cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17515621
title: Scalable Identification of Quantum Phases in Many-Body Systems via Entanglement Fractal Dimension: A Conceptual Framework

description: The Entanglement Fractal Dimension (EFD) framework addresses the exponential computational challenges in identifying quantum phases within many-body systems by introducing a novel, scalable scalar invariant derived from the fractal structure of the bipartite entanglement spectrum. Unlike traditional order parameters or full-state tomography, EFD leverages topological data analysis (TDA) and multiscale entanglement renormalization to extract phase fingerprints in polynomial time, achieving \(\mathcal{O}(N^3)\) complexity for 2D systems via matrix product states (MPS) and projected entangled pair states (PEPS), scalable to \(10^6\) particles with quantum-assisted methods.

Theoretically, EFD is defined as the box-counting dimension of the embedded entanglement spectrum, related to the central charge \(c\) via \(\mathcal{D}_F = d - \frac{c \log 2}{6 \pi (d+1)}\), ensuring discontinuities at quantum phase transitions (QPTs) and topological invariance under deformations. Rigorous proofs establish uniqueness through the injectivity of the entanglement map and Wasserstein distance separation (\(W_1 > \delta > 0\)), with hyperscaling \(\mathcal{D}_F = d \nu (2 - \alpha)/(2 - \alpha + \nu d)\) linking to critical exponents. Finite-size estimators, including maximum-likelihood and correlation dimensions, provide asymptotic normality and noise robustness.

Applications demonstrate EFD's efficacy: in the transverse-field Ising model, it distinguishes paramagnetic (\(\mathcal{D}_F \approx 1\)) from ferromagnetic (\(\approx 1.04\)) phases; for the Fermi-Hubbard model at half-filling, it signals Mott transitions with \(\Delta \mathcal{D}_F > 0.5\) for \(N=10\), validated by exact diagonalization and DMRG benchmarks; and in the Bose-Hubbard model, it peaks near the superfluid-Mott boundary, aligning with XY universality (\(\nu \approx 0.67\)). Reproducible QuTiP simulations for small systems confirm spectral clustering and effective dimensions, with finite-size corrections extrapolated via RG analysis.

Implications extend to quantum technologies: in NISQ variational quantum eigensolvers (VQEs), EFD hybridizes costs for 95\% fidelity Mott detection on 50 qubits; in quantum networks, it optimizes entanglement distribution over lossy channels (40\% rate boost) and enables topological repeaters with \(F > 1 - \epsilon / \mathcal{D}_F^d\). Compared to machine learning approaches, EFD offers interpretability without training, bridging CFT universality and fractalization for experimental validation in ultracold atoms.

This self-contained framework resolves conceptual barriers in phase discrimination, paving the way for scalable many-body simulations and distributed quantum protocols.
DOI: 10.5281/zenodo.17515622
title: Scalable Identification of Quantum Phases in Many-Body Systems via Entanglement Fractal Dimension: A Conceptual Framework

description: The Entanglement Fractal Dimension (EFD) framework addresses the exponential computational challenges in identifying quantum phases within many-body systems by introducing a novel, scalable scalar invariant derived from the fractal structure of the bipartite entanglement spectrum. Unlike traditional order parameters or full-state tomography, EFD leverages topological data analysis (TDA) and multiscale entanglement renormalization to extract phase fingerprints in polynomial time, achieving \(\mathcal{O}(N^3)\) complexity for 2D systems via matrix product states (MPS) and projected entangled pair states (PEPS), scalable to \(10^6\) particles with quantum-assisted methods.

Theoretically, EFD is defined as the box-counting dimension of the embedded entanglement spectrum, related to the central charge \(c\) via \(\mathcal{D}_F = d - \frac{c \log 2}{6 \pi (d+1)}\), ensuring discontinuities at quantum phase transitions (QPTs) and topological invariance under deformations. Rigorous proofs establish uniqueness through the injectivity of the entanglement map and Wasserstein distance separation (\(W_1 > \delta > 0\)), with hyperscaling \(\mathcal{D}_F = d \nu (2 - \alpha)/(2 - \alpha + \nu d)\) linking to critical exponents. Finite-size estimators, including maximum-likelihood and correlation dimensions, provide asymptotic normality and noise robustness.

