cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/85w27rg8m9
title: Integrated Co-expression Analysis of Host–Parasite Transcriptomes Reveals Mechanisms of Host Modulation in an Ant–Cestode System

description: How parasites interact with their hosts at the molecular level is a central question in parasitology, yet identifying host pathways directly targeted by parasites is challenging because infections often have broad effects on host physiology. This difficulty is particularly pronounced in non-model systems, such as the interaction between the parasitic tapeworm Anomotaenia brevis and its intermediate host, the ant Temnothorax nylanderi, in which infection induces strong phenotypic changes. Here, we integrated host and parasite transcriptomes through a combined weighted gene co-expression network analysis (WGCNA) to identify candidate genes and gene networks involved in this interaction. We detected strong negative correlations between parasite and host gene expression, whereas within-species associations were largely positive. Candidate parasite genes were associated with host molecular pathways relevant to infection and host phenotype. The gene networks and expression correlations identified were consistent with those described in model parasite–host systems, supporting the robustness of our approach. Besides, our analysis provided initial functional insights into previously unannotated parasite proteins that may act as effectors of host manipulation. Expression of these parasite genes was correlated with host genes involved in oxidative stress resistance, metabolism, muscle function, immunity, and cuticular sclerotization. These associations suggest that the parasite may modulate multiple host pathways to facilitate infection and transmission. Overall, our findings advance our understanding of molecular mechanisms underlying parasite interference and highlight the value of integrating host and parasite transcriptomic data. More generally, our combined WGCNA framework provides a useful tool for uncovering transcriptional interactions in complex host–parasite systems.

Files and variables

File: link_wgcna.R
Description: R-script for the analysis of the transcriptome data

File: merged_ant_cestode_gcm.csv
Description: Gene count matrix of parasite and host transcriptomes combined, each column is a sample name, each row is a gene, if the gene name starts with a "g", it is a parasite gene, when it starts with an "M" it is a host gene.

Code/software
Software needed is R, R packages used are: WGCNA, ggplot2, dplyr, topGO, clusterProfiler, pathview, data.table, stringr, RColorBrewer, tidyverse, forcats

Access information
Data was derived from the following source:
10.5061/dryad.8cz8w9h3b
Additional data can also be found on this link
DOI: 10.17632/85w27rg8m9.1
title: Integrated Co-expression Analysis of Host–Parasite Transcriptomes Reveals Mechanisms of Host Modulation in an Ant–Cestode System

description: How parasites interact with their hosts at the molecular level is a central question in parasitology, yet identifying host pathways directly targeted by parasites is challenging because infections often have broad effects on host physiology. This difficulty is particularly pronounced in non-model systems, such as the interaction between the parasitic tapeworm Anomotaenia brevis and its intermediate host, the ant Temnothorax nylanderi, in which infection induces strong phenotypic changes. Here, we integrated host and parasite transcriptomes through a combined weighted gene co-expression network analysis (WGCNA) to identify candidate genes and gene networks involved in this interaction. We detected strong negative correlations between parasite and host gene expression, whereas within-species associations were largely positive. Candidate parasite genes were associated with host molecular pathways relevant to infection and host phenotype. The gene networks and expression correlations identified were consistent with those described in model parasite–host systems, supporting the robustness of our approach. Besides, our analysis provided initial functional insights into previously unannotated parasite proteins that may act as effectors of host manipulation. Expression of these parasite genes was correlated with host genes involved in oxidative stress resistance, metabolism, muscle function, immunity, and cuticular sclerotization. These associations suggest that the parasite may modulate multiple host pathways to facilitate infection and transmission. Overall, our findings advance our understanding of molecular mechanisms underlying parasite interference and highlight the value of integrating host and parasite transcriptomic data. More generally, our combined WGCNA framework provides a useful tool for uncovering transcriptional interactions in complex host–parasite systems.

Files and variables

File: link_wgcna.R
Description: R-script for the analysis of the transcriptome data

File: merged_ant_cestode_gcm.csv
Description: Gene count matrix of parasite and host transcriptomes combined, each column is a sample name, each row is a gene, if the gene name starts with a "g", it is a parasite gene, when it starts with an "M" it is a host gene.

