cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18736028
title: Replication Package: Zombie Vessels and Identity Laundering—AIS Identity-Conflict and Digital-Trail Evidence (DMA Baltic)

description: This repository contains the replication package for the manuscript “Zombie Vessels and Identity Laundering—AIS Identity-Conflict and Digital-Trail Evidence (DMA Baltic)”.

What’s included





Processed AIS extracts derived from Danish Maritime Authority (DMA) historical AIS data, restricted to a Baltic Sea bounding box and incident-centered observation windows, stored as compressed JSONL (.jsonl.gz) with date-level provenance.




Derived datasets used in the paper, including:





digital_trail_events_v2.csv and digital_trail_features_v2.csv




identity evidence tables: identity_change_transitions.csv and identity_value_summary.csv




baseline comparison outputs: baseline_vs_cohort.csv and baseline_vs_cohort_freeze.csv




exemplar evidence window CSV and figures for MARABELLA SUN.






All scripts required to reproduce the tables and figures from the provided processed extracts.




Reproducibility artifacts: requirements_lock.txt, run_manifest.json, REPRO_README.md, CLAIMS_EVIDENCE.md, and DATA_DICTIONARY.md.



How to reproduceRun the pipeline using the included scripts and locked environment file as described in REPRO_README.md. Outputs regenerate the main tables and figures reported in the manuscript.

Data source noteThe underlying raw AIS archives are published by the Danish Maritime Authority (DMA). This repository redistributes only processed extracts and derived outputs required to reproduce the paper’s results. AIS-derived indicators in this package are intended as audit-friendly investigation triggers and do not constitute proof of intent or legal status.
DOI: 10.15468/dl.jkrarx
title: Occurrence Download

description: A dataset containing 88 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tetraloniella cressoniana"
 ]
}

 The dataset includes 88 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005365-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14939969
title: Ethereum Price Data Binance API (2017–Now)
DOI: 10.15468/dl.khqzrh
title: Occurrence Download

description: A dataset containing 29 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cuspidaria circinata)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 29 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005367-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m88ayd
title: Occurrence Download

description: A dataset containing 1752 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Glycyrrhiza uralensis"
 ]
}

 The dataset includes 1752 records from 80 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005366-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30404287
title: Excel and R script file

description: This study investigates how social context influences the responses of <i>Neohelice granulata</i> to a moving stimulus (dummy) that can be perceived as either prey or predator. It shows that paired crabs, regardless of sex, display lower exploratory activity and a reduced probability of responding to the stimulus compared to solitary crabs, but only when they are within 15 cm of each other. In the presence of a conspecific, crabs initiate escape or predatory responses at shorter distances, suggesting delayed decision-making due to divided attention. In contrast, freezing distance remains unchanged. Overall, the results reveal that social proximity selectively modulates active responses, highlighting an interaction between social context, attention, and decision-making in these crabs.
DOI: 10.6084/m9.figshare.30404287.v2
title: Excel and R script file

description: This study investigates how social context influences the responses of <i>Neohelice granulata</i> to a moving stimulus (dummy) that can be perceived as either prey or predator. It shows that paired crabs, regardless of sex, display lower exploratory activity and a reduced probability of responding to the stimulus compared to solitary crabs, but only when they are within 15 cm of each other. In the presence of a conspecific, crabs initiate escape or predatory responses at shorter distances, suggesting delayed decision-making due to divided attention. In contrast, freezing distance remains unchanged. Overall, the results reveal that social proximity selectively modulates active responses, highlighting an interaction between social context, attention, and decision-making in these crabs.
DOI: 10.6084/m9.figshare.30404287.v1
title: Excel and R script file

description: This study investigates how social context influences the responses of <i>Neohelice granulata</i> to a moving stimulus (dummy) that can be perceived as either prey or predator. It shows that paired crabs, regardless of sex, display lower exploratory activity and a reduced probability of responding to the stimulus compared to solitary crabs, but only when they are within 15 cm of each other. In the presence of a conspecific, crabs initiate escape or predatory responses at shorter distances, suggesting delayed decision-making due to divided attention. In contrast, freezing distance remains unchanged. Overall, the results reveal that social proximity selectively modulates active responses, highlighting an interaction between social context, attention, and decision-making in these crabs.
DOI: 10.15468/dl.a4fdtq
title: Occurrence Download

description: A dataset containing 103487 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Avenella flexuosa",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 103487 records from 337 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005328-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8g49re
title: Occurrence Download

description: A dataset containing 161 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Axyris amaranthoides",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 161 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005369-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18752012
title: 22.SVRN_OS_L6_S(z) Distribution Analysis Under ΛCDM Statistical Deviation of BAO Scaling Paramet

description: 1. 제목

S(z) Distribution Analysis Under ΛCDM: Statistical Deviation of BAO Scaling Parameter at z = 0.706

