cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27093745
title: <b>Nest defense </b><b>by </b><b>incubating female birds: same threat, different strategies</b>

description: This study was conducted in the Miao minority villages of Leishan County (26°02′-34′ N, 107°55′-108°22′ E), Guizhou Province, southwestern China. It has a total area of 1218.5 km<sup>2</sup> and a humid mid-subtropical monsoon climate with an average annual temperature between 14 °C and 15 °C in most areas, an average maximum temperature of 35.6℃, and an average minimum temperature of -8.9℃. Leishan County is rich in forestry resources, with a forest coverage rate of 72.8% (Zhang <i>et al</i>. 2023).The experimental period of this study was performed in the months of March–July during the years 2021–2023. The study was conducted by hanging artificial nesting boxes to recruit birds for breeding; the two main species of birds utilizing these nesting boxes were Japanese tits and russet sparrows.This study investigated the nest defense strategies of two sympatric cavity-nesting bird species in response to predation risk, brood parasitism risk, and non-lethal human interference. The nest defense behavior of incubating females of Japanese tits (<i>Parus minor</i>) and russet sparrows (<i>Passer cinnamomeus</i>) were tested in response to three types of risk. Both species could eavesdrop on heterospecific alarm calls and effectively distinguish between the territorial calls and alarm calls of Japanese tits. However, the two sympatric cavity-nesting bird species adopted completely different strategies for nest defense. Japanese tits ensure current breeding success by remaining quietly inside the nest without revealing their locations when confronted with threats. In contrast, russet sparrows prioritize their own survival and promptly fly out of the nest when danger approaches. Our study revealed a trade-offs between bird parents’ own survival and reproduction and showed that sympatric cavity-nesting bird species adopt different nest defense strategies when faced with the same threats.
DOI: 10.6084/m9.figshare.27093745.v1
title: <b>Nest defense </b><b>by </b><b>incubating female birds: same threat, different strategies</b>

description: This study was conducted in the Miao minority villages of Leishan County (26°02′-34′ N, 107°55′-108°22′ E), Guizhou Province, southwestern China. It has a total area of 1218.5 km<sup>2</sup> and a humid mid-subtropical monsoon climate with an average annual temperature between 14 °C and 15 °C in most areas, an average maximum temperature of 35.6℃, and an average minimum temperature of -8.9℃. Leishan County is rich in forestry resources, with a forest coverage rate of 72.8% (Zhang <i>et al</i>. 2023).The experimental period of this study was performed in the months of March–July during the years 2021–2023. The study was conducted by hanging artificial nesting boxes to recruit birds for breeding; the two main species of birds utilizing these nesting boxes were Japanese tits and russet sparrows.This study investigated the nest defense strategies of two sympatric cavity-nesting bird species in response to predation risk, brood parasitism risk, and non-lethal human interference. The nest defense behavior of incubating females of Japanese tits (<i>Parus minor</i>) and russet sparrows (<i>Passer cinnamomeus</i>) were tested in response to three types of risk. Both species could eavesdrop on heterospecific alarm calls and effectively distinguish between the territorial calls and alarm calls of Japanese tits. However, the two sympatric cavity-nesting bird species adopted completely different strategies for nest defense. Japanese tits ensure current breeding success by remaining quietly inside the nest without revealing their locations when confronted with threats. In contrast, russet sparrows prioritize their own survival and promptly fly out of the nest when danger approaches. Our study revealed a trade-offs between bird parents’ own survival and reproduction and showed that sympatric cavity-nesting bird species adopt different nest defense strategies when faced with the same threats.
DOI: 10.21227/g364-hm64
title: Training& testing

description: This project investigates the use of machine learning algorithms for the task of supplier selection in supply chain analytics for Medizinn, a company producing medical and industrial equipment electronics. Supplier selection is important in order to achieve operational effectiveness and to deliver quality products, however, classical approaches towards supplier selection are often inadequate for the current supply chain lot of complexities and large amounts of data. This paper aims to address this issue of concern for decision support systems applications through the application of machine learning, specifically decision trees, random forests and support vector machines, to enhance and automate the process of supplier evaluation.This research employs the historical supplier data collecting, features engineering and machine learning modeling to forecast a supplier based on such criteria as cost, quality, and reliability of delivery. The researcher trained and tested the models on supplier data to make predictions and recommendations. The findings of the study show that the application of machine learning does improve the importance and objectivity of the supplier selection process by minimizing human errors and time taken to make decisions. The accuracy of the predictive models was assessed by performance metrics including precision, recall and F1-score.The discussion pertains to the arguments in favor of the use of machine learning such as more effective supplier assessment, exploitation of enormous amounts of data, and operational model development with time through new information. This project also mentions frustrations such as the quality of data and the difficulty in understanding advanced models of machine learning. Overall this project shows that using machine learning in supply chain analytics assists in making better supplier selection decisions, simplifying the procurement process, and improving risk management. This study makes a case that machine learning can be embedded in the supply chain and offer relevant solutions that enhance the reliability, cost, and performance of the supply chain for example at Medizinn. The suggestions emphasize the need for suitable future research that integrates real time data with complex algorithms towards supplier selection.
DOI: 10.15468/dl.ryvb5b
title: Occurrence Download

