cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.3ahw4b
title: Occurrence Download

description: A dataset containing 579 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Picea neoveitchii, Taxus wallichiana mairei)"
 ]
}

 The dataset includes 579 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014666-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xxy559
title: Occurrence Download

description: A dataset containing 166 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Monomorium floricola",
 "Country is Ecuador",
 "HasCoordinate is true"
 ]
}

 The dataset includes 166 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014664-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y4cvua
title: Occurrence Download

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

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014665-260108223611665/datasets/export for details.
DOI: 10.15468/dl.mbvq2x
title: Occurrence Download

description: A dataset containing 37688080 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Costa Rica",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 37688080 records from 1636 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014622-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dqfkp4
title: Occurrence Download

description: A dataset containing 204619 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Botryotinia polyblastis (P.H.Greg.) N.F.Buchw., Stagonosporopsis curtisii (Berk.) Boerema, Drymonema dalmatinum Haeckel, 1880, Dermochelys coriacea (Vandelli, 1761), Aphloia theiformis (Vahl) Benn., Roussea simplex Sm., Molinaea alternifolia Willd., Tambourissa peltata Baker, Mimusops erythroxylon Bojer ex A.DC., Geniostoma borbonicum Spreng., Toddalia asiatica (L.) Lam., Gaertnera psychotrioides (DC.) Baker, Dracaena reflexa var. angustifolia Baker, Sideroxylon cinereum Lam., Lepidochelys olivacea (Eschscholtz, 1829), Stromatinia narcissi Drayton & J.W.Groves, Chlorocebus pygerythrus hilgerti (Neumann, 1902), Prolatoia perileuce Holland, 1893, Oliarus sanctiphilippi Synave, 1960, Nyodes viridirufa Hampson, 1918, Encoptolophus sordidus (Burmeister, 1838), Dausara talliusalis Walker, 1859, Patagona gigas (Vieillot, 1824), Pnigalio pristiphorae Askew, 1965, Hyporhygma quadripunctatus Malloch, Donacia rufescens Lacordaire, 1845, Donacia parvidens C.Schaeffer, 1919, Donacia megacornis Blatchley, 1910, Donacia militaris Lacordaire, 1845, Ficus trigonata L., Ficus citrifolia Mill., Ficus nymphaeifolia Mill., Ficus insipida Willd., Ficus obtusifolia Kunth, Ficus yoponensis Desv., Ficus maxima Mill., Ficus popenoei Standl., Elophila interruptalis Pryer, Opiconsiva anacharsis Fennah, 1969, Synclita occidentalis Lange, 1956, Euchrysops dolorosa (Trimen, 1887), Euchrysops subpallida Bethune-Baker, 1922, Progne dominicensis (Gmelin, 1789), Euchrysops barkeri (Trimen, 1893), Thyreus caeruleopunctatus (Blanchard, 1840), Delta concinnum (de Saussure, 1855), Hypercompe scribonia Stoll, Homalotylus hyperaspidis Timberlake, 1919, Signiphora coquilletti Ashmead, 1900, Metastenus townsendi (Ashmead, 1904))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 204619 records from 1272 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011696-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nu56vg
title: Occurrence Download

description: A dataset containing 262600 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((38.94019 -7.427288, 39.87556 -7.427288, 39.87556 -4.182004, 38.94019 -4.182004, 38.94019 -7.427288))"
 ]
}

 The dataset includes 262600 records from 548 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011699-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/ykzsxpyvhm
title: Dataset on moisture-based hydrothermal carbonization control, adsorption behaviour and surface characteristics of marine-based hydrochars for pollution removal

description: This dataset accompanies a study on pollutant adsorption by hydrochars produced via hydrothermal carbonization (HTC) of marine biowaste (fish and shrimp) and lignocellulosic biomass (corn), benchmarked against commercial activated carbon. It assembles material properties and adsorption performance under controlled laboratory conditions, enabling reproduction, cross‑comparison, and modeling.

The first component reports moisture content and additional water determination for raw biomasses and derived hydrochars. Gravimetric measurements provide mean values and standard deviations, allowing transparent HTC mass balance and yield calculations and clarifying the role of inherent/retained water on carbonization behavior and subsequent material stability.

