cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30344197.v1
title: Improving maternal outcomes of young mothers through mobile health (mHealth) and community-based interventions: Evidence from a quasi-experimental trial in Kwale County, Coastal Kenya

description: Background Inefficient referral systems and limited awareness on the importance of antenatal care (ANC) hinder expectant adolescents and young women from achieving the recommended ANC visits. Mobile health interventions and community-based clinical outreaches have emerged as potential strategies to improve ANC utilization and skilled delivery rates.<br>ObjectiveThis study evaluates the effectiveness of mHealth text messaging and community-based clinical outreaches in improving ANC uptake, skilled deliveries, and infants’ birthweights among expectant adolescents and young women in Kwale County, Coastal Kenya.<br>MethodsA quasi-experimental study was conducted across four public health facilities, comparing three groups: mHealth text messaging intervention, community-based outreaches, and a control group receiving standard care. Multivariate regression models were used to adjust for baseline differences and potential confounders. Key outcomes included ANC attendance, skilled birth attendance, and infant birthweights.<br>ResultsA total of 817 participants were included in the analysis. Participants in the mHealth (RR: 0.5 [95% CI: 0.4 – 0.7], p&lt;0.001) and outreach arms (RR: 0.1 [95% CI: 0.0 – 0.7], p=0.024), had significantly lower risk of low ANC contacts compared to the control arm participants. . Similarly, the odds of unskilled deliveries were significantly lower in the mHealth (OR: 0.2 [95% CI: 0.1 – 0.4, p&lt;0.001] and the outreach arms (OR: 0.1 [95% CI: 0.0 – 0.6], p=0.007), compared to the control group. Notably, infants born to participants in the mHealth-text message intervention (OR: 5.9 [95% CI: 1.7 – 20.8, p=0.006]) and outreach arms (OR: 6.6 [95% CI: 1.7 – 25.7], p=0.007) had significantly higher odds of low birth weight, compared to those born to control arm participants.<br>
DOI: 10.5281/zenodo.17560060
title: TLT

description: The Theory of Luminous Temporality (TLT) version V51.02 integrates foundational

concepts of time quantization through photon events with 2025 advancements in photonic

time crystals, chronon models, string theory’s emergent time, and quantum gravity

extensions. TLT posits time as quantized via luminous processes in EM-dominant systems,

now enhanced with de Sitter/Conformal Field Theory (dS/CFT) duality for dark energy

modeling, consistent with DESI 2025 observations of evolving w(z). Core postulates,

covariant formulations, and derivations are detailed, incorporating ux-induced

entanglement and Planck uctuations. Applications include precision chronometry,

biological metrics, cosmic analysis, and QG probes. SYK model chaos, LQG discreteness, and

asymptotic safety are explored for temporal dynamics. Strengths unify scales; limitations,

including environmental dependence and non-EM extensions, are addressed with

renements for universality. Prospects include O5 GW tests, AI QG simulations, and full

DESI conrmation, oering a framework for redening time via light with QG/string

integrations. The analysis identies critical gaps and enhancements necessary for

comprehensive coverage, integrating 2025 advancements in dark energy from DESI,

gravitational wave observations from LIGO/Virgo/KAGRA, and primordial black hole (PBH)

models. Key recommendations include expanded sections on DESI’s evolving w(z) at 2.8–

4.2σ, LIGO O4c/O5 timelines, PBH seeding for JWST quasars, and rened experimental

protocols with AI-driven simulations. Strengths are amplied through holographic

integrations; limitations, such as non-EM universality, are addressed via generalized

quanta extensions. Prospects encompass O5 GW tests, full DESI DR2 conrmation, and

quantum cosmology probes, positioning TLT as a unied framework for luminous time in QG/string contexts.
DOI: 10.5281/zenodo.17067435
title: TLT

description: The Theory of Luminous Temporality (TLT) version V51.02 integrates foundational

concepts of time quantization through photon events with 2025 advancements in photonic

time crystals, chronon models, string theory’s emergent time, and quantum gravity

extensions. TLT posits time as quantized via luminous processes in EM-dominant systems,

now enhanced with de Sitter/Conformal Field Theory (dS/CFT) duality for dark energy

modeling, consistent with DESI 2025 observations of evolving w(z). Core postulates,

covariant formulations, and derivations are detailed, incorporating ux-induced

entanglement and Planck uctuations. Applications include precision chronometry,

biological metrics, cosmic analysis, and QG probes. SYK model chaos, LQG discreteness, and

asymptotic safety are explored for temporal dynamics. Strengths unify scales; limitations,

including environmental dependence and non-EM extensions, are addressed with

renements for universality. Prospects include O5 GW tests, AI QG simulations, and full