description: A dataset listing the 3377 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is Ohio State Acarology Laboratory (OSAL), Ohio State University"
 ]
}

 The dataset's 3377 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027723-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5hp3gq
title: Occurrence Download

description: A dataset containing 69846 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Ohio State Acarology Laboratory (OSAL), Ohio State University"
 ]
}

 The dataset includes 69846 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027724-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4121/70a2edc7-5fa9-455c-8eca-3bbf2047bc06
title: Data and code belonging to the article "Too much of a good thing? Estimating the trade-off between voter turnout and the representativeness of election results when studying turnout shocks."

description: The materials here will replicate the main results of the paper "Too much of a good thing? Estimating the trade-off between voter turnout and the representativeness of election results when studying turnout shocks.", being Figure 2 and Tables 1 and 3. The enclosed files (almost all of which are Stata-files) will create the dataset used for the analysis and the main figures and tables of the paper. The code begins with files from input sources. That is, all steps of the process, including data cleaning, are performed in this replication. Note that in the paper we also use data from a LISS panel survey called "Wiv Referendum 2018: Third measurement". This data is not included in the files and is not required for running the do-file. The agency that ran this survey prefers it when researchers directly download this (opensource) data from their website: https://www.dataarchive.lissdata.nl/study-units/view/815 (doi: 10.17026/dans-xcr-n8az)<br><strong style="background-color:rgb(255, 238, 204);">Derived From:</strong>Statistics NetherlandsKiesraadSpatial expertise center University of Groningen
DOI: 10.4121/70a2edc7-5fa9-455c-8eca-3bbf2047bc06.v1
title: Data and code belonging to the article "Too much of a good thing? Estimating the trade-off between voter turnout and the representativeness of election results when studying turnout shocks."

description: The materials here will replicate the main results of the paper "Too much of a good thing? Estimating the trade-off between voter turnout and the representativeness of election results when studying turnout shocks.", being Figure 2 and Tables 1 and 3. The enclosed files (almost all of which are Stata-files) will create the dataset used for the analysis and the main figures and tables of the paper. The code begins with files from input sources. That is, all steps of the process, including data cleaning, are performed in this replication. Note that in the paper we also use data from a LISS panel survey called "Wiv Referendum 2018: Third measurement". This data is not included in the files and is not required for running the do-file. The agency that ran this survey prefers it when researchers directly download this (opensource) data from their website: https://www.dataarchive.lissdata.nl/study-units/view/815 (doi: 10.17026/dans-xcr-n8az)<br><strong style="background-color:rgb(255, 238, 204);">Derived From:</strong>Statistics NetherlandsKiesraadSpatial expertise center University of Groningen
DOI: 10.5281/zenodo.13831445
title: Non-Newtonian Power-law fluid simulations in rectangular channel

description: Velocity distribution in rectangular channel for Power law fluid.

Using viscosity: eta(x)=K*(x/gamma0)^(n-1)/gamma0

Solved the PDE equation: https://doc.comsol.com/5.5/doc/com.comsol.help.comsol/comsol_ref_equationbased.23.008.htmlin rectangular channel in COMSOL withc=eta(sqrt(d(u,x)^2+d(u,y)^2), f=0.013333333 [Pa*s]e_a, d_a, alpha, beta, gamma=0gamma0=1[1/s]Boundary conditions on x=0, y=0, x=witdh, y=height is u=0

Output file contains:  Coordinates X, Y [mm] on triangular grid and Velocity u [m/s]

File specific parameters:"H2O_100x096.txt" - n=1, K=0.013333333 [Pa*s], height=1 [mm], width=0.96 [mm]"10pFBS_37C_100x115.txt" - n=0.5, K=0.00541540444218501 [Pa*s], height=1 [mm], width=1.15 [mm]"10pFBS_097x092.txt" - n=0.32, K=0.00541540444218501 [Pa*s], height=0.97 [mm], width=0.92 [mm]
DOI: 10.5281/zenodo.13831446
title: Non-Newtonian Power-law fluid simulations in rectangular channel

description: Velocity distribution in rectangular channel for Power law fluid.

