cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.ejrbf3
title: Occurrence Download

description: A dataset containing 1124142 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is PRECIS"
 ]
}

 The dataset includes 1124142 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069086-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r5lwtp
title: Occurrence Download

description: A dataset containing 7925 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mikania micrantha Kunth"
 ]
}

 The dataset includes 7925 records from 223 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012688-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u8f5bt
title: Occurrence Download

description: A dataset containing 646202 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-124.8046875 13.923403897723347, -125.5078125 23.563987128451217, -119.17968749999999 29.84064389983441, -117.0703125 33.7243396617476, -106.875 33.43144133557529, -96.6796875 30.44867367928756, -88.59374999999999 25.48295117535531, -85.4296875 23.563987128451217, -79.453125 23.241346102386135, -68.203125 24.206889622398023, -61.17187499999999 21.289374355860424, -48.515625 14.604847155053898, -33.75 1.0546279422758869, -33.046875 -12.211180191503997, -37.6171875 -27.68352808378776, -42.890625 -40.17887331434695, -51.67968749999999 -46.55886030311717, -60.46875 -47.75409797968002, -74.1796875 -49.15296965617039, -88.24218749999999 -46.8000594467873, -110.74218749999999 -29.535229562948444, -121.28906250000001 -2.811371193331128, -124.8046875 13.923403897723347))",
 "HasCoordinate is true",
 "TaxonKey is Cathartes aura (Linnaeus, 1758)",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 646202 records from 170 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014447-180131172636756/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4b69r2
title: Occurrence Download

description: A dataset containing 3622 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Canada",
 "DatasetKey is iNaturalist Research-grade Observations",
 "Geometry POLYGON((-81.93604 45.4834,-82.59521 41.98975,-79.05762 42.9126,-78.90381 43.81348,-76.4209 44.0332,-74.24561 45.39551,-72.97119 46.16455,-81.93604 45.4834))",
 {
 "or" : [
 "TaxonKey is Pseudacris crucifer (Wied-Neuwied, 1838)",
 "TaxonKey is Hyla versicolor LeConte, 1825"
 ]
 },
 "Year 2018-2021"
 ]
}

 The dataset includes 3622 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069076-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zqn0vv
title: Occurrence Download

description: A dataset containing 65 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-117.33398 32.84267,-112.76367 16.63619,-94.13086 14.26438,-91.14258 18.14585,-87.09961 18.14585,-85.86914 21.61658,-88.33008 22.91792,-91.31836 21.45307,-96.41602 25.16517,-117.33398 32.84267))",
 "HasCoordinate is true",
 "TaxonKey is Carcharodon carcharias (Linnaeus, 1758)",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 65 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012688-190320150433242/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.srujub
title: Occurrence Download

description: A dataset containing 4585 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Paratrechina longicornis (Latreille, 1802)"
 ]
}

 The dataset includes 4585 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014447-180824113759888/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s7tb8a
title: Occurrence Download

description: A dataset containing 19233 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "Geometry POLYGON((-91.38277 44.90677,-90.09904 42.17061,-87.91336 42.57155,-87.67957 44.50506,-89.79262 45.62223,-91.38277 44.90677))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "LifeStage is Adult",
 "OccurrenceStatus is Present",
 "TaxonKey is Lepidoptera"
 ]
}

 The dataset includes 19233 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058836-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xyteuk
title: Occurrence Download

description: A dataset containing 23579 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((3.92871 43.51828,15.24902 43.51828,15.24902 48.16755,3.92871 48.16755,3.92871 43.51828))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Cervus elaphus Linnaeus, 1758"
 ]
}

 The dataset includes 23579 records from 93 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069083-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.1hnsfp
title: Occurrence Download

description: A dataset containing 219154 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Danaus plexippus (Linnaeus, 1758)"
 ]
}