Code/software
Software needed is R, R packages used are: WGCNA, ggplot2, dplyr, topGO, clusterProfiler, pathview, data.table, stringr, RColorBrewer, tidyverse, forcats

Access information
Data was derived from the following source:
10.5061/dryad.8cz8w9h3b
Additional data can also be found on this link
DOI: 10.17632/rd8kt4brt5
title: Trancriptional profile of iGNs and iMNs after TDP-43 knockdown

description: Research Hypothesis
The study hypothesized that while TDP-43 loss-of-function is a near-universal hallmark of ALS, its molecular consequences—specifically regarding gene expression and cryptic splicing—are cell-type specific (glutamatergic induced neurons - iGNs versus cholinergic induced neurons - iMNs) . We further hypothesized that the presence of astrocytes (iAs) would modulate the detectable splicing repertoire of human induced neurons (iNs), providing a more clinically relevant representation of the disease environment.

What the Data Shows
Subtype-Specific Signatures: The data reveals distinct transcriptomic profiles for iGNs and iMNs. While TDP-43 depletion affected 92 genes in iMNs and 52 in iGNs, only 33 genes overlapped between the two subtypes.
Conserved Pathological Core: Despite low overall overlap, a conserved set of ALS-relevant targets—including STMN2, UNC13A, CAMK2B, and ATG4B—were consistently downregulated and mis-spliced across both populations.
Glial Influence: The inclusion of astrocytes (iNs+iAs) facilitated the detection of additional cryptic events, most notably in HDGFL2, which matched patient-derived cerebrospinal fluid data .

Data organization:
Gene counts - "Counts_processed_pure_iGNs_iMNs.csv" (pure iNs) and "Counts_processed_iGNs_iAs.csv" (co-culture iNs+iAs)
Splicing variants frequency - "Splicing_iMN_iGN_sgvc_combined.rds" (pure iNs) and "Splicing_ioGN_ioAs_sgvc_combined.rds" (co-culture iNs+iAs)
DOI: 10.17632/rd8kt4brt5.2
title: Trancriptional profile of iGNs and iMNs after TDP-43 knockdown

description: Research Hypothesis
The study hypothesized that while TDP-43 loss-of-function is a near-universal hallmark of ALS, its molecular consequences—specifically regarding gene expression and cryptic splicing—are cell-type specific (glutamatergic induced neurons - iGNs versus cholinergic induced neurons - iMNs) . We further hypothesized that the presence of astrocytes (iAs) would modulate the detectable splicing repertoire of human induced neurons (iNs), providing a more clinically relevant representation of the disease environment.

What the Data Shows
Subtype-Specific Signatures: The data reveals distinct transcriptomic profiles for iGNs and iMNs. While TDP-43 depletion affected 92 genes in iMNs and 52 in iGNs, only 33 genes overlapped between the two subtypes.
Conserved Pathological Core: Despite low overall overlap, a conserved set of ALS-relevant targets—including STMN2, UNC13A, CAMK2B, and ATG4B—were consistently downregulated and mis-spliced across both populations.
Glial Influence: The inclusion of astrocytes (iNs+iAs) facilitated the detection of additional cryptic events, most notably in HDGFL2, which matched patient-derived cerebrospinal fluid data .

Data organization:
Gene counts - "Counts_processed_pure_iGNs_iMNs.csv" (pure iNs) and "Counts_processed_iGNs_iAs.csv" (co-culture iNs+iAs)
Splicing variants frequency - "Splicing_iMN_iGN_sgvc_combined.rds" (pure iNs) and "Splicing_ioGN_ioAs_sgvc_combined.rds" (co-culture iNs+iAs)
DOI: 10.17632/4drxt6z4vh
title: The diet-derived gut microbiota metabolite indole-3-propionic acid reverses insulin resistance in adipocytes

description: Raw data in excel files:
MTT_Dec_22
2_NBDG_uptake_Nov_23
Oil Red O_ Aug_23
PCRs _may_24
LD number & area_Sep_23
Glycerol_jul_24
DOI: 10.17632/4drxt6z4vh.1
title: The diet-derived gut microbiota metabolite indole-3-propionic acid reverses insulin resistance in adipocytes

description: Raw data in excel files:
MTT_Dec_22
2_NBDG_uptake_Nov_23
Oil Red O_ Aug_23
PCRs _may_24
LD number & area_Sep_23
Glycerol_jul_24
DOI: 10.17632/ng9pb44nnd.2
title: How Does Marijuana Legislation Affect Crime? Medical and Recreational Laws Across 50 States