Applications demonstrate EFD's efficacy: in the transverse-field Ising model, it distinguishes paramagnetic (\(\mathcal{D}_F \approx 1\)) from ferromagnetic (\(\approx 1.04\)) phases; for the Fermi-Hubbard model at half-filling, it signals Mott transitions with \(\Delta \mathcal{D}_F > 0.5\) for \(N=10\), validated by exact diagonalization and DMRG benchmarks; and in the Bose-Hubbard model, it peaks near the superfluid-Mott boundary, aligning with XY universality (\(\nu \approx 0.67\)). Reproducible QuTiP simulations for small systems confirm spectral clustering and effective dimensions, with finite-size corrections extrapolated via RG analysis.

Implications extend to quantum technologies: in NISQ variational quantum eigensolvers (VQEs), EFD hybridizes costs for 95\% fidelity Mott detection on 50 qubits; in quantum networks, it optimizes entanglement distribution over lossy channels (40\% rate boost) and enables topological repeaters with \(F > 1 - \epsilon / \mathcal{D}_F^d\). Compared to machine learning approaches, EFD offers interpretability without training, bridging CFT universality and fractalization for experimental validation in ultracold atoms.

This self-contained framework resolves conceptual barriers in phase discrimination, paving the way for scalable many-body simulations and distributed quantum protocols.
DOI: 10.48717/rdkx-dg84
title: EVN Observation EW029B
DOI: 10.48717/3k1p-we33
title: EVN Observation EM163
DOI: 10.48717/mshf-wa97
title: EVN Experiment EM163
DOI: 10.48717/7py6-d405
title: EVN Observation F22C1
DOI: 10.48717/wcc6-p524
title: EVN Experiment F22C1
DOI: 10.48717/jts0-yp82
title: EVN Observation EC086D
DOI: 10.48717/smgk-5s29
title: EVN Observation EC086C
DOI: 10.48717/eptw-bt79
title: EVN Experiment FR064
DOI: 10.48717/m79r-dx11
title: EVN Observation FR064
DOI: 10.48717/yq6x-9b25
title: EVN Observation EB096E
DOI: 10.48717/fszq-dd85
title: EVN Observation EB096B
DOI: 10.48717/z5mh-9n18
title: EVN Observation EM164A
DOI: 10.48717/e549-sc03
title: EVN Observation EB096C
DOI: 10.48717/5fvc-3442
title: EVN Observation EC073B
DOI: 10.48717/gzyh-da37
title: EVN Experiment EC073
DOI: 10.48717/hm9g-m955
title: EVN Observation EB096D
DOI: 10.48717/6ffw-jp67
title: EVN Experiment EC086
DOI: 10.48717/xv5q-yc10
title: EVN Observation EC086B
DOI: 10.48717/aaz7-m829
title: EVN Observation EG119A
DOI: 10.48717/cj4p-5a62
title: EVN Observation EW029D
DOI: 10.48717/80bn-av80
title: EVN Experiment EW029
DOI: 10.48717/b89p-6z56
title: EVN Observation EW029C
DOI: 10.48717/9r3x-2x46
title: EVN Observation EV024C
DOI: 10.48717/qtvc-8g18
title: EVN Observation GM081A
DOI: 10.48717/v1qk-ng28
title: EVN Observation EC084A
DOI: 10.48717/1kgz-cf33
title: EVN Experiment EC084
DOI: 10.48717/95xx-5h85
title: EVN Observation N22L2
DOI: 10.48717/4vbq-8g53
title: EVN Experiment N22L2
DOI: 10.48717/p8az-ps13
title: EVN Observation EG111A
DOI: 10.48717/qn31-3e13
title: EVN Experiment EG111
DOI: 10.48717/050e-fs08
title: EVN Observation EA065H
DOI: 10.5281/zenodo.17042570
title: Supplementary materials