 The dataset includes 219154 records from 181 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012689-181003121212138/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dcaksp
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-78.19806 -40.63481,-65.36602 -40.63481,-65.36602 -35.50419,-78.19806 -35.50419,-78.19806 -40.63481))",
 "HasCoordinate is true",
 "TaxonKey is Pitavia punctata (Ruiz & Pav.) Molina",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 11 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014447-181003121212138/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c5acvd
title: Occurrence Download

description: A dataset containing 96 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Draba confertifolia Turcz."
 ]
}

 The dataset includes 96 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0061774-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e3dsrv
title: Occurrence Download

description: A dataset containing 7925 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mikania micrantha Kunth"
 ]
}

 The dataset includes 7925 records from 223 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012689-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3urr3h
title: Occurrence Download

description: A dataset containing 123 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Selaginella selaginoides (L.) Schrank & C.F.P.Mart.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is Japan"
 ]
}

 The dataset includes 123 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069587-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.227bgh
title: Occurrence Download

description: A dataset containing 3267561 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Acaciella angustissima (Mill.) Britton & Rose, Alternanthera brasiliana (L.) Kuntze, Argyreia nervosa (Burm.fil.) Bojer, Asparagus asparagoides (L.) Druce, Asparagus asparagoides (L.) W.Wight, Asparagus asparagoides Jessop, Bunchosia armeniaca (Cav.) Rich., Bunchosia armeniaca (Cav.) DC., Cascabela thevetia (L.) Lippold, Cecropia Loefl., Cecropis F.Boie, 1826, Ceropia, Chloris virgata Sw., Chloris virgata P.Durand, Celosia virgata Jacq., Chelonia virgata Schweigger, 1812, Cyperus aromaticus (Ridl.) Mattf. & Kük., Grewia asiatica L., Grewia asiatica Wall., 1829, Ipomoea quamoclit L., Jatropha gossypiifolia L., Murraya paniculata (L.) Jacq., Murraya paniculata (L.) Jack, Murraya paniculata Hassk., Murraya paniculata Kaneh., Neptunia oleracea Lour., Opuntia engelmannii Salm-Dyck ex Engelm., Opuntia engelmannii Salm-Dyck, Polyalthia longifolia (Sonn.) Thwaites, Polyalthia longifolia Benth. & Hook.f., Polyalthia oblongifolia Burck, Polyalthia oblongifolia C.B.Rob., Pueraria montana var. lobata (Willd.) Maesen & S.M.Almeida ex Sanjappa & Predeep, Pueraria montana var. lobata (Willd.) Maesen & S.M.Almeida, Pueraria montana var. lobata Wild., Pueraria montana subsp. lobata (Willd.) Maesen & S.M.Almeida, Pueraria montana subsp. lobata (Willd.) Maesen & S.M.Almeida ex Sanjappa & Predeep, Roya, Rosa L., Rasa Saldaitis & Yakovlev, 2019, Risa Becker, 1907, Rota Bat., Cif. & Nascim., Rota Langenheim & Epis, 1957, Roya W.West & G.S.West, 1896, Rusa C.H.Smith, 1827, Roea Hügel ex Benth., Roia Scop., Rosca Schioedte & Meinert, 1884, Rosea Fabr., Rosea Klotzsch, Rosea Mart., Roya Iredale, 1912, Rosea de Puytorac, 1994, Rusa Griffith, 1827, Rosea (Barrois, 1876) Barrois, 1876, Roa Jordan, 1923, Roosa de Laubenfels, 1934, Rottboellia cochinchinensis (Lour.) Clayton, Sagittaria platyphylla (Engelm.) J.G.Sm., Senegalia insuavis (Lace) Pedley, Sporobolus pyramidalis P.Beauv., Syzygium cumini (L.) Skeels, Vachellia karroo (Hayne) Banfi & Galasso, Vachellia nilotica (L.) P.J.H.Hurter & Mabb., Ziziphus spina-christi (L.) Desf., Ziziphus spina-christi (L.) Willd, Ziziphus spina-christi (Mill.) Georgi, Solanum torvum Sw., Solanum torvum Buch.-Ham., Solanum torvum Buch.-Ham. ex Wall., Solanum torvum Schltdl., 1830, Solanum torreyi A.Gray, Solanum tonsum H.St.John, Sporobolus natalensis (Steud.) T.Durand & Schinz, Sporobolus nogalensis Chiov., Sporobolus jacquemontii Kunth, Sporobolus fertilis (Steud.) Clayton)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than or equal to 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 3267561 records from 5784 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069564-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/6zwpxi
title: Lake_Transparency_NH4.tab