description: Novel policies often have unintended consequences—programs that legalize one crime
may affect other crimes. Marijuana legalization is an example of such spillovers, as
states have legalized use in a staggered fashion. While previous research focuses on a
few states or one type of legalization, we consider all 50 states and both medical and
recreational marijuana. Using a difference in differences approach we find distinct im-
pacts from medical and recreational legalization, largely borne out by dynamic analysis.
In our main synthetic difference in differences model, we find that medical legalization
reduces property crime, while recreational legalization reduces violent crime. Our re-
sults are consistent with a channel where legalization makes other crimes costly. Thus,
policymakers should wait to understand cost-benefit impacts, focus on the specific case,
and learn from outcomes in similar states.

We had to compile our data set from a variety of sources. These data were sometimes reported
at different frequencies, and made available at different periods. We therefore ultimately
use data for a sample period where we could collect all of the variables and controls. The
main data on crime rates come from FBI Uniformed Crime Reports (UCR) arrest data from
1995 to 2019, while the data on recreational and medical use laws come from various state
level sources. Our analysis involves both violent crime and property crime. Violent crime is
a summary of the following four individual crimes: aggravated assault, murder, rape, and
robbery. Similarly, property crime combines the following crimes: larceny, burglary, and
motor theft. In addition to these data, we utilize a host of state-level controls including:
a dummy variable for political party of the governor; a dummy variable for political party
in control of the state senate; poverty rate; unemployment rate; population density; aver-
age number of neighboring states that have legalized recreational and medical marijuana;
cigarette tax rate; liquor-related arrests; number of police per capita; and real income per
capita. We select the data for the controls based on discussions in previous research such
as Pacula and Kilmer (2003) and Dragone et al. (2019). These data are from the BLS, BEA
Census Bureau, and other sources. We also include
state and year fixed effects and use robust clustered standard errors. More information below in the readme file.
DOI: 10.17632/bdg5yj4jzf
title: Pediatric Melanoma in Arizona: Demographic, Tumor, and Treatment Characteristics from the Arizona Cancer Registry

description: Supplemental data for "Pediatric Melanoma in Arizona: Demographic, Tumor, and Treatment Characteristics from the Arizona Cancer Registry"
DOI: 10.17632/bdg5yj4jzf.1
title: Pediatric Melanoma in Arizona: Demographic, Tumor, and Treatment Characteristics from the Arizona Cancer Registry

description: Supplemental data for "Pediatric Melanoma in Arizona: Demographic, Tumor, and Treatment Characteristics from the Arizona Cancer Registry"
DOI: 10.17632/thr22vjccm
title: Supplemental Material: Epidermotropism of T Lymphocytes Provides Insight into the Pathogenesis of Morphea

description: This dataset contains supplementary materials associated with the article “Epidermotropism of T Lymphocytes Provides Insight into the Pathogenesis of Morphea” published in JAAD International. The supplementary files include extended methods, additional statistical analyses, supplementary tables, and supplementary figures that support the findings presented in the main manuscript. These materials provide further detail on histopathologic assessment, immunohistochemistry, digital pathology quantification, T-cell receptor clonality analysis, and exploratory statistical modeling, and were not included in the main article due to space limitations.
DOI: 10.17632/thr22vjccm.1
title: Supplemental Material: Epidermotropism of T Lymphocytes Provides Insight into the Pathogenesis of Morphea

description: This dataset contains supplementary materials associated with the article “Epidermotropism of T Lymphocytes Provides Insight into the Pathogenesis of Morphea” published in JAAD International. The supplementary files include extended methods, additional statistical analyses, supplementary tables, and supplementary figures that support the findings presented in the main manuscript. These materials provide further detail on histopathologic assessment, immunohistochemistry, digital pathology quantification, T-cell receptor clonality analysis, and exploratory statistical modeling, and were not included in the main article due to space limitations.
DOI: 10.15468/dl.ke4b2h
title: Occurrence Download

