cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/4tw223jvjv.1
title: Genetic Programming for Land use allocation optimization

description: Land use allocation optimization is essential to identify ideal landscape compositions for the future. However, standard land use allocation optimization algorithms require long computation times. Solutions to land use allocation problems are encoded as sequences of elements, where each element represents a land unit or a group of land units. The manipulation solutions with this encoding result in long computation times that increase with every additional land unit. Therefore, we present an alternative solution encoding: functions describing a variable in space. The encoding yields the potential to evolve solutions detached from the single land units and evolve fields representing the landscape as one object. In this study, we test the scaling potential of evolving functions to solve land use allocation problems. We use a Genetic Programming algorithm to evolve functions representing continuous fields, which we then map to nominal land use maps. We compare the new approach with two state-of-the-art algorithms that use standard encoding. We perform the benchmark on one raster and one vector land use allocation problem with multi-objectives and constraints, with 10 problem sizes each. The results prove that Genetic Programming alleviates the problem of exponentially increasing run times of the traditional encoding scheme. On the largest raster problem, Genetic Programming was up to 722 times faster than the benchmark algorithm, with 27 seconds in contrast to 325 minutes. The improvement in computation time does not affect the algorithm performance in finding optimal solutions; Genetic Programming outperforms the benchmark algorithms on both problems. Due to the function encoding, Genetic Programming can produce land use maps with spatial patterns that are unlikely to obtain with the standard encoding. We conclude that evolving functions enables more efficient land use allocation planning and yields much potential for other spatial optimization applications.
DOI: 10.17632/4tw223jvjv.2
title: Genetic Programming for Land use allocation optimization

description: Land use allocation optimization is essential to identify ideal landscape compositions for the future. However, due to the solution encoding, standard land use allocation algorithms cannot cope with large land use allocation problems. Solutions are encoded as sequences of elements, where each element represents a land unit or a group of land units. As a consequence, computation times increase with every additional land unit. We present an alternative solution encoding: functions describing a variable in space. Function encoding yields the potential to evolve solutions detached from individual land units and evolve fields representing the landscape as a single object. In this study, we use a genetic programming algorithm to evolve functions representing continuous fields, which we then map to nominal land use maps. We compare the scalability of the new approach with the scalability of two state-of-the-art algorithms with standard encoding. We perform the benchmark on one raster and one vector land use allocation problem with multiple objectives and constraints, with ten problem sizes each. The results prove that the run times increase exponentially with the problem size for standard encoding schemes, while the increase is linear with genetic programming. Genetic programming was up to 722 times faster than the benchmark algorithm. The improvement in computation time does not reduce the algorithm performance in finding optimal solutions; often it even increases.  
We conclude that evolving functions enables more efficient land use allocation planning and yields much potential for other spatial optimization applications.
DOI: 10.17632/4tw223jvjv.3
title: Genetic programming for computationally efficient land use allocation optimization

description: Land use allocation optimization is essential to identify ideal landscape compositions for the future. However, due to the solution encoding, standard land use allocation algorithms cannot cope with large land use allocation problems. Solutions are encoded as sequences of elements, where each element represents a land unit or a group of land units. As a consequence, computation times increase with every additional land unit. We present an alternative solution encoding: functions describing a variable in space. Function encoding yields the potential to evolve solutions detached from individual land units and evolve fields representing the landscape as a single object. In this study, we use a genetic programming algorithm to evolve functions representing continuous fields, which we then map to nominal land use maps. We compare the scalability of the new approach with the scalability of two state-of-the-art algorithms with standard encoding. We perform the benchmark on one raster and one vector land use allocation problem with multiple objectives and constraints, with ten problem sizes each. The results prove that the run times increase exponentially with the problem size for standard encoding schemes, while the increase is linear with genetic programming. Genetic programming was up to 722 times faster than the benchmark algorithm. The improvement in computation time does not reduce the algorithm performance in finding optimal solutions; often it even increases.  
We conclude that evolving functions enables more efficient land use allocation planning and yields much potential for other spatial optimization applications.
DOI: 10.17632/4tw223jvjv.4
title: Genetic programming for computationally efficient land use allocation optimization