description: :unav
DOI: 10.57745/etie5k
title: Lake_Phytoplankton.csv

description: :unav
DOI: 10.57745/hfmxwu
title: Lake_Laboratory_Analyses.csv

description: :unav
DOI: 10.57745/stap4i
title: Lake_High-Frequency_Physico-chemical_Parameters.tab

description: :unav
DOI: 10.57745/xq6crz
title: Tributaries_Monthly_Physical_Parameters.tab

description: :unav
DOI: 10.57745/yxt1fo
title: Lake_Monthly_Physical_Parameters.tab

description: :unav
DOI: 10.6084/m9.figshare.25451161
title: Ecological health assessment of Tibetan alpine grasslands in Gannan using remote sensed ecological indicators

description: Ecosystem health assessments are crucial to protect the ecological environment and ensure the sustainable ecological functions of alpine ecoregions. At present, few studies evaluating the ecosystem health of the Gannan alpine grassland, China, an ecologically fragile area, based on a remote sensing theoretical framework exist. As such, this study assessed the ecosystem health of the Gannan alpine grassland based on the Remote Sensing-based Ecological Index (RSEI) and provided a comparative analysis of the RSEI and Gross Primary Productivity (GPP), extending the study of their spatiotemporal patterns and influencing factors. The results suggested that RSEI and GPP showed strong comparability in an ecological sense, with the RSEI better reflecting changes in ecosystem health of the Gannan alpine grassland than the GPP. Overall, the health of the Gannan alpine grassland ecosystem was good (RSEI of 0.61–0.76) and a slow, fluctuating upward trend was seen from 2000 (RSEI = 0.66) to 2020 (RSEI = 0.72). Notably, the RSEI was high in the south and low in the north of the region. Over the past 21 years, 43.92% of the ecologically healthy grassland in the southwest of Gannan has been degrading, while the poor ecological health of 39.04% of the grasslands in the southeast and northeast improved. The model test results show that RSEI could reasonably evaluate the ecosystem health of Gannan alpine grassland. Our assessment results provide important scientific data and information on health monitoring and targeted ecological restoration efforts in the Gannan region.
DOI: 10.6084/m9.figshare.25451161.v1
title: Ecological health assessment of Tibetan alpine grasslands in Gannan using remote sensed ecological indicators

description: Ecosystem health assessments are crucial to protect the ecological environment and ensure the sustainable ecological functions of alpine ecoregions. At present, few studies evaluating the ecosystem health of the Gannan alpine grassland, China, an ecologically fragile area, based on a remote sensing theoretical framework exist. As such, this study assessed the ecosystem health of the Gannan alpine grassland based on the Remote Sensing-based Ecological Index (RSEI) and provided a comparative analysis of the RSEI and Gross Primary Productivity (GPP), extending the study of their spatiotemporal patterns and influencing factors. The results suggested that RSEI and GPP showed strong comparability in an ecological sense, with the RSEI better reflecting changes in ecosystem health of the Gannan alpine grassland than the GPP. Overall, the health of the Gannan alpine grassland ecosystem was good (RSEI of 0.61–0.76) and a slow, fluctuating upward trend was seen from 2000 (RSEI = 0.66) to 2020 (RSEI = 0.72). Notably, the RSEI was high in the south and low in the north of the region. Over the past 21 years, 43.92% of the ecologically healthy grassland in the southwest of Gannan has been degrading, while the poor ecological health of 39.04% of the grasslands in the southeast and northeast improved. The model test results show that RSEI could reasonably evaluate the ecosystem health of Gannan alpine grassland. Our assessment results provide important scientific data and information on health monitoring and targeted ecological restoration efforts in the Gannan region.
DOI: 10.6084/m9.figshare.27629150
title: Optimal parameter schemes for global and regional gross primary productivity estimation: a comparative analysis