description: A dataset containing 42792512 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Brachyelytrum, Nardus, Lygeum, Anisopogon, Danthoniastrum, Duthiea, Metcalfia, Pappagrostis, Pseudodanthonia, Sinochasea, Stephanachne, Phaenosperma, Brylkinia, Glyceria, Koordersiochloa, Lycochloa, Melica, Pleuropogon, Schizachne, Triniochloa, Ampelodesmos, Achnatherum, Aciachne, Amelichloa, Anemanthele, Austrostipa, Barkworthia, Celtica, Eriocoma, Hesperostipa, Jarava, Lorenzochloa, Macrochloa, Nassella, Neotrinia, Oloptum, Ortachne, Orthoraphium, Oryzopsis, Pappostipa, Patis, Piptochaetium, Piptatheropsis, Piptatherum, Psammochloa, Pseudoeriocoma, Ptilagrostiella, Ptilagrostis, Stipa, Stipellula, Thorneochloa, Timouria, Trikeraia, Diarrhena, Neomolinia, Brachypodium, Amphibromus, Torreyochloa, Phalaris, Acrospelion, Aegialina, Arrhenatherum, Avellinia, Avena, Cinnagrostis, Gaudinia, Graphephorum, Helictotrichon, Koeleria, Lagurus, Limnodea, Peyritschia, Rostraria, Sibirotrisetum, Sphenopholis, Tricholemma, Trisetaria, Trisetum, Trisetopsis, Tzveleviochloa, Anthoxanthum, Airopsis, Briza, Echinopogon, Greeneochloa, Pentapogon, Relchela, Hypseochloa, Boldrinia, Calotheca, Chascolytrum, Condilorachia, Erianthecium, Lombardochloa, Microbriza, Poidium, Rhombolytrum, Poaceae, Laegaardia, Paramochloa, Agrostis, Agrostula, Alpagrostis, Calamagrostis, Gastridium, Lachnagrostis, Podagrostis, Polypogon, Triplachne, Dryopoa, Scolochloa, Mibora, Echinaria, Oreochloa, Psilathera, Sesleria, Sesleriella, Aira, Avenella, Corynephorus, Periballia, Antinoria, Helictochloa, Molineriella, Holcus, Vahlodea, Deschampsia, Castellia, Drymochloa, Festuca, Leucopoa, Poaceae, Lolium, Patzkea, Pseudobromus, Xanthochloa, Dactylis, Lamarckia, Cynosurus, Ammochloa, Agropyropsis, Catapodium, Cutandia, Desmazeria, Hainardia, Parapholis, Sphenopus, Vulpiella, Catabrosa, Catabrosella, Coleanthus, Colpodium, Hyalopoa, Hyalopodium, Paracolpodium, Phippsia, Puccinellia, Sclerochloa, Arctopoa, Agrostopoa, Poa, Avenula, Milium, Phleum, Beckmannia, Pholiurus, Pseudophleum, Rhizocephalus, Aniselytron, Cinna, Cinnastrum, Cyathopus, Simplicia, Arctagrostis, Hookerochloa, Nicoraepoa, Saxipoa, Sylvipoa, Brizochloa, Arctohyalopoa, Arctophila, Dupontia, Dupontiopsis, Alopecurus, Limnas, Apera, Bellardiochloa, Gaudinopsis, Nephelochloa, Parvotrisetum, Ventenata, Littledalea, Bromus, Agropyron, Anthosachne, Australopyrum, Connorochloa, Crithopsis, Douglasdeweya, Elymus, Eremopyrum, Festucopsis, Henrardia, Heteranthelium, Hordelymus, Hordeum, Kengyilia, Leymus, Pascopyrum, Peridictyon, Psathyrostachys, Pseudoroegneria, Secale, Stenostachys, Taeniatherum, Aegilops, Amblyopyrum, Dasypyrum, Thinopyrum, Triticum)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 42792512 records from 8610 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007667-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15267749
title: A precipitation route for the synthesis of g-C3N4/TiO2 composites with enhanced photo activity
DOI: 10.5281/zenodo.15267750
title: Novel preparation of photoactive g-C3N4/TiO2 composites by precipitation of TiO2 onto the surface of g-C3N4
DOI: 10.5281/zenodo.15472482
title: A precipitation route for the synthesis of g-C3N4/TiO2 composites with enhanced photo activity
DOI: 10.57969/s9v880
title: Lagrangian Drifter Laboratory at Scripps Institution of Oceanography Directional Wave Spectra Drifter (DWSD) EKAMSAT-ASTRAL Sensor Data