description:  Scientific applications continue to rely heavily on legacy Fortran codebases originally developed for homogeneous, CPU-based systems. As High-Performance Computing (HPC) evolves towards heterogeneous GPU-accelerated architectures, many modern accelerators lack native Fortran bindings, creating an urgent need to translate and optimize legacy code for portability. Frameworks like Kokkos provide performance portability across multiple architectures and a single-source C++ abstraction, but manual porting from Fortran to Kokkos requires significant domain expertise and remains time-intensive. While large language models (LLMs) have demonstrated promise in source-to-source code generation, their use in building fully autonomous workflows for translating and optimizing parallel code is largely unexplored, particularly in the context of achieving performance portability across diverse hardware. This paper presents an agentic AI workflow in which specialized LLM “agents” collaborate to translate, validate, compile, run, test, debug, and optimize Fortran kernels into portable Kokkos C++ programs. Our results show that the pipeline successfully modernizes a variety of benchmark kernels, producing performance-portable Kokkos codes across hardware partitions. Paid OpenAI models such as GPT-5 and o4-mini-high executed the full workflow for only a few U.S. dollars, producing optimized codes that exceeded the Fortran baselines, whereas open-source models like Llama4-Maverick often failed to produce functional codes. This work demonstrates the feasibility of agentic AI for Fortran-to-Kokkos code transformation and offers a path toward autonomously modernizing legacy scientific applications to run portably and efficiently across a diverse set of supercomputers. It further illustrates the potential of LLM-driven agentic systems to perform structured, domain-specific reasoning tasks in scientific and systems-oriented applications. LA-UR-25-28882
DOI: 10.5281/zenodo.17064942
title: Supplementary materials

description:  Scientific applications continue to rely heavily on legacy Fortran codebases originally developed for homogeneous, CPU-based systems. As High-Performance Computing (HPC) evolves towards heterogeneous GPU-accelerated architectures, many modern accelerators lack native Fortran bindings, creating an urgent need to translate and optimize legacy code for portability. Frameworks like Kokkos provide performance portability across multiple architectures and a single-source C++ abstraction, but manual porting from Fortran to Kokkos requires significant domain expertise and remains time-intensive. While large language models (LLMs) have demonstrated promise in source-to-source code generation, their use in building fully autonomous workflows for translating and optimizing parallel code is largely unexplored, particularly in the context of achieving performance portability across diverse hardware. This paper presents an agentic AI workflow in which specialized LLM “agents” collaborate to translate, validate, compile, run, test, debug, and optimize Fortran kernels into portable Kokkos C++ programs. Our results show that the pipeline successfully modernizes a variety of benchmark kernels, producing performance-portable Kokkos codes across hardware partitions. Paid OpenAI models such as GPT-5 and o4-mini-high executed the full workflow for only a few U.S. dollars, producing optimized codes that exceeded the Fortran baselines, whereas open-source models like Llama4-Maverick often failed to produce functional codes. This work demonstrates the feasibility of agentic AI for Fortran-to-Kokkos code transformation and offers a path toward autonomously modernizing legacy scientific applications to run portably and efficiently across a diverse set of supercomputers. It further illustrates the potential of LLM-driven agentic systems to perform structured, domain-specific reasoning tasks in scientific and systems-oriented applications. LA-UR-25-28882
DOI: 10.48717/73c5-7y35
title: EVN Observation EN011B
DOI: 10.48717/g7re-ja54
title: EVN Experiment RM017
DOI: 10.48717/yepw-r873
title: EVN Observation RM017B
DOI: 10.48717/jrt5-yf62
title: EVN Observation EK050G
DOI: 10.48717/q38x-nv27
title: EVN Experiment EK050
DOI: 10.48717/6z40-fg50
title: EVN Observation EK051C
DOI: 10.48717/gv07-aw52
title: EVN Observation EK051B
DOI: 10.48717/f7qy-d919
title: EVN Observation EK051A
DOI: 10.15468/dl.2mrfv9
title: Occurrence Download

description: A dataset containing 8594 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Material sample, Living Specimen, Occurrence evidence, Human Observation)",
 "Continent is one of (South America, North America, Europe, Oceania, Asia, Antarctica, Africa)",
 "DatasetKey is one of (iNaturalist Research-grade Observations, CABI Africa Invasive and Alien Species data)",
 "OccurrenceStatus is Present",
 "TaxonKey is Calotropis procera"
 ]
}

