cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25346/s6collfi/8j4ajh
title: msXL_240r_MMSI+overhead+M0420VI_04_F.tif
DOI: 10.25346/s6collfi/vk9nto
title: msXL_240r_MMSI+overhead+M0400UV_54_F.tif
DOI: 10.25346/s6collfi/onbiwe
title: msXL_240r_MMSI+overhead+M0400UV_03_F.tif
DOI: 10.25346/s6collfi/9nzazo
title: msXL_240r_MMSI+overhead+M0385UV_53_F.tif
DOI: 10.25346/s6collfi/24b8wq
title: msXL_240r_MMSI+overhead+M0385UV_02_F.tif
DOI: 10.25346/s6collfi/mkbx6t
title: msXL_240r_MMSI+overhead+M0365UV_52_F.tif
DOI: 10.25346/s6collfi/2ymmjg
title: msXL_240r_MMSI+overhead+M0365UV_01_F.tif
DOI: 10.25346/s6collfi/2xk0in
title: msXL_027v_MMSI+overhead+W615R72_51_F.tif
DOI: 10.25346/s6collfi/rgg0c8
title: msXL_027v_MMSI+overhead+W61587C_47_F.tif
DOI: 10.25346/s6collfi/xk6c63
title: msXL_027v_MMSI+overhead+W615665_44_F.tif
DOI: 10.25346/s6collfi/hgpxpd
title: msXL_027v_MMSI+overhead+W530R72_50_F.tif
DOI: 10.25346/s6collfi/pmym1b
title: msXL_027v_MMSI+overhead+W53087C_46_F.tif
DOI: 10.25346/s6collfi/zxykmn
title: msXL_027v_MMSI+overhead+W530665_43_F.tif
DOI: 10.25346/s6collfi/px2ilf
title: msXL_027v_MMSI+overhead+W505R72_49_F.tif
DOI: 10.25346/s6collfi/el1j0n
title: msXL_027v_MMSI+overhead+W50587C_45_F.tif
DOI: 10.25346/s6collfi/72ic2v
title: msXL_027v_MMSI+overhead+W505665_42_F.tif
DOI: 10.25346/s6collfi/w2wpma
title: msXL_027v_MMSI+overhead+W450R72_48_F.tif
DOI: 10.25346/s6collfi/nyek5g
title: msXL_027v_MMSI+overhead+W450R25_32_F.tif
DOI: 10.25346/s6collfi/jqx5n8
title: msXL_027v_MMSI+overhead+W450G58_36_F.tif
DOI: 10.25346/s6collfi/b0iwmq
title: msXL_027v_MMSI+overhead+W450B47_40_F.tif
DOI: 10.25346/s6collfi/6ualph
title: msXL_027v_MMSI+overhead+W450665_41_F.tif
DOI: 10.25346/s6collfi/zb4fcv
title: msXL_027v_MMSI+overhead+W400R25_31_F.tif
DOI: 10.25346/s6collfi/msthbo
title: msXL_027v_MMSI+overhead+W400G58_35_F.tif
DOI: 10.25346/s6collfi/3rwxvr
title: msXL_027v_MMSI+overhead+W400B47_39_F.tif
DOI: 10.25346/s6collfi/tciy4t
title: msXL_027v_MMSI+overhead+W385R25_30_F.tif
DOI: 10.25346/s6collfi/fsoqoi
title: msXL_027v_MMSI+overhead+W385G58_34_F.tif
DOI: 10.25346/s6collfi/oicjux
title: msXL_027v_MMSI+overhead+W385B47_38_F.tif
DOI: 10.25346/s6collfi/punyem
title: msXL_027v_MMSI+overhead+W365R25_29_F.tif
DOI: 10.15468/dl.mhst5u
title: Occurrence Download

description: A dataset containing 1627 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Carausius morosus"
 ]
}