The second component contains BET Rouquerol data from nitrogen adsorption–desorption isotherms. Both raw isotherm points and processed outputs are included: specific surface area, cumulative pore volume, average pore diameter, and pore‑size contributions (micro/meso/macro). Results are provided for activated carbon, corn hydrochar, fish hydrochar, and shrimp hydrochar. These data allow verification of BET calculations, assessment of textural differences among feedstocks (protein‑ and chitin‑rich marine residues vs lignocellulosic corn), and evaluation of how HTC processing translates into accessible surface and porosity.

The third component provides adsorption kinetics for seven representative contaminants—tetracycline, doxycycline hyclate, acetaminophen, methylene blue, methyl orange, 4‑nitrophenol, and potassium dichromate—measured on all four adsorbents. Time‑course uptake data are supplied alongside fitted parameters for pseudo‑first‑order, pseudo‑second‑order, and Two‑PFO models, with goodness‑of‑fit metrics. Outcomes are reported both as qe (mg g⁻¹) and as surface‑area‑normalized qe,mol (nmol m⁻²), facilitating comparison across materials with disparate surface areas and enabling mechanistic interpretation beyond mass‑based capacities.

Collectively, the dataset links feedstock composition, moisture behavior, and textural properties to adsorption kinetics across multiple pollutant classes. It is suitable for (i) reproducing and validating kinetic fits; (ii) benchmarking hydrochars against activated carbon; (iii) developing predictive relationships between material descriptors and adsorption efficiency; and (iv) use in meta‑analysis or machine‑learning workflows that require harmonized physicochemical inputs and adsorption outputs.
DOI: 10.17632/ykzsxpyvhm.1
title: Dataset on moisture-based hydrothermal carbonization control, adsorption behaviour and surface characteristics of marine-based hydrochars for pollution removal

description: This dataset accompanies a study on pollutant adsorption by hydrochars produced via hydrothermal carbonization (HTC) of marine biowaste (fish and shrimp) and lignocellulosic biomass (corn), benchmarked against commercial activated carbon. It assembles material properties and adsorption performance under controlled laboratory conditions, enabling reproduction, cross‑comparison, and modeling.

The first component reports moisture content and additional water determination for raw biomasses and derived hydrochars. Gravimetric measurements provide mean values and standard deviations, allowing transparent HTC mass balance and yield calculations and clarifying the role of inherent/retained water on carbonization behavior and subsequent material stability.

The second component contains BET Rouquerol data from nitrogen adsorption–desorption isotherms. Both raw isotherm points and processed outputs are included: specific surface area, cumulative pore volume, average pore diameter, and pore‑size contributions (micro/meso/macro). Results are provided for activated carbon, corn hydrochar, fish hydrochar, and shrimp hydrochar. These data allow verification of BET calculations, assessment of textural differences among feedstocks (protein‑ and chitin‑rich marine residues vs lignocellulosic corn), and evaluation of how HTC processing translates into accessible surface and porosity.

The third component provides adsorption kinetics for seven representative contaminants—tetracycline, doxycycline hyclate, acetaminophen, methylene blue, methyl orange, 4‑nitrophenol, and potassium dichromate—measured on all four adsorbents. Time‑course uptake data are supplied alongside fitted parameters for pseudo‑first‑order, pseudo‑second‑order, and Two‑PFO models, with goodness‑of‑fit metrics. Outcomes are reported both as qe (mg g⁻¹) and as surface‑area‑normalized qe,mol (nmol m⁻²), facilitating comparison across materials with disparate surface areas and enabling mechanistic interpretation beyond mass‑based capacities.

Collectively, the dataset links feedstock composition, moisture behavior, and textural properties to adsorption kinetics across multiple pollutant classes. It is suitable for (i) reproducing and validating kinetic fits; (ii) benchmarking hydrochars against activated carbon; (iii) developing predictive relationships between material descriptors and adsorption efficiency; and (iv) use in meta‑analysis or machine‑learning workflows that require harmonized physicochemical inputs and adsorption outputs.
DOI: 10.17632/dn5tjwpp37
title: Immunosuppression CSCC outcomes Mendeley Supplemental Tables 1-3

description: Supplemental tables 1-3 for the manuscript "Impact of Immunosuppression on Cutaneous Squamous Cell Carcinoma Outcomes" by Klein et al
DOI: 10.17632/dn5tjwpp37.1
title: Immunosuppression CSCC outcomes Mendeley Supplemental Tables 1-3