DESI conrmation, oering a framework for redening time via light with QG/string

integrations. The analysis identies critical gaps and enhancements necessary for

comprehensive coverage, integrating 2025 advancements in dark energy from DESI,

gravitational wave observations from LIGO/Virgo/KAGRA, and primordial black hole (PBH)

models. Key recommendations include expanded sections on DESI’s evolving w(z) at 2.8–

4.2σ, LIGO O4c/O5 timelines, PBH seeding for JWST quasars, and rened experimental

protocols with AI-driven simulations. Strengths are amplied through holographic

integrations; limitations, such as non-EM universality, are addressed via generalized

quanta extensions. Prospects encompass O5 GW tests, full DESI DR2 conrmation, and

quantum cosmology probes, positioning TLT as a unied framework for luminous time in QG/string contexts.
DOI: 10.15468/dl.f78wgz
title: Occurrence Download

description: A dataset containing 3815 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Udaspes folus"
 ]
}

 The dataset includes 3815 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003318-251120083545085/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.30685541
title: Dataset for: Throughfall promotes surface soil organic carbon accumulation but impairs deep-soil stabilization in a subtropical evergreen broadleaf forest

description: Rainfall redistribution by forest canopies alters both the pathways of water and solute inputs, yet its role in soil carbon (C) stabilization remains poorly understood. We conducted a field soil column experiment in a subtropical evergreen broadleaf forest to compare the effects of throughfall and bulk precipitation on soil organic carbon (SOC) and its functional fractions―particulate organic carbon (POC) and mineral-associated organic carbon (MAOC). Compared with bulk precipitation, throughfall enhanced surface SOC across seasons, associated with a clear shift toward strengthened nutrient–SOC coupling and fueled by high-quality dissolved organic matter (DOM). POC dynamics were also transformed: under bulk precipitation, they were balanced by rainfall inputs and microbial processing, whereas under throughfall, they became dominated by the internal processing of high-quality leachates. In deeper soils, this efficient surface processing likely induced substrate starvation, which, potentially compounded by altered infiltration, led to a significant net loss of stable, deep-soil MAOC, especially during the dry season. Despite these distinct stabilization pathways between treatments, the POC: MAOC ratio, as well as the proportional contributions of POC and MAOC to SOC, remained stable. However, this apparent stability appears to be a statistically insensitive metric that masks these profound, depth-specific C fraction responses. Together, these findings reveal a rainfall-pathway trade-off: throughfall promotes efficient, nutrient-coupled surface C accumulation but at the cost of destabilizing the deep-soil C pool, leading to a net loss of MAOC. This highlights the need to integrate pathway-specific and depth-specific processes when projecting the long-term stability of forest soil C under changing precipitation regimes.
DOI: 10.6084/m9.figshare.30685541.v1
title: Dataset for: Throughfall promotes surface soil organic carbon accumulation but impairs deep-soil stabilization in a subtropical evergreen broadleaf forest