 The dataset includes 1627 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006557-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6collfi/yz8ljl
title: msXL_027v_MMSI+overhead+W365G58_33_F.tif
DOI: 10.25346/s6collfi/1ng1kz
title: msXL_027v_MMSI+overhead+W365B47_37_F.tif
DOI: 10.25346/s6collfi/z4spxx
title: msXL_027v_MMSI+overhead+TX940IR_26_F.tif
DOI: 10.25346/s6collfi/67jler
title: msXL_027v_MMSI+overhead+TX735IR_25_F.tif
DOI: 10.25346/s6collfi/eespwc
title: msXL_027v_MMSI+overhead+TX630RD_24_F.tif
DOI: 10.25346/s6collfi/biysw2
title: msXL_027v_MMSI+overhead+TX1050R_28_F.tif
DOI: 10.25346/s6collfi/yaaoc0
title: msXL_027v_MMSI+overhead+TX1050R_27_F.tif
DOI: 10.25346/s6collfi/zwwc1j
title: msXL_027v_MMSI+overhead+RR940IR_23_F.tif
DOI: 10.25346/s6collfi/yskey8
title: msXL_027v_MMSI+overhead+RR450RB_22_F.tif
DOI: 10.58020/ct69-kn76
title: Vitamin D and Type 2 Diabetes Study
DOI: 10.25346/s6collfi/iot6nb
title: msXL_027v_MMSI+overhead+RL940IR_21_F.tif
DOI: 10.25346/s6collfi/jx9orm
title: msXL_027v_MMSI+overhead+RL450RB_20_F.tif
DOI: 10.25346/s6collfi/wsphu4
title: msXL_027v_MMSI+overhead+M1050IR_19_F.tif
DOI: 10.25346/s6collfi/dfoxit
title: msXL_027v_MMSI+overhead+M0940IR_67_F.tif
DOI: 10.25346/s6collfi/4cyukv
title: msXL_027v_MMSI+overhead+M0940IR_18_F.tif
DOI: 10.25346/s6collfi/n85axp
title: msXL_027v_MMSI+overhead+M0850IR_66_F.tif
DOI: 10.25346/s6collfi/xqogj7
title: msXL_027v_MMSI+overhead+M0850IR_17_F.tif
DOI: 10.25346/s6collfi/rql3bx
title: msXL_027v_MMSI+overhead+M0780IR_16_F.tif
DOI: 10.25346/s6collfi/vb82sa
title: msXL_027v_MMSI+overhead+M0735IR_65_F.tif
DOI: 10.57783/rbwv-ds30
title: North Carolina HCUP
DOI: 10.25346/s6collfi/luhrso
title: msXL_027v_MMSI+overhead+M0735IR_15_F.tif
DOI: 10.25346/s6collfi/kbt5wk
title: msXL_027v_MMSI+overhead+M0700IR_14_F.tif
DOI: 10.25346/s6collfi/mmwy7a
title: msXL_027v_MMSI+overhead+M0655DR_64_F.tif
DOI: 10.25346/s6collfi/6clqck
title: msXL_027v_MMSI+overhead+M0655DR_13_F.tif
DOI: 10.25346/s6collfi/pvtvtx
title: msXL_027v_MMSI+overhead+M0630RD_63_F.tif
DOI: 10.25346/s6collfi/dbosyk
title: msXL_027v_MMSI+overhead+M0630RD_12_F.tif
DOI: 10.25346/s6collfi/vllgvc
title: msXL_027v_MMSI+overhead+M0615RO_62_F.tif
DOI: 10.25346/s6collfi/9zpnkw
title: msXL_027v_MMSI+overhead+M0615RO_11_F.tif
DOI: 10.25346/s6collfi/czz007
title: msXL_027v_MMSI+overhead+M0590AM_61_F.tif
DOI: 10.25346/s6collfi/ar3kga
title: msXL_027v_MMSI+overhead+M0590AM_10_F.tif
DOI: 10.25346/s6collfi/5rnx06
title: msXL_027v_MMSI+overhead+M0560LI_60_F.tif
DOI: 10.25345/c5m902g85
title: MassIVE MSV000100006 - Proteomic Profiling of Large Extracellular Vesicles Reveals Distinct Molecular Signatures Associated with Breast Cancer Brain Metastasis
title: Breast cancer brain metastasis (BCBM) remains a significant clinical challenge, with limited understanding of how tumor-derived extracellular vesicles (TDEVs) contribute to its progression. Large extracellular vesicles (lEVs), which are nanovesicles sized between 100-1000 nm, represent a distinct EV subtype capable of carrying molecular cargo that reflects and potentially influences the metastatic behavior of tumor cells. We conducted a comprehensive quantitative proteomic analysis of lEVs from four breast cancer cell lines, including a brain-tropic MDA-MB-231BR, to elucidate the role of lEVs in BCBM. Label-free LC-MS/MS identified 1708 proteins at 1% FDR. Analysis revealed 145 proteins consistently altered in the 231BR line compared to the others. Notably, CLIC4, MSN, LDHB, RPLP2, EEF1G, and PSMD3 were upregulated. Pathway analysis identified the activation of CXCR4 and inhibition of RhoGDI signaling, pathways consistent with enhanced motility. Importantly, comparative analysis with whole cell membrane (WCM) proteomes demonstrated lEVs are a distinct compartment, not a random membrane sample. lEVs were uniquely enriched for adhesion-related proteins and showed significantly stronger activation of pro-metastatic pathways: Ephrin Receptor and Integrin, than the WCM. These findings establish a molecular framework linking lEV proteomes to brain-metastatic potential and highlight lEVs as promising compartment for studying tumor signaling and as potential biomarkers of BCBM.
DOI: 10.25346/s6collfi/rfoywu
title: msXL_027v_MMSI+overhead+M0560LI_09_F.tif
DOI: 10.25346/s6collfi/rlvrdd
title: msXL_027v_MMSI+overhead+M0530GN_59_F.tif
DOI: 10.25346/s6collfi/cevgno
title: msXL_027v_MMSI+overhead+M0530GN_08_F.tif
DOI: 10.25346/s6collfi/qqtxvy
title: msXL_027v_MMSI+overhead+M0505CN_58_F.tif
DOI: 10.25346/s6collfi/0xpr8u
title: msXL_027v_MMSI+overhead+M0505CN_07_F.tif
DOI: 10.15468/dl.ch28uj
title: Occurrence Download