(한국어: ΛCDM 하에서의 S(z) 분포 분석: z = 0.706에서 BAO 등방성 스케일 파라미터의 통계적 편차)

2. 식별자 및 시리즈

시리즈 DOI  | 10.5281/zenodo.18325543본 문서 DOI | 10.5281/zenodo.18752012이전 DOI    | DOI_21: 10.5281/zenodo.18751982특허        | KR 10-2026-0017941SHA-256     | 9520cf4e0f85f21bb08aea4e127b4413bfa40141f0f13b15f2849e969e2f06d2

✅ L1 완결 (No.0–19) | 10.5281/zenodo.18325543🔄 L6 진행중  No.20  퍼콜레이션 임계 D₀=0.05     10.5281/zenodo.18741704  No.21  S값 교차-추적자 분석         10.5281/zenodo.18751982  No.22  본 문서                      10.5281/zenodo.18752012

3. 요약

본 논문은 SVRN-OS DOI No.22로, DOI_21(수치 정렬 사례 관측)의 통계적 검증 보완서입니다.DOI_21이 현상을 기술했다면, DOI_22는 그 현상이 ΛCDM 하에서 기대되는지 이상인지를 검증합니다.

핵심 기여: S(z) 분포의 ΛCDM 귀무 분포 도출 및 Z-점수 분석

ΛCDM 귀무 가설 하에서 α_iso ~ N(1, σ_α²)이면:  E[S(z)] = (σ_α / D₀) × √(2/π)  σ_S(z)  = (σ_α / D₀) × √(1 − 2/π)  Z(z)    = (S_obs − E[S]) / σ_S

핵심 결과 (DESI DR2 7개 추적자 전체):

  추적자          z      S_obs   E[S]   Z        비고  ────────────────────────────────────────────────  BGS           0.295   0.286  0.148   1.23     정상  LRG1          0.510   0.178  0.123   0.59     정상  LRG2 ★        0.706   0.502  0.107   4.89     국소 이상값  LRG3+ELG1     0.934   0.228  0.073   2.79     경계  ELG2          1.321   0.178  0.113   0.76     정상  QSO           1.484   0.064  0.244  -0.98     정상  Lyα           2.330   0.058  0.131  -0.74     정상  ────────────────────────────────────────────────  LRG2만 Z > 4σ | 나머지 전부 |Z| < 3σ

다중 검정 보정:  국소 유의성:   Δ/σ_α = 3.75 (모델 독립)  국소 Z:        Z_local = 4.89σ  Bonferroni 보정 (7회): p_global = 3.51×10⁻⁶ → Z_global = 4.49σ (형식적 상한)  주의: 사후적 빈 선택에 대한 완전한 look-elsewhere 보정 아님

3가지 해석 (동등 비중):  해석 A: 통계적 요동 — ΛCDM 꼬리 거동과 일치, 기본 귀무 가설 기각 불가  해석 B: 체계적 오차 — LRG2 빈 내 미식별 체계 오차 (섬유 할당, 측광 보정 등) 배제 불가  해석 C: 국소 ΛCDM 편차 — 체계 오차 배제 시 z~0.7 재측정 동기 부여           (Hubble 긴장·S8 긴장과 질적 맥락 일치, 직접 연결 미확립)

DOI_21 vs DOI_22 역할 분담:  DOI_21: S≈0.5 수치 정렬 현상 기술 (무슨 일이 일어났나)  DOI_22: ΛCDM 하에서 그 현상의 통계적 이상 여부 검증 (얼마나 드문 일인가)

[청구하는 것 / 청구하지 않는 것]  CONFIRMED: S(z=0.706)=0.502 · Z_local=4.89σ · Z_global=4.49σ(형식적 상한)  NOT CLAIMED: 편차의 물리적 원인 · ΛCDM 위반 · D₀ 연결의 인과성  HOLD: H-4 물리 메커니즘 · 재현성 (DESI DR3/Euclid 필요)