description: Rainfall redistribution by forest canopies alters both the pathways of water and solute inputs, yet its role in soil carbon (C) stabilization remains poorly understood. We conducted a field soil column experiment in a subtropical evergreen broadleaf forest to compare the effects of throughfall and bulk precipitation on soil organic carbon (SOC) and its functional fractions―particulate organic carbon (POC) and mineral-associated organic carbon (MAOC). Compared with bulk precipitation, throughfall enhanced surface SOC across seasons, associated with a clear shift toward strengthened nutrient–SOC coupling and fueled by high-quality dissolved organic matter (DOM). POC dynamics were also transformed: under bulk precipitation, they were balanced by rainfall inputs and microbial processing, whereas under throughfall, they became dominated by the internal processing of high-quality leachates. In deeper soils, this efficient surface processing likely induced substrate starvation, which, potentially compounded by altered infiltration, led to a significant net loss of stable, deep-soil MAOC, especially during the dry season. Despite these distinct stabilization pathways between treatments, the POC: MAOC ratio, as well as the proportional contributions of POC and MAOC to SOC, remained stable. However, this apparent stability appears to be a statistically insensitive metric that masks these profound, depth-specific C fraction responses. Together, these findings reveal a rainfall-pathway trade-off: throughfall promotes efficient, nutrient-coupled surface C accumulation but at the cost of destabilizing the deep-soil C pool, leading to a net loss of MAOC. This highlights the need to integrate pathway-specific and depth-specific processes when projecting the long-term stability of forest soil C under changing precipitation regimes.
DOI: 10.5281/zenodo.17559983
title: TMT-RP

description: Temporal Memory Theory -- Retrieval and Prediction (TMT-RP) redenes time as a

dynamic eld with intrinsic memory, predictive capacity, and dimensional structure. It

introduces four original laws: the Law of Temporal Retrieval, the Law of Predictive

Memory, the Law of Chrono-Mass-Energy Imprint (CMEI), and the Law of Dimensional

Memory-Energy-Mass Coupling (DMEMC). These laws formalize how time stores,

accesses, and anticipates event structures, and how memory density across temporal

dimensions induces energy curvature and emergent mass. TMT-RP integrates fractional

calculus, multi-time manifolds, conformal dynamics, and quantum eld quantization. It

interfaces with tensor networks, causal sets, and loop quantum gravity, and yields

falsiable predictions for bounce cosmology, entropy moderation, dark energy dynamics,

quantum gravitational noise, and observable mass-energy phenomena. Simulation plots,

empirical ts, and conceptual diagrams support the theory’s structure and predictions.

\tableofcontents

\part{Temporal Memory Theory -- Retrieval and Prediction (TMT-RP)}

1 / 48\chapter{Introduction}

Time is traditionally treated as a passive parameter in physics. Temporal Memory Theory

– Retrieval and Prediction (TMT-RP) challenges this view by proposing that time is a

primary physical eld with internal structure, capable of storing imprints of past events

and projecting future congurations. This framework introduces a memory-based

causality and dimensional modulation, oering a new paradigm for quantum gravity and

cosmology. In contrast to classical General Relativity (GR) where time is external to

dynamics, TMT-RP endows time itself with dynamics and memory.

The theory is built on four pillars:

•

Temporal Memory: Time retains imprints of all events it traverses or that inuence

its geometry.

•

Conformal Dynamics: Time evolves through a scale-invariant conformal structure,

captured via fractional calculus.

•

Predictive Causality: Time encodes causal relationships that extend beyond local

interactions, eectively

``

anticipating'' future states.

•

Dimensional Modulation: Time propagates across layered dimensions (multiple

temporal coordinates), shaping mass-energy emergence in each layer.

By reframing time as an active participant, TMT-RP opens new pathways in understanding

quantum gravity, cosmology, and the arrow of time.

\chapter{Foundational Laws of TMT-RP}

This chapter introduces the four foundational laws of TMT-RP, each capturing a

fundamental aspect of the temporal eld’s behavior.
DOI: 10.5281/zenodo.17359144
title: TMT-RP

description: Temporal Memory Theory -- Retrieval and Prediction (TMT-RP) redenes time as a

dynamic eld with intrinsic memory, predictive capacity, and dimensional structure. It

introduces four original laws: the Law of Temporal Retrieval, the Law of Predictive

Memory, the Law of Chrono-Mass-Energy Imprint (CMEI), and the Law of Dimensional

Memory-Energy-Mass Coupling (DMEMC). These laws formalize how time stores,

accesses, and anticipates event structures, and how memory density across temporal

dimensions induces energy curvature and emergent mass. TMT-RP integrates fractional

calculus, multi-time manifolds, conformal dynamics, and quantum eld quantization. It

interfaces with tensor networks, causal sets, and loop quantum gravity, and yields

falsiable predictions for bounce cosmology, entropy moderation, dark energy dynamics,

quantum gravitational noise, and observable mass-energy phenomena. Simulation plots,

empirical ts, and conceptual diagrams support the theory’s structure and predictions.