description: A dataset containing 89587 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Amazona aestiva"
 ]
}

 The dataset includes 89587 records from 131 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006534-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6collfi/oqg1qc
title: msXL_027v_MMSI+overhead+M0470LB_57_F.tif
DOI: 10.25346/s6collfi/4fbwd2
title: msXL_027v_MMSI+overhead+M0470LB_06_F.tif
DOI: 10.25346/s6collfi/oijrzc
title: msXL_027v_MMSI+overhead+M0450RB_56_F.tif
DOI: 10.25346/s6collfi/ubbcuw
title: msXL_027v_MMSI+overhead+M0450RB_05_F.tif
DOI: 10.25346/s6collfi/d76ffv
title: msXL_027v_MMSI+overhead+M0420VI_55_F.tif
DOI: 10.25346/s6collfi/tnqyvf
title: msXL_027v_MMSI+overhead+M0420VI_04_F.tif
DOI: 10.25346/s6collfi/xkgi4j
title: msXL_027v_MMSI+overhead+M0400UV_54_F.tif
DOI: 10.25346/s6collfi/c2n5to
title: msXL_027v_MMSI+overhead+M0400UV_03_F.tif
DOI: 10.25346/s6collfi/xifguj
title: msXL_027v_MMSI+overhead+M0385UV_53_F.tif
DOI: 10.25346/s6collfi/a9forj
title: msXL_027v_MMSI+overhead+M0385UV_02_F.tif
DOI: 10.25346/s6collfi/uiaoec
title: msXL_027v_MMSI+overhead+M0365UV_52_F.tif
DOI: 10.25346/s6collfi/ndtktq
title: msXL_027v_MMSI+overhead+M0365UV_01_F.tif
DOI: 10.25346/s6collfi/edw1h2
title: msXL_027r_MMSI+overhead+W615R72_51_F.tif
DOI: 10.25346/s6collfi/pawecp
title: msXL_027r_MMSI+overhead+W61587C_47_F.tif
DOI: 10.25346/s6collfi/lneoa7
title: msXL_027r_MMSI+overhead+W615665_44_F.tif
DOI: 10.25346/s6collfi/ywqepn
title: msXL_027r_MMSI+overhead+W530R72_50_F.tif
DOI: 10.25346/s6collfi/187f49
title: msXL_027r_MMSI+overhead+W53087C_46_F.tif
DOI: 10.25346/s6collfi/cbyglf
title: msXL_027r_MMSI+overhead+W530665_43_F.tif
DOI: 10.25346/s6collfi/136pxc
title: msXL_027r_MMSI+overhead+W505R72_49_F.tif
DOI: 10.25346/s6collfi/mo6xjf
title: msXL_027r_MMSI+overhead+W50587C_45_F.tif
DOI: 10.25346/s6collfi/v4n9dz
title: msXL_027r_MMSI+overhead+W505665_42_F.tif
DOI: 10.25346/s6collfi/99gsaf
title: msXL_027r_MMSI+overhead+W450R72_48_F.tif
DOI: 10.25346/s6collfi/8q4qia
title: msXL_027r_MMSI+overhead+W450R25_32_F.tif
DOI: 10.25346/s6collfi/pxkah4
title: msXL_027r_MMSI+overhead+W450G58_36_F.tif
DOI: 10.25346/s6collfi/4nup2a
title: msXL_027r_MMSI+overhead+W450B47_40_F.tif
DOI: 10.25346/s6collfi/gluwhc
title: msXL_027r_MMSI+overhead+W450665_41_F.tif
DOI: 10.25346/s6collfi/bw4gjm
title: msXL_027r_MMSI+overhead+W400R25_31_F.tif
DOI: 10.25346/s6collfi/ttghuk
title: msXL_027r_MMSI+overhead+W400G58_35_F.tif
DOI: 10.25346/s6collfi/lr2xka
title: msXL_027r_MMSI+overhead+W400B47_39_F.tif
DOI: 10.25346/s6collfi/htlkhz
title: msXL_027r_MMSI+overhead+W385R25_30_F.tif
DOI: 10.25346/s6collfi/qokrck
title: msXL_027r_MMSI+overhead+W385G58_34_F.tif
DOI: 10.25346/s6collfi/vryuxb
title: msXV_028v_MMSIoverhead.tif
DOI: 10.25346/s6collfi/ulyiah
title: msXV_028r_MMSIoverhead.tif
DOI: 10.25346/s6collfi/ojzpmr
title: msXV_007v_MMSIoverhead.tif
DOI: 10.25346/s6collfi/zmqm6d
title: msXL_344v_MMSIoverhead.tif
DOI: 10.25346/s6collfi/81ne9b
title: msXL_344r_MMSIoverhead.tif
DOI: 10.25346/s6collfi/umvkzr
title: msXL_341v_MMSIoverhead.tif
DOI: 10.5281/zenodo.17704565
title: Laves Phase Precipitation Dataset for Steels Design