Using viscosity: eta(x)=K*(x/gamma0)^(n-1)/gamma0

Solved the PDE equation: https://doc.comsol.com/5.5/doc/com.comsol.help.comsol/comsol_ref_equationbased.23.008.htmlin rectangular channel in COMSOL withc=eta(sqrt(d(u,x)^2+d(u,y)^2), f=0.013333333 [Pa*s]e_a, d_a, alpha, beta, gamma=0gamma0=1[1/s]Boundary conditions on x=0, y=0, x=witdh, y=height is u=0

Output file contains:  Coordinates X, Y [mm] on triangular grid and Velocity u [m/s]

File specific parameters:"H2O_100x096.txt" - n=1, K=0.013333333 [Pa*s], height=1 [mm], width=0.96 [mm]"10pFBS_37C_100x115.txt" - n=0.5, K=0.00541540444218501 [Pa*s], height=1 [mm], width=1.15 [mm]"10pFBS_097x092.txt" - n=0.32, K=0.00541540444218501 [Pa*s], height=0.97 [mm], width=0.92 [mm]
DOI: 10.15468/dl.acpbgd
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Madagascar",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pandanus utilis Bory"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027725-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7nwxpw
title: Occurrence Download

description: A dataset containing 70 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Madagascar",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pontederia crassipes Mart."
 ]
}

 The dataset includes 70 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027728-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n26czg
title: Occurrence Download

description: A dataset containing 1869 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-148.48214 5.61522,-56.51786 5.61522,-56.51786 42.38478,-148.48214 42.38478,-148.48214 5.61522))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Choeronycteris mexicana Tschudi, 1844"
 ]
}

 The dataset includes 1869 records from 69 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027729-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tztwk2
title: Occurrence Download

description: A dataset containing 157 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Spain",
 "OccurrenceStatus is Present",
 "TaxonKey is Hippolais icterina (Vieillot, 1817)",
 "Year 2019-2024"
 ]
}

 The dataset includes 157 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027730-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yddpst
title: Occurrence Download

description: A dataset containing 4824 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Spain",
 "OccurrenceStatus is Present",
 "TaxonKey is Iduna opaca (Cabanis, 1850)",
 "Year 2019-2024"
 ]
}

 The dataset includes 4824 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027731-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6xm3tt
title: Occurrence Download

description: A dataset containing 237482 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Amazona viridigenalis (Cassin, 1853), Amphispiza bilineata (Cassin, 1850), Antrostomus carolinensis (J.F.Gmelin, 1789), Aphelocoma coerulescens (Bosc, 1795), Baeolophus inornatus (Gambel, 1845), Baeolophus ridgwayi (Richmond, 1902), Catherpes mexicanus (Swainson, 1829), Colaptes chrysoides (Malherbe, 1852), Colinus virginianus (Linnaeus, 1758), Columbina inca (R.Lesson, 1847), Cyrtonyx montezumae (Vigors, 1830), Dryobates nuttallii (Gambel, 1843), Euptilotis neoxenus (Gould, 1838), Geococcyx californianus (Lesson, 1829), Glaucidium gnoma Wagler, 1832, Gymnorhinus cyanocephalus Wied-Neuwied, 1841, Lagopus leucura (Richardson, 1831), Megascops kennicottii (D.G.Elliot, 1867), Melozone aberti (S.F.Baird, 1852), Melozone crissalis (Vigors, 1839), Mimus polyglottos (Linnaeus, 1758), Oreortyx pictus (Douglas, 1829), Pachyramphus aglaiae (Lafresnaye, 1839), Peucaea aestivalis (M.H.K.Lichtenstein, 1823), Peucaea carpalis Coues, 1873, Pica nuttalli (Audubon, 1837), Picoides arcticus (Swainson, 1832), Poecile gambeli (Ridgway, 1886), Polioptila californica Brewster, 1881, Psaltriparus minimus (J.K.Townsend, 1837), Aratinga holochlora (P.L.Sclater, 1859), Rhynchopsitta pachyrhyncha (Swainson, 1827), Sitta pusilla Latham, 1790, Sitta pygmaea Vigors, 1839, Strix occidentalis (Xántus de Vésey, 1860), Toxostoma lecontei Lawrence, 1851, Tympanuchus phasianellus (Linnaeus, 1758))",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation)",
 "Country is one of (United States of America, Canada, Mexico, Greenland, Aruba, Anguilla, Antigua and Barbuda, Bahamas, Barbados, Saint Barthélemy, Bonaire, Sint Eustatius and Saba, Bermuda, Belize, Costa Rica, Cuba, Curaçao, Dominica, Dominican Republic, Guatemala, Grenada, Guadeloupe, Haiti, Honduras, Jamaica, Saint Kitts and Nevis, Cayman Islands, Montserrat, Saint Martin (French part), Nicaragua, Panama, Puerto Rico, El Salvador, Turks and Caicos Islands, Trinidad and Tobago, Sint Maarten (Dutch part), Saint Vincent and the Grenadines, Virgin Islands (British))",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1990",
 "CoordinateUncertaintyInMeters is less than 999",
 "ScientificName is not null",
 "DecimalLongitude is not null",
 "DecimalLatitude is not null"
 ]
}