\tableofcontents

\part{Temporal Memory Theory -- Retrieval and Prediction (TMT-RP)}

1 / 48\chapter{Introduction}

Time is traditionally treated as a passive parameter in physics. Temporal Memory Theory

– Retrieval and Prediction (TMT-RP) challenges this view by proposing that time is a

primary physical eld with internal structure, capable of storing imprints of past events

and projecting future congurations. This framework introduces a memory-based

causality and dimensional modulation, oering a new paradigm for quantum gravity and

cosmology. In contrast to classical General Relativity (GR) where time is external to

dynamics, TMT-RP endows time itself with dynamics and memory.

The theory is built on four pillars:

•

Temporal Memory: Time retains imprints of all events it traverses or that inuence

its geometry.

•

Conformal Dynamics: Time evolves through a scale-invariant conformal structure,

captured via fractional calculus.

•

Predictive Causality: Time encodes causal relationships that extend beyond local

interactions, eectively

``

anticipating'' future states.

•

Dimensional Modulation: Time propagates across layered dimensions (multiple

temporal coordinates), shaping mass-energy emergence in each layer.

By reframing time as an active participant, TMT-RP opens new pathways in understanding

quantum gravity, cosmology, and the arrow of time.

\chapter{Foundational Laws of TMT-RP}

This chapter introduces the four foundational laws of TMT-RP, each capturing a

fundamental aspect of the temporal eld’s behavior.
DOI: 10.13140/rg.2.2.15971.98088
title: The World Forum for Energy Transformation
DOI: 10.15468/dl.m59jcu
title: Occurrence Download

description: A dataset containing 263005 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chelonia mydas"
 ]
}

 The dataset includes 263005 records from 524 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003306-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.96yy7r
title: Occurrence Download

description: A dataset containing 2187 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "DatasetKey is one of (Spider (Arachnida, Araneae) fauna of the lowland part of the Balkhash-Alakol basin (SE Kazakhstan). Part 1: Literature data, Spider (Arachnida, Araneae) fauna of the lowland part of the Balkhash-Alakol basin (SE Kazakhstan). Part 2: IZRK collection data, Collection Arachnology SMF, A revision of the spider genus Zaitunia (Araneae, Filistatidae), International Barcode of Life project (iBOL), Karakumosa gen. nov., a new Central Asian genus of fossorial wolf spiders (Araneae: Lycosidae: Lycosinae), Arachnida and Myriapoda (Luomus))",
 "TaxonKey is Araneae"
 ]
}

 The dataset includes 2187 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003319-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mbd8f3
title: Occurrence Download

description: A dataset containing 3764 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Borbo cinnara"
 ]
}

 The dataset includes 3764 records from 71 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003320-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57830/2314556
title: Field data for the Vegetation Mapping Inventory Project of Gila Cliff Dwellings National Monument - Open Format Data Package

description: These data were converted from the originally delivered Microsoft Access PLOTs database from the Vegetation Mapping Inventory Project of Gila Cliff Dwellings National Monument.  These comma-delimited data tables contain(s) vegetation mapping plot classification and accuracy assessment data, as well as summary information about the data itself. If a table is empty, then it was empty in the original database.
DOI: 10.57830/2314557
title: Field data for the Vegetation Mapping Inventory Project of Glacier National Park - Open Format Data Package

description: These data were converted from the originally delivered Microsoft Access PLOTs database from the Vegetation Mapping Inventory Project of Glacier National Park.  These comma-delimited data tables contain(s) vegetation mapping plot classification and accuracy assessment data, as well as summary information about the data itself. If a table is empty, then it was empty in the original database.
DOI: 10.15468/dl.c5b85u
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Deutzia gracilis",
 "Country is Armenia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049166-250811113504898/datasets/export for details.
DOI: 10.15468/dl.kdcsa4
title: Occurrence Download