description: Supplemental tables 1-3 for the manuscript "Impact of Immunosuppression on Cutaneous Squamous Cell Carcinoma Outcomes" by Klein et al
DOI: 10.17632/w7ht3nvyj3
title: DATA: Pasture Intensification Effects on Nitrous Oxide Emissions: Evidence for Lower Emission Factors than IPCC Defaults in the Southern Brazilian Highland

description: This dataset contains measurements of nitrous oxide (N₂O) fluxes collected from pasture systems in the Southern Brazilian Highlands. Data were obtained from four grazing systems (natural grassland, improved natural grassland, perennial pasture, and annual pasture) under urea fertilizer, cattle urine, and dung applications between 2020 and 2021. The dataset includes gas flux measurements, soil properties, meteorological data, and emission factor calculations used in the manuscript 'Pasture Intensification Effects on Nitrous Oxide Emissions: Evidence for Lower Emission Factors than IPCC Defaults'. These data support region-specific emission factors for greenhouse gas inventories.
DOI: 10.25574/45520
title: Chandra X-ray Observatory ObsId 45520
DOI: 10.17632/w7ht3nvyj3.1
title: DATA: Pasture Intensification Effects on Nitrous Oxide Emissions: Evidence for Lower Emission Factors than IPCC Defaults in the Southern Brazilian Highland

description: This dataset contains measurements of nitrous oxide (N₂O) fluxes collected from pasture systems in the Southern Brazilian Highlands. Data were obtained from four grazing systems (natural grassland, improved natural grassland, perennial pasture, and annual pasture) under urea fertilizer, cattle urine, and dung applications between 2020 and 2021. The dataset includes gas flux measurements, soil properties, meteorological data, and emission factor calculations used in the manuscript 'Pasture Intensification Effects on Nitrous Oxide Emissions: Evidence for Lower Emission Factors than IPCC Defaults'. These data support region-specific emission factors for greenhouse gas inventories.
DOI: 10.17632/msdp96z7nn
title: Combined PISA dataset

description: An algorithm model needs a generalised and wide coverage dataset. A generalised dataset able to provide high-accuracy prediction result. 
Combined Student, School and Teacher dataset with adjustment to a balanced dataset, which is ready to train the model.
DOI: 10.25574/45527
title: Chandra X-ray Observatory ObsId 45527
DOI: 10.17632/msdp96z7nn.1
title: Combined PISA dataset

description: An algorithm model needs a generalised and wide coverage dataset. A generalised dataset able to provide high-accuracy prediction result. 
Combined Student, School and Teacher dataset with adjustment to a balanced dataset, which is ready to train the model.
DOI: 10.25574/45534
title: Chandra X-ray Observatory ObsId 45534
DOI: 10.17632/9jz5p3jdg2
title: LSTM and Reinforcement Learning Based Peak Analyzer for Electrocardiogram Characteristic Waves Delineation (performance results + annotated ECG record datasets)

description: Supplementary data containing three databases storing ECG records annotated by three different annotators in addition to the performance results of the proposed method.
The data comprise the following 6 files:
1. PDF file describing the content of the supplementary data, presenting a guide for reading the annotated ECG records, and discussing additional performance comparison of the proposed method with the related works.
2. Excel file presenting the performance of the reinforcement learning model in manipulating the peak analyzer using different episode-ending parameters.
3. Second Excel file describing in detail the selected training and validating datasets from the provided three ECG record databases aligned with the performance results of the proposed method.
4. Three SQLite database files consisting of:
4.a. 73 annotated ECG records selected from the ecgpuwave annotated records of the QTDB.
4.b. 71 annotated ECG records selected from the expert annotated records of the QTDB.
4.c. 160 of our own manually annotated records.
DOI: 10.25574/45540
title: Chandra X-ray Observatory ObsId 45540
DOI: 10.17632/9jz5p3jdg2.2
title: LSTM and Reinforcement Learning Based Peak Analyzer for Electrocardiogram Characteristic Waves Delineation (performance results + annotated ECG record datasets)