description: This dataset contains manually curated quantitative data on Laves phase precipitation in steels, extracted from peer-reviewed publications. It includes alloy composition, thermal and thermomechanical processing variables, and microstructural metrics such as precipitate size and volume fraction, with DOI-based traceability to every source.
DOI: 10.25346/s6collfi/4a0nyf
title: msXL_341r_MMSIoverhead.tif
DOI: 10.6084/m9.figshare.30698471
title: Planilha de dados

description: Pesquisa em hospital oncológico de referência da região Norte do Brasil, com o fim de analisar pacientes com sarcoma de partes moles tratados por médicos não ortopedistas oncologistas, avaliando perfil epidemiológico, estadiamento e desafios terapêuticos.
DOI: 10.6084/m9.figshare.30698471.v1
title: Planilha de dados

description: Pesquisa em hospital oncológico de referência da região Norte do Brasil, com o fim de analisar pacientes com sarcoma de partes moles tratados por médicos não ortopedistas oncologistas, avaliando perfil epidemiológico, estadiamento e desafios terapêuticos.
DOI: 10.15468/dl.cysy9v
title: Occurrence Download

description: A dataset containing 15731 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Prunus ramburii, Datura innoxia, Orinus kokonorica, Mosla soochouensis, Vitis davidi, Shorea leprosula, Scyphiphora hydrophylacea, Sicyos pachycarpa, Quercus fabrei, Dorcoceras hygrometricum, Erythranthe lutea, Fragaria mandshurica)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 15731 records from 464 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006558-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25910/9dad-x127
title: Building readiness in community-based organisations to enable the implementation of public health interventions for adults and older adults: a scoping review