 The dataset includes 8594 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013305-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48717/x2ry-6e31
title: EVN Observation EK051D
DOI: 10.48717/bx13-0x93
title: EVN Observation RSG17
DOI: 10.48717/ch1q-cd45
title: EVN Experiment RSG17
DOI: 10.48717/48ag-kw92
title: EVN Observation EC082E
DOI: 10.48717/2fg4-pc42
title: EVN Observation EA065I
DOI: 10.48717/6vyn-ex24
title: EVN Observation EK051E
DOI: 10.48717/jsys-n839
title: EVN Observation EV024D
DOI: 10.48717/bp8h-ky91
title: EVN Observation EB096I
DOI: 10.48717/8gmv-tp16
title: EVN Observation RG013A
DOI: 10.48717/s67z-hz76
title: EVN Observation GM081E
DOI: 10.48717/xbq7-1p20
title: EVN Observation ES104
DOI: 10.48717/eq4b-5438
title: EVN Experiment ES104
DOI: 10.48717/fjce-g588
title: EVN Observation EM164C
DOI: 10.48717/nnzd-zm64
title: EVN Observation ES103A
DOI: 10.48717/fkbe-m617
title: EVN Observation RSY09
DOI: 10.48717/ewem-gt42
title: EVN Experiment RSY09
DOI: 10.48717/hf08-dp36
title: EVN Observation EM164D
DOI: 10.48717/8e6b-8c57
title: EVN Observation ES103F
DOI: 10.48717/wvf0-ze94
title: EVN Observation EL064A
DOI: 10.48717/63pc-b014
title: EVN Observation EC088B
DOI: 10.48717/f71g-0c10
title: EVN Observation GP060
DOI: 10.48717/cqsb-dx26
title: EVN Experiment GP060
DOI: 10.48717/z3v3-4s64
title: EVN Observation EG119B
DOI: 10.48717/abs4-1y88
title: EVN Experiment EG119
DOI: 10.48717/9fk5-ae66
title: EVN Observation N22C3
DOI: 10.48717/6a0a-5m25
title: EVN Experiment N22C3
DOI: 10.48717/27sw-n408
title: EVN Observation EP124A
DOI: 10.48717/8s72-h543
title: EVN Observation EC089B
DOI: 10.48717/9rvj-2010
title: EVN Observation GM081D
DOI: 10.48717/6nqd-1k97
title: EVN Experiment GM081
DOI: 10.48717/8b42-ry65
title: EVN Observation N22X1
DOI: 10.15468/dl.dxaatq
title: Occurrence Download

description: A dataset containing 557 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Vulpes vulpes",
 "HasCoordinate is true",
 "Country is Czechia"
 ]
}

 The dataset includes 557 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013306-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48717/36pq-d878
title: EVN Experiment N22X1
DOI: 10.15468/dl.zugq79
title: Occurrence Download

description: A dataset containing 163 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Vulpes vulpes",
 "HasCoordinate is true",
 "Country is Slovakia"
 ]
}

 The dataset includes 163 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013307-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48717/7jgp-e331
title: EVN Observation EB092F
DOI: 10.48717/zfhz-7670
title: EVN Observation EM166A
DOI: 10.48717/vtzj-3z12
title: EVN Observation EC088C
DOI: 10.48717/3n8t-4s58
title: EVN Observation EC088D
DOI: 10.48717/17v7-3s36
title: EVN Experiment EB091
DOI: 10.48717/b4r7-rh33
title: EVN Observation EB091C
DOI: 10.48717/qn6g-k616
title: EVN Observation EB092B
DOI: 10.48717/8c28-1489
title: EVN Observation EB092E
DOI: 10.48717/2nhr-x962
title: EVN Observation EM166B
DOI: 10.48717/89cc-qc33
title: EVN Observation EB096L
DOI: 10.48717/ztm8-1280
title: EVN Observation EB096K
DOI: 10.48717/g0yz-3772
title: EVN Observation N22L3
DOI: 10.48717/x4v6-0977
title: EVN Experiment N22L3
DOI: 10.48717/kts9-5126
title: EVN Observation EP124B
DOI: 10.48717/q23w-7295
title: EVN Observation N22K1
DOI: 10.48717/0xwb-d483
title: EVN Experiment N22K1
DOI: 10.15468/dl.zy3uyd
title: Occurrence Download

description: A dataset containing 1071 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Oman",
 "Year is 2020",
 "InstitutionCode is iNaturalist"
 ]
}