description: Supplementary data containing three databases storing ECG records annotated by three different annotators in addition to the performance results of the proposed method.
The data comprise the following 6 files:
1. PDF file describing the content of the supplementary data, presenting a guide for reading the annotated ECG records, and discussing additional performance comparison of the proposed method with the related works.
2. Excel file presenting the performance of the reinforcement learning model in manipulating the peak analyzer using different episode-ending parameters.
3. Second Excel file describing in detail the selected training and validating datasets from the provided three ECG record databases aligned with the performance results of the proposed method.
4. Three SQLite database files consisting of:
4.a. 73 annotated ECG records selected from the ecgpuwave annotated records of the QTDB.
4.b. 71 annotated ECG records selected from the expert annotated records of the QTDB.
4.c. 160 of our own manually annotated records.
DOI: 10.25574/45541
title: Chandra X-ray Observatory ObsId 45541
DOI: 10.17632/mj5g26f36t
title: Growth profiling in screening of rice with saline-alkaline tolerance

description: This dataset is based on the results of saline-alkaline tolerance screening conducted at the experimental field of Hiroshima University, using 35 rice varieties, including those from the global and Japanese rice core collections. The screening was conducted in two independent experiments. In both experiments, rice seedlings were hydroponically grown for four weeks in a half-strength Kimura B solution. They were then subjected to control (pH 5.5) and saline-alkaline (SA) conditions (50 mM Na, pH 8.3 in both screenings, and pH 8.7 only in the first screening) for two weeks. 
In the first screening, 34 varieties were evaluated for growth (dry weight and dry weight ratio) and SPAD value. In the second screening, 12 varieties were selected based on the shoot dry weight ratio from the first screening, including 7 tolerant, 3 sensitive, and one reference variety each for salt tolerance and salt sensitivity. These were evaluated for growth (length, fresh weight, dry weight, dry weight ratio) and chlorophyll concentration. In all experiments, one plant was treated as one replicate, and four replicates were used for each treatment. This study is the first to investigate saline-alkaline tolerance in rice varieties from core collections, and it reveals the effects of saline-alkaline stress across diverse genotypes. The selected tolerant varieties and the dataset will be useful for identifying tolerance-related genes and loci, and for developing new stress-tolerant rice cultivars.
DOI: 10.17632/mj5g26f36t.2
title: Growth profiling in screening of rice with saline-alkaline tolerance

description: This dataset is based on the results of saline-alkaline tolerance screening conducted at the experimental field of Hiroshima University, using 35 rice varieties, including those from the global and Japanese rice core collections. The screening was conducted in two independent experiments. In both experiments, rice seedlings were hydroponically grown for four weeks in a half-strength Kimura B solution. They were then subjected to control (pH 5.5) and saline-alkaline (SA) conditions (50 mM Na, pH 8.3 in both screenings, and pH 8.7 only in the first screening) for two weeks. 
In the first screening, 34 varieties were evaluated for growth (dry weight and dry weight ratio) and SPAD value. In the second screening, 12 varieties were selected based on the shoot dry weight ratio from the first screening, including 7 tolerant, 3 sensitive, and one reference variety each for salt tolerance and salt sensitivity. These were evaluated for growth (length, fresh weight, dry weight, dry weight ratio) and chlorophyll concentration. In all experiments, one plant was treated as one replicate, and four replicates were used for each treatment. This study is the first to investigate saline-alkaline tolerance in rice varieties from core collections, and it reveals the effects of saline-alkaline stress across diverse genotypes. The selected tolerant varieties and the dataset will be useful for identifying tolerance-related genes and loci, and for developing new stress-tolerant rice cultivars.
DOI: 10.25574/45546
title: Chandra X-ray Observatory ObsId 45546
DOI: 10.25574/45555
title: Chandra X-ray Observatory ObsId 45555
DOI: 10.17632/56xp222nbc
title: Root-Surrounding Soil of Stellera chamaejasme Harbors a Conserved Bacterial Community Structure in Different Grasslands of the Hexi Corridor, China

description: Physicochemical Properties of Bacteria and Microbial Abundance
DOI: 10.17632/56xp222nbc.1
title: Root-Surrounding Soil of Stellera chamaejasme Harbors a Conserved Bacterial Community Structure in Different Grasslands of the Hexi Corridor, China