description: A dataset containing 139723 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Phytomyza krygeri Hering, 1949, Ophiomyia aquilegiana Lundqvist, 1947, Anthocharis scolymus Butler, 1866, Euchloe olympia (Edwards, 1871), Lipaphis erysimi (Kaltenbach, 1843), Phytophthora brassicae De Cock & Man, Plectosphaerella cucumerina (Lindf.) W.Gams, Colletotrichum higginsianum Sacc., Alternaria brassicicola (Schwein.) Wiltshire, Hyaloperonospora arabidopsidis (Gäum.) Göker, Riethm., Voglmayr, Weiss & Oberw., Rhodococcus fascians (Tilford, 1936) Goodfellow, 1984, Hyaloperonospora parasitica (Pers.) Constant., Heterodera schachtii A.Schmidt, 1871, Xanthomonas campestris subsp. campestris, Xanthomonas campestris_B, Azorhizobium caulinodans Dreyfus et al., 1988, Bursaphelenchus xylophilus Steiner & Buhrer, 1934, Albugo laibachii Thines & Y.J.Choi, Ceutorhynchus atomus Boheman, 1845, Albugo candida (Pers. ex J.F.Gmel.) Roussel, Hauptidia distinguenda (Kirschbaum, 1868), Podosphaera drabae (Juel) U.Braun & S.Takam., Eurydema dominulus (Scopoli, 1763), Anourosorex squamipes Milne-Edwards, 1872, Apolygus lucorum (Meyer-Dur, 1843), Ara rubrogenys Lafresnaye, 1847, Nyctereutes procyonoides (Gray, 1834), Diabrotica speciosa Germar, 1824, Helicoverpa armigera (Hübner, 1808), Spodoptera littoralis (Boisduval, 1833), Elasmolomus pallens (Dallas, 1852), Helcystogramma melantherella (Busck, 1900), Tetranychus desertorum Banks, 1900, Tetranychus tumidellus Pritchard & Baker, 1955, Tetranychus gigas Pritchard & Baker, 1955, Latoia vivida (Walker, 1865), Spilarctia casigneta (Kollar, 1884), Spilosoma unipuncta Hampson, 1905, Pseudoradiarctia lentifasciata (Hampson, 1916), Athetis sincera Swinhoe, 1889, Etiella grisea Hampson, 1903, Opogona regressa Meyrick, 1916, Haplotinea ditella (Pierce et al., 1938), Astronauta stellans (Meyrick, 1922), Leguminivora ptychora (Meyrick, 1907), Morpho helenor Cramer, 1782, Aproaerema modicella, Spodoptera latifascia Walker, 1856, Anarsia ephippias Meyrick, 1908, Sophonia suturalis (Melichar, 1903))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 139723 records from 1005 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013615-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/7vm7xskfg4.2
title: PotatoCare: Deep learning based potato disease dataset

description: The dataset consists of 10,117 images categorized into 10 classes, representing different potato diseases and healthy samples. The classes include Black Scurf (49 images), Blackleg (47), Blackspot Bruising (770), Brown Rot (105), Common Scab (60), Dry Rot (1,355), Healthy Potatoes (815), Miscellaneous (73), Pink Rot (57), and Soft Rot (560). The dataset was compiled from various sources and merged to create a diverse and representative collection of images. However, the distribution of images across classes is imbalanced, with some diseases like Dry Rot and Blackspot Bruising having significantly more samples than others like Blackleg and Pink Rot. This dataset is useful for training deep learning models for automated disease detection in potatoes, enabling early identification and reducing the risk of crop damage. The diverse nature of the dataset enhances model generalizability, making it suitable for real-world agricultural applications.
DOI: 10.15468/dl.qr3atg
title: Occurrence Download

description: A dataset containing 19991672 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Malpighiales"
 ]
}

 The dataset includes 19991672 records from 8928 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012980-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xp668f
title: Occurrence Download

description: A dataset containing 294203 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-4.48031 36.6578,-4.43627 36.6578,-4.43627 36.68815,-4.48031 36.68815,-4.48031 36.6578))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 294203 records from 68 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015626-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.