description: Data and supplementary file from: 'Building readiness in community-based organisations to enable the implementation of public health interventions for adults and older adults: a scoping review' Background: A key challenge to implementing and scaling up evidence-based interventions (EBIs) into practice is organisational readiness; described as an organisation’s motivation, general capacities, and capabilities specific to the EBI. Building organisational readiness has been investigated in some health disciplines (e.g., mental health). However, the importance of building organisational readiness to effectively implement public health EBIs for adults and older adults in the community setting remains largely unexplored. Our aim was to examine how readiness was defined and measured, what strategies were used to build readiness, and the relationship between readiness-building strategies and implementation, service-level, and person-level outcomes. Methods: In this scoping review, we searched seven databases and conducted forward and backward citation tracking. From a pool of eight reviewers, combinations of two reviewers independently screened references for eligibility. A single reviewer extracted data, and a second reviewer checked data. Results for each implementation, service-level and person-level outcome in each study were extracted and categorised as favourable, nonsignificant, or unfavourable. Results: Twelve studies were included, which implemented a mix of different public health EBIs to almost 40,000 participants (n = 37,883; 54% women) across varied community settings. Only four studies defined readiness; all used different definitions. Five studies used five different instruments to assess readiness, all with poor psychometric properties. All studies used multiple strategies to build readiness (range 4–20 strategies per study), with all using strategies to assess, plan and monitor implementation of the EBI (i.e., ‘evaluative and iterative strategies’) and strategies to support collaboration between organisations delivering the EBI (i.e., ‘develop interest-holder interrelationships’). Three-quarters of the strategies focused on building the organisation’s capability to deliver the specific EBI (e.g., assessing readiness, conducting educational meetings) and were delivered by external support teams. Exploring the relationship between readiness-building strategies and study outcomes indicated more favourable than unfavourable outcomes, particularly for implementation and service-level outcomes (38/48; 79% favourable). Conclusions: Within this limited sample, the use of readiness-building strategies improved the implementation of public health EBIs in community organisations. However, consistency of definitions and terminology and more sophisticated testing of readiness-building strategies will help confirm how best to do this. Trial registration: Open Science Framework, May 5, 2024.
DOI: 10.25345/c5gh9bp4g
title: MassIVE MSV000100007 - Quantitation of Cervical Proteome Remodeling
title: There were 3 project batches in which 30 monkey cervix tissue, 38 human cervix tissue, and 32 monkey cervix ECM enriched pellets were analyzed. Proteomic label free quantitation was performed in order to compare the nonpregnant and pregnant cervical proteomes of different tissue types. The pregnant monkey samples were also organized into separate groups based on the pregnancy trimesters when they were collected. Jayanta K. Chakrabarty, Anu Jain, Hongyu Li, Echo Xu, Lipi Das, Alex W. Rookyard, Davey Liu, David S. Johnson, Joy-Sarah Y. Vink, Kristin M. Myers, Lewis M. Brown.
DOI: 10.15468/dl.hx2pa3
title: Occurrence Download

description: A dataset containing 507 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (6 - Inventaire général de la flore vasculaire et de la fonge sur le TAG du CBNMed-Divers jeux de données floristiques produits par le CBNMED sur son territoire d'agrément, Données de l'inventaire forestier national de l'IGN-Relevés floristiques du protocole de l'Inventaire National Forestier de l'IGN)",
 "Geometry POLYGON((-2.0597 43.50398,-9.39308 44.1728,-9.10264 42.19657,-6.58558 41.6202,-7.14224 37.08824,-5.54487 35.92102,-1.91448 36.46791,0.45737 38.73032,0.07013 39.55622,3.31327 41.93942,3.79732 42.40578,-2.0597 43.50398))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Senecio inaequidens"
 ]
}

 The dataset includes 507 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006560-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17909/vff8-3p23
title: P/2023 V6 Data 

description: This data was obtained as part of GO-17795 and used in the analysis of P/2023 V6.
DOI: 10.34740/kaggle/dsv/13856324
title: Dataset Objects Launched into Outer Space UNOOSA
DOI: 10.15468/dl.c5txn8
title: Occurrence Download

description: A dataset containing 1168 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is United States of America",
 "Geometry POLYGON((-166.65115 21.83431,-50.2176 21.83431,-50.2176 73.28169,-166.65115 73.28169,-166.65115 21.83431))",
 "TaxonKey is Elymus repens"
 ]
}

 The dataset includes 1168 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006561-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p14qju9c
title: Greater Sage-Grouse Survival Encounter Histories and Counts in Association with Precipitation, Temperature, and Drought in the Great Basin, USA

description: A suite of tables filled with greater sage-grouse (Centrocercus urophasianus; hereafter sage-grouse) survival data and environmental covariate values, extracted to sage-grouse nest, brood, and general locations, as well as lek locations. We quantified 8 indicators of temperature and/or moisture conditions across time spans representing lags of 0-1.5 years, while accounting for other environmental factors.
DOI: 10.15468/dl.96thut
title: Occurrence Download

description: A dataset containing 1632 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "TaxonKey is Anopheles claviger"
 ]
}