description: Physicochemical Properties of Bacteria and Microbial Abundance
DOI: 10.25574/45560
title: Chandra X-ray Observatory ObsId 45560
DOI: 10.25574/45566
title: Chandra X-ray Observatory ObsId 45566
DOI: 10.17632/ddmkcddn5c
title: TFF2 Interacts with A4GNT to Regulate Inflammation to Protect against Pneumonia during Influenza Virus Infection

description: Trefoil factor 2 (TFF2) has been shown to reduce inflammation and promote mucosal repair in models of gastric and colonic injury. However, the role of TFF2 in acute respiratory tract virus infection remains elusive. Here, we demonstrate that TFF2 protects mice against pneumonia in influenza virus infections. In vitro studies have revealed that TFF2 recognizes the terminal GlcNAc-α-1,4-Gal disaccharide of cell surface proteins mediated by the glycosyltransferase activity of α1,4-N-acetylglucosaminyltransferase (A4GNT). Functionally, TFF2 organized membranous TFF2-A4GNT-glycan protein complex serves to restrain cellular inflammation pathways by augmenting inhibitory Tyr527 phosphorylation at the C-terminus of src-family kinases (SFKs), thereby effectively preventing the phosphorylation of stimulatory SFKs Tyr416. Finally, we have conclusively verified that the protective effect of TFF2 treatment relies on the TFF2-A4GNT-glycan axis during influenza virus infection. In the future, TFF2 may offer a new potential intervention strategy for inflammatory diseases induced by acute respiratory infections.
DOI: 10.17632/ddmkcddn5c.1
title: TFF2 Interacts with A4GNT to Regulate Inflammation to Protect against Pneumonia during Influenza Virus Infection

description: Trefoil factor 2 (TFF2) has been shown to reduce inflammation and promote mucosal repair in models of gastric and colonic injury. However, the role of TFF2 in acute respiratory tract virus infection remains elusive. Here, we demonstrate that TFF2 protects mice against pneumonia in influenza virus infections. In vitro studies have revealed that TFF2 recognizes the terminal GlcNAc-α-1,4-Gal disaccharide of cell surface proteins mediated by the glycosyltransferase activity of α1,4-N-acetylglucosaminyltransferase (A4GNT). Functionally, TFF2 organized membranous TFF2-A4GNT-glycan protein complex serves to restrain cellular inflammation pathways by augmenting inhibitory Tyr527 phosphorylation at the C-terminus of src-family kinases (SFKs), thereby effectively preventing the phosphorylation of stimulatory SFKs Tyr416. Finally, we have conclusively verified that the protective effect of TFF2 treatment relies on the TFF2-A4GNT-glycan axis during influenza virus infection. In the future, TFF2 may offer a new potential intervention strategy for inflammatory diseases induced by acute respiratory infections.
DOI: 10.25574/45567
title: Chandra X-ray Observatory ObsId 45567
DOI: 10.25574/45581
title: Chandra X-ray Observatory ObsId 45581
DOI: 10.17632/dv29j554sb.1
title: Data and MATLAB Code for Reinforcement Learning Framework based Shuffled Multi Opposition-based Learning Evolutionary Algorithm

description: Shuffled algorithms partition a population into groups, each undergoing an independent evolutionary process. To enhance search diversity and prevent premature convergence, the shuffled multi evolutionary algorithm (SMEA) was introduced, allowing each group to evolve using a randomly selected evolutionary process. This paper presents an improved version of SMEA, called the reinforcement learning-based shuffled multi opposition-based learning evolutionary algorithm (RLSMOBEA). The proposed algorithm integrates opposition-based versions of three evolutionary processes: shuffled frog leaping (SFL), shuffled complex evolution (SCE), and shuffled differential evolution (SDE), within its local search stage. Unlike SMEA, which selects the evolutionary process randomly, RLSMOBEA incorporates reinforcement learning (RL) and SMEA to intelligently choose the most suitable evolutionary process at each iteration among the available candidates. Furthermore, the RLSMOBEA algorithm is enhanced by incorporating linear population size reduction (LPSR), leading to the development of the L-RLSMOBEA variant. This modification employs a linear function to gradually decrease both the population size and the number of groups during the optimization process. The performance of L-RLSMOBEA is rigorously assessed using the CEC2014 and CEC2017 benchmark test suites