kzbm
title: Global Zircon Trace Element Compilation & Geochronology and Trace Element Data of Detrital Zircon and Rutile from Gano Bauxite, Northern Iran

description: The supplementary tables, which include a compilation of zircon trace elements from three types of magmatic rocks (Table S1) and three types of magma (Table S2), as well as the results of U–Pb and trace elements analysis of Gano bauxite zircon (Table S3), Lu-Hf analysis of Gano bauxite zircon (Table S4), and U-Pb analysis of Gano bauxite rutile (Table S5).
DOI: 10.17632/4xjm23kzbm.2
title: Global Zircon Trace Element Compilation & Geochronology and Trace Element Data of Detrital Zircon and Rutile from Gano Bauxite, Northern Iran

description: The supplementary tables, which include a compilation of zircon trace elements from three types of magmatic rocks (Table S1) and three types of magma (Table S2), as well as the results of U–Pb and trace elements analysis of Gano bauxite zircon (Table S3), Lu-Hf analysis of Gano bauxite zircon (Table S4), and U-Pb analysis of Gano bauxite rutile (Table S5).
DOI: 10.17632/89tphpc9ft
title: Package B: Replication Materials for "Maximal Extractable Value and Allocative Inefficiencies in Public Blockchains"

description: This dataset contains the data and code for replicating Tables 5-8 and Figures 3, 5-7, E.1 from "Maximal Extractable Value and Allocative Inefficiencies in Public Blockchains" (Journal of Financial Economics).

IMPORTANT: This package is STANDALONE and can be used independently to reproduce the regression results and additional figures. This package complements the separate Data Package series (1 through 13) which produces Tables 1-4 and Figures 1 & 4. Together, these packages provide complete replication of all empirical results in the paper. 
Related Datasets: Companion data packages for descriptive statistics, which DOIs can be found in MASTER_README.md.

IMPORTANT: Please read instructions in MASTER_README.md and readme_PkgB.md.
DOI: 10.17632/89tphpc9ft.1
title: Package B: Replication Materials for "Maximal Extractable Value and Allocative Inefficiencies in Public Blockchains"

description: This dataset contains the data and code for replicating Tables 5-8 and Figures 3, 5-7, E.1 from "Maximal Extractable Value and Allocative Inefficiencies in Public Blockchains" (Journal of Financial Economics).

IMPORTANT: This package is STANDALONE and can be used independently to reproduce the regression results and additional figures. This package complements the separate Data Package series (1 through 13) which produces Tables 1-4 and Figures 1 & 4. Together, these packages provide complete replication of all empirical results in the paper. 
Related Datasets: Companion data packages for descriptive statistics, which DOIs can be found in MASTER_README.md.

IMPORTANT: Please read instructions in MASTER_README.md and readme_PkgB.md.
DOI: 10.17632/tr59ph2vj2
title: The insula modulates the effects of aerobic training on cardiovascular function and ambulation in multiple sclerosis

description: Cardiovascular dysregulation is a frequent non-motor manifestation of multiple sclerosis (MS), potentially linked to structural damage in autonomic control regions such as the insular cortex. This study examined the role of insular integrity in modulating cardiovascular fitness (CF) and ambulation, and its influence on the efficacy of aerobic training (AT) in people with MS. A total of 61 MS patients (20 with relapsing–remitting and 41 with progressive MS) were retrospectively analyzed and randomized into two intervention arms: an AT group (MS-AT, n = 31) performing moderate-intensity aerobic exercise, and a motor training control group (MS-C, n = 30) performing non-aerobic exercises. Each patient underwent clinical, cardiopulmonary, and MRI evaluations at baseline and after 24 training sessions over 2–3 months.

Two separate healthy control groups were included: one for cardiopulmonary assessment (HC-clinic, n = 20) and another for imaging comparison (HC-MRI, n = 60). At baseline, MS patients demonstrated significantly reduced VO₂max, HR reserve (HRR), and 6-minute walk test (6MWT) performance compared to healthy controls, alongside widespread gray matter atrophy including the bilateral insula. Notably, the presence of left insular T2-hyperintense lesions correlated with impaired HRR.

Following AT, the MS-AT group—particularly patients without insular lesions—showed significant improvement in 6MWT distance and preservation of insular volume. In contrast, the MS-C group exhibited progressive left insular atrophy. Moreover, greater gains in walking capacity were significantly associated with more limited loss of left anterior insular volume.

These results highlight the insula’s role in modulating both baseline cardiovascular impairment and training-related functional improvements. Assessing insular integrity via MRI may offer predictive value for rehabilitation outcomes and guide individualized aerobic training strategies in MS care.
DOI: 10.17632/tr59ph2vj2.1
title: The insula modulates the effects of aerobic training on cardiovascular function and ambulation in multiple sclerosis

description: Cardiovascular dysregulation is a frequent non-motor manifestation of multiple sclerosis (MS), potentially linked to structural damage in autonomic control regions such as the insular cortex. This study examined the role of insular integrity in modulating cardiovascular fitness (CF) and ambulation, and its influence on the efficacy of aerobic training (AT) in people with MS. A total of 61 MS patients (20 with relapsing–remitting and 41 with progressive MS) were retrospectively analyzed and randomized into two intervention arms: an AT group (MS-AT, n = 31) performing moderate-intensity aerobic exercise, and a motor training control group (MS-C, n = 30) performing non-aerobic exercises. Each patient underwent clinical, cardiopulmonary, and MRI evaluations at baseline and after 24 training sessions over 2–3 months.

Two separate healthy control groups were included: one for cardiopulmonary assessment (HC-clinic, n = 20) and another for imaging comparison (HC-MRI, n = 60). At baseline, MS patients demonstrated significantly reduced VO₂max, HR reserve (HRR), and 6-minute walk test (6MWT) performance compared to healthy controls, alongside widespread gray matter atrophy including the bilateral insula. Notably, the presence of left insular T2-hyperintense lesions correlated with impaired HRR.

Following AT, the MS-AT group—particularly patients without insular lesions—showed significant improvement in 6MWT distance and preservation of insular volume. In contrast, the MS-C group exhibited progressive left insular atrophy. Moreover, greater gains in walking capacity were significantly associated with more limited loss of left anterior insular volume.

These results highlight the insula’s role in modulating both baseline cardiovascular impairment and training-related functional improvements. Assessing insular integrity via MRI may offer predictive value for rehabilitation outcomes and guide individualized aerobic training strategies in MS care.
DOI: 10.6084/m9.figshare.29210301.v1
title: Natural Attraction and Driving Forces: Analyzing the Impact of Parkland on Residential Prices and Typological Difference*

description: Exploring the driving mechanism of parks and green spaces on the spatial heterogeneity of housing prices helps to rationalize the allocation pattern of social and environmental resources and the housing economic market, and to optimize the spatial pattern of green facilities in human habitat. The study integrates Shenzhen housing samples and their price transaction data, park green space and other related facilities data, and by using Ordinary Least Squares (OLS) Geographically Weighted Regression Model (GWR), compares and analyses the sequence of effects of architectural characteristics, location characteristics, neighborhood characteristics and green space characteristics on housing prices, and analyses the relationship between park green space proximity and housing prices, and the types of differences in their effect mechanisms. The results show that the driving effect of overall park green space proximity on housing prices shows the concentration effect of high-level development areas; the proximity of different types of park green space drives housing prices as urban parks &gt; natural parks &gt; community parks, and urban parks have higher economic value. The results of this study suggest that future land resource planning should pay more attention to the economic drive of ecological space and effectively improve the efficiency of social resource services.