 The dataset includes 1632 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006563-251120083545085/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.30698750.v1
title: Dataset

description: Dataset for the studyThis repository contains the replication dataset, analysis scripts, and experimental materials for the research paper <b>"Improving Phishing Resilience with AI-Generated Training: Large-Scale Evidence on Prompting, Personalization, and Duration"</b>.Study OverviewThe associated study investigates the effectiveness of Large Language Models (LLMs) in generating tailored phishing awareness training. Through two controlled experiments involving 480 participants, the research examines how Prompting Strategies (Study 1), Personalization, and Training Duration (Study 2) influence user detection accuracy, recall, and F1-scores against realistic phishing emails.Repository ContentsThis archive provides all necessary resources to reproduce the statistical analyses and replicate the experimental design:<b>📂 Datasets:</b> Anonymized and cleaned participant data (CSV format) for Study 1 ($N=80$) and Study 2 ($N=400$), including performance metrics (Pre/Post) and psychometric profiles (BFI-2-XS, StP-II-B, TEIQue-SF).<b>📂 Analysis Code:</b> Python scripts using <i>pandas</i> and <i>pingouin</i> to replicate the Mixed ANOVA, t-tests, and correlation analyses reported in the paper.<b>📂 Experimental Materials:</b> The complete set of email stimuli (genuine vs. phishing) developed using the NIST Phish Scale, including screenshots and HTML templates.<b>📂 LLM Prompts:</b> The exact system and user prompts used to generate the training modules via OpenAI's model, covering four prompting strategies (Direct-profile, Few-shot, Table-based, Guideline-based) and personalization logic.<b>📂 Instruments:</b> Details on the psychometric scales and the ad-hoc training reaction questionnaire used in the study.Methodological NoteThe data collection was conducted on the Prolific platform with approval from the Ethics Committee of the University of Bari. All Personal Identifiable Information (PII) has been removed to ensure participant anonymity.
DOI: 10.6084/m9.figshare.30698750
title: Dataset

description: Dataset for the studyThis repository contains the replication dataset, analysis scripts, and experimental materials for the research paper <b>"Improving Phishing Resilience with AI-Generated Training: Large-Scale Evidence on Prompting, Personalization, and Duration"</b>.Study OverviewThe associated study investigates the effectiveness of Large Language Models (LLMs) in generating tailored phishing awareness training. Through two controlled experiments involving 480 participants, the research examines how Prompting Strategies (Study 1), Personalization, and Training Duration (Study 2) influence user detection accuracy, recall, and F1-scores against realistic phishing emails.Repository ContentsThis archive provides all necessary resources to reproduce the statistical analyses and replicate the experimental design:<b>📂 Datasets:</b> Anonymized and cleaned participant data (CSV format) for Study 1 ($N=80$) and Study 2 ($N=400$), including performance metrics (Pre/Post) and psychometric profiles (BFI-2-XS, StP-II-B, TEIQue-SF).<b>📂 Analysis Code:</b> Python scripts using <i>pandas</i> and <i>pingouin</i> to replicate the Mixed ANOVA, t-tests, and correlation analyses reported in the paper.<b>📂 Experimental Materials:</b> The complete set of email stimuli (genuine vs. phishing) developed using the NIST Phish Scale, including screenshots and HTML templates.<b>📂 LLM Prompts:</b> The exact system and user prompts used to generate the training modules via OpenAI's model, covering four prompting strategies (Direct-profile, Few-shot, Table-based, Guideline-based) and personalization logic.<b>📂 Instruments:</b> Details on the psychometric scales and the ad-hoc training reaction questionnaire used in the study.Methodological NoteThe data collection was conducted on the Prolific platform with approval from the Ethics Committee of the University of Bari. All Personal Identifiable Information (PII) has been removed to ensure participant anonymity.
DOI: 10.5281/zenodo.17704524
title: The results of Schmidt hammer tests and rock surface moisture examination in the Białe Skały massif, Stołowe Mountains tableland, SW Poland

description: This research was funded by National Science Centre, Poland, research project no. 2020/39/D/ST10/00861.

The record contains two datasets with raw data collected in the field: 1) Results of Schmidt hammer tests (Białe_Skały_Schmidt_dane),  2) Results of rock surface moisture tests by three devices (Protiometer, T610, T660). Both datasets were collected in the Stołowe Mountains tableland, SW Poland, in the eastern section of the Białe Skały massif. Two types of sites were tested: more densely jointed, disintegrated ones (marked as "Z" in the file) and less densely jointed, more massive ones (marked as "M" in the file).