 The dataset includes 237482 records from 88 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027726-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qs3s2z
title: Occurrence Download

description: A dataset containing 297 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Spain",
 "OccurrenceStatus is Present",
 "TaxonKey is Iduna pallida (Hemprich & Ehrenberg, 1833)",
 "Year 2019-2024"
 ]
}

 The dataset includes 297 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027732-240906103802322/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.27093808
title: Spinach PanTE

description: pan-TE of spinach
DOI: 10.6084/m9.figshare.27093808.v1
title: Spinach PanTE

description: pan-TE of spinach
DOI: 10.15468/dl.ehz4cs
title: Occurrence Download

description: A dataset containing 1869 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-148.48214 5.61522,-56.51786 5.61522,-56.51786 42.38478,-148.48214 42.38478,-148.48214 5.61522))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Choeronycteris mexicana Tschudi, 1844"
 ]
}

 The dataset includes 1869 records from 69 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027733-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h3r67f
title: Occurrence Download

description: A dataset containing 40834 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Poa chaixii Vill."
 ]
}

 The dataset includes 40834 records from 539 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027734-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13831379
title: data for Paper # 2024GL112625

description: The file include data (grl_data.xls) and code (Calibration_main.m and DiffCalibrationTestPeriod.m) used in the paper, which has been submitted to Geophysical Research Letters (A field calibration method that improves soil heat flux accuracy, [Paper # 2024GL112625]). The data includ soil data (heat flux measured by six soil heat flux plates, soil temperature measured at the depths of 0.9, 2.1, 4.5, 10, 25, and 50 cm, soil moisture measured at the depths of 2, 10, and 25 cm) and weather data. The code used to analyze the data was in Matlab Format.
DOI: 10.5281/zenodo.13831380
title: data for Paper # 2024GL112625

description: The file include data (grl_data.xls) and code (Calibration_main.m and DiffCalibrationTestPeriod.m) used in the paper, which has been submitted to Geophysical Research Letters (A field calibration method that improves soil heat flux accuracy, [Paper # 2024GL112625]). The data includ soil data (heat flux measured by six soil heat flux plates, soil temperature measured at the depths of 0.9, 2.1, 4.5, 10, 25, and 50 cm, soil moisture measured at the depths of 2, 10, and 25 cm) and weather data. The code used to analyze the data was in Matlab Format.
DOI: 10.6084/m9.figshare.24872142
title: Genotype of pan-SV in 452 Spinacia accessions

description: Genotype of pan-SV in 452 <i>Spinacia</i> accessions
DOI: 10.6084/m9.figshare.24872142.v1
title: Genotype of pan-SV in 452 Spinacia accessions

description: Genotype of pan-SV in 452 <i>Spinacia</i> accessions
DOI: 10.15468/dl.gkbuef
title: Occurrence Download

description: A dataset containing 84 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Madagascar",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ravenala madagascariensis Sonn."
 ]
}

 The dataset includes 84 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027735-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nac2kg
title: Occurrence Download

description: A dataset containing 13719 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Spain",
 "OccurrenceStatus is Present",
 "TaxonKey is Cinclus cinclus (Linnaeus, 1758)",
 "Year 2019-2024"
 ]
}

 The dataset includes 13719 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027738-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j5eyyw
title: Occurrence Download

description: A dataset containing 104 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Taxonomy and molecular phylogeny of the Platystictidae of Sri Lanka (Insecta: Odonata)"
 ]
}

 The dataset includes 104 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027736-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.59v7rd
title: Occurrence Download

description: A dataset containing 34258 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Animal Sound Archive"
 ]
}

 The dataset includes 34258 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027737-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3jz3ha
title: Occurrence Download

description: A dataset containing 2013 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Spain",
 "OccurrenceStatus is Present",
 "TaxonKey is Corvus frugilegus Linnaeus, 1758",
 "Year 2019-2024"
 ]
}

 The dataset includes 2013 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027739-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.