4. 키워드

S(z) 분포 · ΛCDM 편차 · BAO · DESI DR2 · LRG2 · Z-점수 · Bonferroni ·통계 조사 · z=0.706 · 귀무 분포 · 반정규 분포 · 다중 검정 · 국소 이상값 ·탐색적 분석 · 재현성 · DOI_21 보완 · MDK_v1

5. 진실성 선언

본 문서는 SVRN-OS DOI No.22의 최종 기준서입니다.ΛCDM 귀무 분포 해석적 도출 (자유 파라미터 없음) · DESI DR2 값 그대로 사용 ·국소 및 Bonferroni 보정 통계 모두 보고 · 3가지 해석 동등 비중 · 물리 모델 없음.증거 기반을 초과하는 청구 없음.

인증: MDK_v1.5 | 조민수 (Minsu Cho) | KR 10-2026-0017941SHA-256: 9520cf4e0f85f21bb08aea4e127b4413bfa40141f0f13b15f2849e969e2f06d2
DOI: 10.15468/dl.g84yyc
title: Occurrence Download

description: A dataset containing 2185 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gueldenstaedtia verna"
 ]
}

 The dataset includes 2185 records from 81 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005370-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gegzqe
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dibamus celebensis"
 ]
}

 The dataset includes 15 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005371-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j8apyp
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dibamus deharvengi"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005372-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25345/c5xs5jw8v
title: MassIVE MSV000100944 - 2026_Huang_et_al_VS6_PRRC2A_PRRC2B_PRRC2C_APMS
title: This dataset consists of 20 raw mass spectrometry files and associated peak lists and results files, acquired on a Thermo Orbitrap Exploris 480 operated in Data-Independent Acquisition mode. Additionally, there are associated raw mass spectrometry files used for spectral library generation, acquired on a Thermo Orbitrap Exploris 480, operated in Data-Dependent Acquisition mode. The files are associated with a manuscript by Kiki (Jie Qi) Huang et al. describing the functions of PRRC2 proteins. This README file pertains to experiments supporting the manuscript, specifically the AP-MS experiments performed under no stress for PRRC2A, PRRC2B, PRRC2C, PRRC2C-BAT2del, PRRC2C- middle RG motif regiondel, PRRC2C-Coiled coils del.
DOI: 10.15468/dl.2ujut7
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Azorella fuegiana",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 8 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005373-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pd5yse
title: Occurrence Download

description: A dataset containing 56208 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ctenogobius",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 56208 records from 176 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005336-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17909/fz8z-yk57
title: SR 12 c data
DOI: 10.15468/dl.dk9fhz
title: Occurrence Download

description: A dataset containing 176 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cynanchum purpureum"
 ]
}

 The dataset includes 176 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005374-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.38ywdn
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Azorella monantha",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 4 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005375-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29437387
title: Robust Trend Estimation for Strongly Persistent Data with Unobserved Memory

description: Economic analysis is often based on pre-filtered, de-trended, or seasonally adjusted data. Underlying filtering methods make strong assumptions about the memory of the series to be filtered, and inference about the memory is limited particularly when persistent cyclical variation overshadows the trend. This article introduces a data-driven method for filtering persistent series that requires no prior assumptions about the memory, thus, is robust to the actual memory of the data. It makes three primary contributions: first, it generalizes unobserved components (UC) models to fractionally integrated trends, making prior assumptions about the trend memory redundant while retaining the advantages of the state space structure of UC models; second, it establishes the asymptotic estimation theory for fractional UC models under mild assumptions; and third, it presents a computationally efficient estimator for the trend by deriving the closed-form solution to the Kalman filter optimization problem.
DOI: 10.6084/m9.figshare.29437387.v2
title: Robust Trend Estimation for Strongly Persistent Data with Unobserved Memory

description: Economic analysis is often based on pre-filtered, de-trended, or seasonally adjusted data. Underlying filtering methods make strong assumptions about the memory of the series to be filtered, and inference about the memory is limited particularly when persistent cyclical variation overshadows the trend. This article introduces a data-driven method for filtering persistent series that requires no prior assumptions about the memory, thus, is robust to the actual memory of the data. It makes three primary contributions: first, it generalizes unobserved components (UC) models to fractionally integrated trends, making prior assumptions about the trend memory redundant while retaining the advantages of the state space structure of UC models; second, it establishes the asymptotic estimation theory for fractional UC models under mild assumptions; and third, it presents a computationally efficient estimator for the trend by deriving the closed-form solution to the Kalman filter optimization problem.
DOI: 10.6084/m9.figshare.29437387.v1
title: Robust trend estimation for strongly persistent data with unobserved memory