 The dataset includes 1071 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013308-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48717/9jar-yg51
title: EVN Observation EC089A
DOI: 10.48717/xbqn-tf29
title: EVN Experiment EC089
DOI: 10.48717/a4b9-6z08
title: EVN Observation ES103C
DOI: 10.48717/8pcq-zf53
title: EVN Observation ES103D
DOI: 10.48717/qbva-p674
title: EVN Observation EM156C
DOI: 10.48717/keng-6545
title: EVN Observation EB096J
DOI: 10.48717/qt8p-yr86
title: EVN Observation EC088A
DOI: 10.48717/7bw6-a007
title: EVN Observation EG120
DOI: 10.48717/djxv-fk09
title: EVN Experiment EG120
DOI: 10.48717/acan-xs41
title: EVN Observation EM166C
DOI: 10.48717/weqe-yw98
title: EVN Experiment EM166
DOI: 10.48717/2v66-bq82
title: EVN Observation EB092D
DOI: 10.48717/19r3-gq20
title: EVN Observation EB092C
DOI: 10.48717/h7qa-xy23
title: EVN Observation EL064B
DOI: 10.48717/vec9-7175
title: EVN Experiment EL064
DOI: 10.48717/92jx-vr31
title: EVN Observation ES103E
DOI: 10.48717/77tc-p757
title: EVN Experiment EP124
DOI: 10.48717/914c-ej15
title: EVN Observation EP124C
DOI: 10.48717/sk9p-9717
title: EVN Observation ES103B
DOI: 10.48717/vmfh-fk48
title: EVN Observation EK051F
DOI: 10.48717/zjy7-3158
title: EVN Observation EK051G
DOI: 10.48717/1424-bf40
title: EVN Observation EK051H
DOI: 10.48717/se3f-1154
title: EVN Experiment EK051
DOI: 10.48717/2rxs-8e70
title: EVN Experiment EB096
DOI: 10.48717/94gk-d886
title: EVN Observation EB096M
DOI: 10.48717/vcdt-xb22
title: EVN Observation EV024E
DOI: 10.48717/pz1j-6k57
title: EVN Observation RG013C
DOI: 10.48717/nsmp-bq45
title: EVN Observation RG013B
DOI: 10.48717/940t-xv84
title: EVN Observation EB092A
DOI: 10.48717/21z7-0535
title: EVN Experiment EB092
DOI: 10.48717/6sdy-8384
title: EVN Observation EN011C
DOI: 10.48717/5kfn-ms49
title: EVN Observation EG123A
DOI: 10.48717/j34f-7661
title: EVN Observation EC082F
DOI: 10.48717/qbjx-6m41
title: EVN Experiment EC082
DOI: 10.48717/fw1y-ym86
title: EVN Observation EA065J
DOI: 10.48717/dnew-9s26
title: EVN Observation EV024F
DOI: 10.48717/xrxc-vp05
title: EVN Observation RG013D
DOI: 10.48717/rzcb-3r33
title: EVN Observation RG013E
DOI: 10.48717/9x7t-8y79
title: EVN Observation EG123B
DOI: 10.48717/t7v5-zy38
title: EVN Observation EA065K
DOI: 10.48717/37x1-ev66
title: EVN Experiment EA065
DOI: 10.48717/qhvq-4091
title: EVN Observation EW031A
DOI: 10.48717/6ph5-a438
title: EVN Observation RA006
DOI: 10.48717/kbm1-jd38
title: EVN Experiment RA006
DOI: 10.48717/dbwa-b366
title: EVN Observation EV024G
DOI: 10.48717/yacy-ax77
title: EVN Observation EJ027B
DOI: 10.48717/8jq8-he34
title: EVN Observation ES103H
DOI: 10.48717/e0zx-j086
title: EVN Observation GV028B
DOI: 10.48717/txtj-vb57
title: EVN Observation EC088E
DOI: 10.48717/tmga-zp73
title: EVN Observation N23L1
DOI: 10.48717/va8b-z895
title: EVN Experiment N23L1
DOI: 10.48717/fkfv-gh30
title: EVN Observation ES103I
DOI: 10.48717/7x9b-m864
title: EVN Observation EJ027D
DOI: 10.48717/8c6v-5h05
title: EVN Observation ES103G
DOI: 10.48717/74qt-3787
title: EVN Observation EJ027C
DOI: 10.48717/8xgk-8910
title: EVN Observation ES106A
DOI: 10.48717/24h0-6z93
title: EVN Observation EM169
DOI: 10.48717/haj2-ja74
title: EVN Experiment EM169
DOI: 10.48717/an4h-mn12
title: EVN Observation EM168B
DOI: 10.48717/mp71-j347
title: EVN Observation EL065A
DOI: 10.48717/3d30-7d12
title: EVN Observation N23X1
DOI: 10.48717/7tab-s880
title: EVN Experiment N23X1
DOI: 10.48717/0qhy-j707
title: EVN Observation GV028C
DOI: 10.48717/1hhh-9a75
title: EVN Observation ES106B
DOI: 10.48717/m3eg-6g32
title: EVN Experiment ES106
DOI: 10.48717/mkmh-0045
title: EVN Observation ED050B
DOI: 10.48717/35ea-c726
title: EVN Experiment ED050
DOI: 10.48717/5m9t-h230
title: EVN Observation EC088G
DOI: 10.48717/3s6k-vn77
title: EVN Observation EL065B
DOI: 10.48717/nf66-gj44
title: EVN Observation EM168A
DOI: 10.48717/sx17-mh70
title: EVN Experiment EM168
DOI: 10.48717/d9md-mb42
title: EVN Observation EM170A
DOI: 10.15468/dl.mycrf6
title: Occurrence Download