description: The Lagrangian Drifter Laboratory’s Directional Wave Spectra Drifter (DWSD) observations were funded by the Office of Naval Research (ONR) for the EKAMSAT/ASTRAL DRI project The EKAMSAT/ASTRAL project sought to understand the interactions of the upper ocean, air-sea interface, and marine boundary layer during the monsoon onset. The DWSDs and Directional Wave Spectra Barometer Wind Drifters (DWSBWDs) provided wave and wind observations in the Bay of Bengal during monsoon onset. In each 2024 and 2025, drifters were deployed during research cruises. This is the First-Five spectral data from the drifters.
DOI: 10.5061/dryad.w3r2280p3
title: Australian long-finned pilot whales (Globicephala melas) emit stereotypical, variable, biphonic, multi-component, and sequenced vocalisations, similar to those recorded in the northern hemisphere

description: While in the northern hemisphere, many studies have been conducted on the
 vocal repertoire of long-finned pilot whales (Globicephala melas), no such
 study has been conducted in the southern hemisphere. Presented here, is
 the first study on the vocalisations of long-finned pilot whales along the
 southern coast of mainland Australia. Multiple measures were taken of 2
 028 vocalisations recorded over five years in several locations. These
 vocalisations included tonal sounds with and without overtones, sounds of
 burst-pulse character, graded sounds, biphonations, and calls of multiple
 components. Vocalisations were further categorised based on spectrographic
 features into 18 contour classes. Altogether, vocalisations ranged from
 approximately 200 Hz to 25 kHz in fundamental frequency and from 0.03 s to
 2.07 s in duration. These measures compared well with those from northern
 hemisphere pilot whales. Some call types were almost identical to northern
 hemisphere vocalisations, even though the geographic ranges of the two
 populations are far apart. Other call types were unique to Australia.
 Striking similarities with calls of short-finned pilot whales
 (Globicephala macrorhynchus) and sometimes sympatric killer whales
 (Orcinus orca) were also found. Theories for call convergence and
 divergence are discussed.
DOI: 10.5061/dryad.9kd51c5f8
title: Deciphering linkages between microbial communities and priming effects in lake sediments with different salinity

description: Priming effects (PEs) and their associated microbial drivers are not well
 studied in lake sediments. Here, we investigated PEs and underlying
 potential microbial drivers in the sediments of lakes on the
 Qinghai-Tibetan Plateau (QTP). Sediments were collected from three QTP
 lakes with different salinity, followed by microcosm construction and
 subsequent incubation at in-situ temperature. The sediment microcosms were
 amended with 13C-labeled glucose, on which PE intensities were evaluated
 in the incubations on the 7th and 42nd day. Positive PEs were observed in
 all the studied lake sediment microcosms. PE intensities exhibited
 significantly (P &lt; 0.05) linear correlations with most of the
 measured physicochemical factors (e.g., salinity, sediment total
 nitrogen/phosphorus and ratios of carbon: nitrogen), and such linear
 correlations were inverse for the early (i.e., on the 7th day) and late
 (i.e., on the 42nd day) PEs. Prokaryotic and fungal community compositions
 significantly changed owing to glucose addition in the studied lake
 microcosms, suggesting that both prokaryotes and fungi may contribute to
 the observed PEs. Network analysis showed that the numbers of positive
 correlations between fungal taxa and other microorganisms increased with
 the enhancement of the late PE intensity, suggesting that fungi and
 associated co-metabolisms may play key roles in late PEs in this study.
 Collectively, this study gives new insights into PE intensity and
 underlying microbial drivers of PE in lake sediments, and such knowledge
 is of great importance to understanding organic matter mineralization in
 lake ecosystems.
DOI: 10.15468/dl.uz6pry
title: Occurrence Download

description: A dataset containing 65617 records in GBIF matching the query:
SELECT
 '440' AS fish_cell,
 GBIF_MGRSCode(1000, decimalLatitude, decimalLongitude, 0) AS cell, -- NO randomization
 COUNT(DISTINCT speciesKey) AS species_richness
 FROM occurrence
 WHERE hasCoordinate = TRUE
 AND speciesKey IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND decimalLatitude &gt;= 41.89415215079384
 AND decimalLatitude &lt;= 46.479780028101516
 AND decimalLongitude &gt;= 17.98222425708759
 AND decimalLongitude &lt;= 25.177592367184833
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND (coordinateUncertaintyInMeters &lt;= 100 OR coordinateUncertaintyInMeters IS NULL)
 GROUP BY fish_cell, cell

 The dataset's 65617 records were derived from 241 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002564-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.