description: The spatiotemporal heterogeneity of the parameters greatly impacts the estimation of Gross Primary Productivity (GPP) by Light Use Efficiency (LUE) models. Vegetation type-specific parameters are commonly set at present; however, the various environmental heterogeneities at global and regional scales can also induce disparate optimal parameter schemes, which has not been further explored. Therefore, in this study, we comparatively explored the parameter differences and GPP estimation accuracy under different parameter schemes, including Vegetation type Parameters (VPs), Fixed Parameters (FPs), Vegetation type Monthly Parameters (VMPs), and Fixed Monthly Parameters (FMPs), at both global and regional scales. Two different strategies were applied to validate the ability of the temporal prediction and spatial expansion for the four parameter schemes. The results indicate that the VP scheme shows only a limited superiority over the FP scheme for GPP estimation at the global scale (ΔR<sup>2</sup> = 0.01–0.02), which indicates that the VP scheme is not necessary for the global application of LUE models, due to the considerable spatiotemporal heterogeneity of vegetation. However, for regional applications, such as the Mediterranean region, the VP scheme is preferable to the FP scheme, with an improved ΔR<sup>2</sup> of more than 0.05, which is because the vegetation within the same type is much more similar than at a global scale. Furthermore, it is found that the time-varying parameters (VMPs and FMPs) contribute little to the GPP simulation at both global and regional scales, which is possibly due to the limited amount of available data. Overall, the results of this study will not only offer guidance and optimal parameter schemes for the global and regional estimation of GPP, but also highlight the importance of considering spatiotemporal heterogeneity for parameters.
DOI: 10.6084/m9.figshare.27629150.v1
title: Optimal parameter schemes for global and regional gross primary productivity estimation: a comparative analysis

description: The spatiotemporal heterogeneity of the parameters greatly impacts the estimation of Gross Primary Productivity (GPP) by Light Use Efficiency (LUE) models. Vegetation type-specific parameters are commonly set at present; however, the various environmental heterogeneities at global and regional scales can also induce disparate optimal parameter schemes, which has not been further explored. Therefore, in this study, we comparatively explored the parameter differences and GPP estimation accuracy under different parameter schemes, including Vegetation type Parameters (VPs), Fixed Parameters (FPs), Vegetation type Monthly Parameters (VMPs), and Fixed Monthly Parameters (FMPs), at both global and regional scales. Two different strategies were applied to validate the ability of the temporal prediction and spatial expansion for the four parameter schemes. The results indicate that the VP scheme shows only a limited superiority over the FP scheme for GPP estimation at the global scale (ΔR<sup>2</sup> = 0.01–0.02), which indicates that the VP scheme is not necessary for the global application of LUE models, due to the considerable spatiotemporal heterogeneity of vegetation. However, for regional applications, such as the Mediterranean region, the VP scheme is preferable to the FP scheme, with an improved ΔR<sup>2</sup> of more than 0.05, which is because the vegetation within the same type is much more similar than at a global scale. Furthermore, it is found that the time-varying parameters (VMPs and FMPs) contribute little to the GPP simulation at both global and regional scales, which is possibly due to the limited amount of available data. Overall, the results of this study will not only offer guidance and optimal parameter schemes for the global and regional estimation of GPP, but also highlight the importance of considering spatiotemporal heterogeneity for parameters.
DOI: 10.15156/bio/sh0198400.10fu
title: SH0198400.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0198401.10fu
title: SH0198401.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0198402.10fu
title: SH0198402.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0198403.10fu
title: SH0198403.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.