description: Land use allocation optimization is essential to identify ideal landscape compositions for the future. However, due to the solution encoding, standard land use allocation algorithms cannot cope with large land use allocation problems. Solutions are encoded as sequences of elements, where each element represents a land unit or a group of land units. As a consequence, computation times increase with every additional land unit. We present an alternative solution encoding: functions describing a variable in space. Function encoding yields the potential to evolve solutions detached from individual land units and evolve fields representing the landscape as a single object. In this study, we use a genetic programming algorithm to evolve functions representing continuous fields, which we then map to nominal land use maps. We compare the scalability of the new approach with the scalability of two state-of-the-art algorithms with standard encoding. We perform the benchmark on one raster and one vector land use allocation problem with multiple objectives and constraints, with ten problem sizes each. The results prove that the run times increase exponentially with the problem size for standard encoding schemes, while the increase is linear with genetic programming. Genetic programming was up to 722 times faster than the benchmark algorithm. The improvement in computation time does not reduce the algorithm performance in finding optimal solutions; often it even increases. 
We conclude that evolving functions enables more efficient land use allocation planning and yields much potential for other spatial optimization applications.
DOI: 10.15468/dl.k45ex2
title: Occurrence Download

description: A dataset containing 246477 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Culex pipiens Linnaeus, 1758"
 ]
}

 The dataset includes 246477 records from 298 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062637-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.crqetf
title: Occurrence Download

description: A dataset containing 68 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "GadmGid is USA.14_1",
 "TaxonKey is Bembix nubilipennis Cresson, 1873"
 ]
}

 The dataset includes 68 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062645-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ync2z7
title: Occurrence Download

description: A dataset containing 36936 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Griselinia littoralis (Raoul) Raoul",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 36936 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062644-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g4qun2
title: Occurrence Download

description: A dataset listing the 24 species recorded in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Puma concolor couguar (Kerr, 1792)"
 ]
}

 The dataset's 24 records were derived from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062641-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mj684f
title: Occurrence Download

description: A dataset containing 1358 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Guaiacum officinale L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1358 records from 89 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062646-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dxw8tg
title: Occurrence Download

description: A dataset containing 908266 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Bahamas",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 908266 records from 363 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062642-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15472/qnsvm0
title: Monitoreo de fauna en sabanas inundables en el municipio de Orocué (bloque Cachicamo)

description: Este recurso reúne los datos del tercer monitoreo de fauna del Bloque Cachicamo (LAM3585), ubicado en el municipio de Orocué (Casanare) y operado por Frontera Energy Corp. El trabajo en campo se desarrolló entre el 16 y 26 de agosto de 2024, y abarcó los grupos taxonómicos de anfibios, reptiles, aves y mamíferos. Las áreas de muestreo se distribuyeron en la vereda La Venturosa.
DOI: 10.24431/rw1k9gd
title: Prince William Sound Kelp Mariculture Development: Kelp Harvest Data of Nereocystis luetkeana, Alaria marginata, and Saccharina latissima at Six Test Sites, 2022-2023

description: These data are part of Chugach Regional Resources Commission’s (CRRC) Prince William Sound Kelp Mariculture Development for Habitat Restoration and Local Economy project. These data reflect harvest yields and growth information recorded at operational kelp research sites during the 2022-2023 growing season. These data consist of total poundage harvested from each species, as well as sample weights taken from three discrete, one-foot sections of the growline of each species outplanted at test sites. From these sample sections, the total number of plants was recorded by counting the number of holdfasts within that section. From each section, five plants were randomly selected and measured, recording length, from the holdfast to the end of the frond, and width of the frond at the widest part, to note basic, phenotypic differences. Test site locations include two sites on the Western side of Prince William Sound and four sites on the Eastern side of Prince William Sound. Three, 100-foot grow lines of three different species of kelp were outplanted at each test site. Species outplanted included sugar kelp (Saccharina latissima), ribbon kelp (Alaria marginata), and bull kelp (Nereocystis luetkeana).
DOI: 10.24431/rw1k9ge
title: Prince William Sound Kelp Mariculture Development: Oceanographic Data at Six Kelp Test Sites, 2022-2023