description: Economic analysis is often based on pre-filtered, de-trended, or seasonally adjusted data. Underlying filtering methods make strong assumptions about the memory of the series to be filtered, and inference about the memory is limited particularly when persistent cyclical variation overshadows the trend. This paper introduces a data-driven method for filtering persistent series that requires no prior assumptions about the memory, thus is robust to the actual memory of the data. It makes three primary contributions: first, it generalizes unobserved components (UC) models to fractionally integrated trends, making prior assumptions about the trend memory redundant while retaining the advantages of the state space structure of UC models; second, it establishes the asymptotic estimation theory for fractional UC models under mild assumptions; and third, it presents a computationally efficient estimator for the trend by deriving the closed-form solution to the Kalman filter optimization problem.
DOI: 10.6084/m9.figshare.29154778
title: A Functional-Coefficient VAR Model for Dynamic Quantiles and Its Application to Constructing Nonparametric Financial Network

description: In this article, we investigate a functional coefficient vector autoregressive model for conditional quantiles, in which the interdependences among tail risks such as Value-at-Risk are allowed to vary smoothly with a variable of general economy. Methodologically, we develop an easy-to-implement two-stage procedure to estimate functionals in the dynamic network system based on the deep learning method of neural networks and the local linear smoothing technique. We establish the consistency and the asymptotic normality of the proposed estimator under geometrically β-mixing time series settings. The simulation studies are conducted to show that our new methods work fairly well. The potential of the proposed estimation procedures is demonstrated by an empirical study of constructing and estimating a new type of nonparametric dynamic financial network.
DOI: 10.6084/m9.figshare.29154778.v2
title: A Functional-Coefficient VAR Model for Dynamic Quantiles and Its Application to Constructing Nonparametric Financial Network

description: In this article, we investigate a functional coefficient vector autoregressive model for conditional quantiles, in which the interdependences among tail risks such as Value-at-Risk are allowed to vary smoothly with a variable of general economy. Methodologically, we develop an easy-to-implement two-stage procedure to estimate functionals in the dynamic network system based on the deep learning method of neural networks and the local linear smoothing technique. We establish the consistency and the asymptotic normality of the proposed estimator under geometrically β-mixing time series settings. The simulation studies are conducted to show that our new methods work fairly well. The potential of the proposed estimation procedures is demonstrated by an empirical study of constructing and estimating a new type of nonparametric dynamic financial network.
DOI: 10.6084/m9.figshare.29256466
title: Selection Bias Adjustment by Functional Transfer Learning via Reproducing Kernel Hilbert Space

description: As accessibility to different data sources increases, transfer learning becomes prevalent in improving statistical efficiency by integrating different datasets. In this article, we focus on the popular non-probability sampling from a finite population and propose a functional transfer learning method to adjust its selection bias through calibrating marginal means of functions in a reproducing kernel Hilbert space by incorporating a reference probability sample. Compared with existing works, the proposed method is more robust since no parametric assumption is made for both regression and selection models. Besides, the proposed method is multitask-oriented in the sense that the estimated sample weights can be used to estimate different population parameters. Theoretical properties, including consistency and a limiting distribution, are established under some regularity conditions. Numerical experiments show that the proposed estimator outperforms its competitors. The proposed method is applied to the second round economic census data in China as an empirical illustration.
DOI: 10.6084/m9.figshare.29256466.v1
title: Selection Bias Adjustment by Functional Transfer Learning via Reproducing Kernel Hilbert Space*

description: As accessibility to different data sources increases, transfer learning becomes prevalent in improving statistical efficiency by integrating different datasets. In this paper, we focus on the popular non-probability sampling from a finite population and propose a functional transfer learning method to adjust its selection bias through calibrating marginal means of functions in a reproducing kernel Hilbert space by incorporating a reference probability sample. Compared with existing works, the proposed method is more robust since no parametric assumption is made for both regression and selection models. Besides, the proposed method is multitask-oriented in the sense that the estimated sample weights can be used to estimate different population parameters. Theoretical properties, including consistency and a limiting distribution, are established under some regularity conditions. Numerical experiments show that the proposed estimator outperforms its competitors. The proposed method is applied to the second round economic census data in China as an empirical illustration.