description: A dataset containing 28493 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Germany",
 "OccurrenceStatus is Present",
 "TaxonKey is Lacerta agilis"
 ]
}

 The dataset includes 28493 records from 120 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013309-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f4thhu
title: Occurrence Download

description: A dataset containing 278 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Vulpes vulpes",
 "HasCoordinate is true",
 "Country is Hungary"
 ]
}

 The dataset includes 278 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013310-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48717/a27w-hv71
title: EVN Observation ES103J
DOI: 10.48717/z2ze-bv69
title: EVN Experiment ES103
DOI: 10.48717/95wr-z530
title: EVN Experiment N23C1
DOI: 10.48717/9x5p-s532
title: EVN Observation N23C1
DOI: 10.48717/27qz-hf56
title: EVN Observation EC088H
DOI: 10.48717/s3g3-z215
title: EVN Observation GV028A
DOI: 10.48717/xstw-v306
title: EVN Experiment GV028
DOI: 10.48717/j67v-s104
title: EVN Observation EC088F
DOI: 10.48717/n7mh-rv16
title: EVN Experiment EC088
DOI: 10.48717/w9ew-r422
title: EVN Observation EO019B
DOI: 10.48717/nxme-1m07
title: EVN Experiment EO019
DOI: 10.48717/rxw7-ta94
title: EVN Experiment EJ027
DOI: 10.48717/zf3g-3g61
title: EVN Observation EJ027A
DOI: 10.48717/49nt-nf59
title: EVN Observation EK053A
DOI: 10.48717/e6y0-2x57
title: EVN Observation EK053B
DOI: 10.48717/c1zx-he13
title: EVN Observation EW031B
DOI: 10.48717/e6am-kw25
title: EVN Observation EW034
DOI: 10.48717/hhbt-et92
title: EVN Experiment EW034
DOI: 10.48717/5dy9-5546
title: EVN Observation EG123C
DOI: 10.48717/4zdd-n315
title: EVN Observation EN011D
DOI: 10.7910/dvn/saibuu
title: Replication Data for: Does Corruption Increase Inequality? Evidence from a European Union Fact-Based Assessment

description: Data used to prepare analyses and outputs used in "Does Corruption Increase Inequality? Evidence from a European Union Fact-Based Assessment"
DOI: 10.48717/9r0a-3q71
title: EVN Observation EL062A
DOI: 10.48717/daf1-v380
title: EVN Observation EP125A
DOI: 10.48717/8j3j-zc70
title: EVN Observation EK052A
DOI: 10.48717/nmj5-s924
title: EVN Observation EA069A
DOI: 10.48717/v1xq-zm90
title: EVN Observation N23C2
DOI: 10.15468/dl.pvh9vz
title: Occurrence Download

description: A dataset containing 420 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Oman",
 "Year is 2021",
 "InstitutionCode is iNaturalist"
 ]
}

 The dataset includes 420 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013311-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48717/n1sw-kz50
title: EVN Experiment N23C2
DOI: 10.48717/21z4-0536
title: EVN Observation EM164E
DOI: 10.48717/d26s-3491
title: EVN Observation EV024H
DOI: 10.48717/vc6h-vm06
title: EVN Observation N23M1
DOI: 10.48717/674t-ww30
title: EVN Experiment N23M1
DOI: 10.48717/madh-a906
title: EVN Observation EH042B
DOI: 10.48717/79da-v542
title: EVN Observation EG125
DOI: 10.48717/tcd4-rv35
title: EVN Experiment EG125
DOI: 10.48717/1ja9-1n61
title: EVN Observation EH042A