description: These data are part of Chugach Regional Resources Commission’s (CRRC) Prince William Sound Kelp Mariculture Development for Habitat Restoration and Local Economy project. These data reflect basic water quality parameters collected at operational kelp research sites during the 2022-2023 growing season. These data consist of water temperature, salinity, dissolved oxygen, and turbidity readings from CRRC and Native Conservancy’s (NC) kelp test site locations. Samplers used Yellow Springs Instruments (YSI) water quality meters for temperature, dissolved oxygen, and salinity readings at the sites; for turbidity readings, samplers used a secchi disk. When YSI measurements were unavailable due to technical or calibration issues, salinity and temperature readings were taken with a refractometer and thermometer, respectively. Test site locations include two sites on the Western side of Prince William Sound and four sites on the Eastern side of Prince William Sound. Three, 100-foot long grow lines of three different species of kelp were outplanted at each test site. Species outplanted included sugar kelp (Saccharina latissima), ribbon kelp (Alaria marginata), and bull kelp (Nereocystis luetkeana).
DOI: 10.24431/rw1k9gf
title: Prince William Sound Kelp Mariculture Development: Nutrient Data at Six Kelp Test Sites, 2022-2023

description: These data are part of Chugach Regional Resources Commission’s (CRRC) Prince William Sound Kelp Mariculture Development for Habitat Restoration and Local Economy project. These data reflect nutrient concentrations in water samples collected at operational kelp research sites during the 2022-2023 growing season. These data consist of measurements of quantities of nitrate, ammonia, total phosphate, and silicate obtained from samples collected at CRRC and Native Conservancy’s (NC) kelp test site locations. Samples were collected at the water’s surface level and at 3 meters below the water’s surface. Samplers used a Niskin bottle to collect samples at depth; samples were collected and stored in a cool, dark location, until samplers returned from the field, where samples were immediately frozen. Frozen samples collected on the Eastern side of Prince William Sound by Native Conservancy’s sampling team were sent on ice in coolers to CRRC’s lab facility in Seward at the Alutiiq Pride Marine Institute. Samples collected on the Western side of Prince William Sound were driven to the Alutiiq Pride Marine Institute. All samples received at APMI were frozen until they could be logged and sent to Alaska Water Labs in Wasilla for analysis. Test site locations include two sites on the Western side of Prince William Sound and four sites on the Eastern side of Prince William Sound. Three, 100-foot grow lines of three different species of kelp were outplanted at each test site. Species outplanted included sugar kelp (Saccharina latissima), ribbon kelp (Alaria marginata), and bull kelp (Nereocystis luetkeana). These data are collated here in numerical, table form, and also converted to boxplots to provide a more visual demonstration of nutrient levels sampled throughout the growing season.
DOI: 10.5281/zenodo.14623747
title: eriocaulon_aquaticum_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.14623748
title: eriocaulon_aquaticum_zanfona_1.fasta.gz
DOI: 10.15468/dl.qm95xj
title: Occurrence Download

description: A dataset containing 7891 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gymnocladus dioicus (L.) K.Koch",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 7891 records from 119 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062647-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ce346q
title: Occurrence Download

description: A dataset listing the 12249 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Bahamas",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia"
 ]
}

 The dataset's 12249 records were derived from 363 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062643-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/kb6wme
title: Replication Data for: Tropical Cyclones Unevenly Shape Drought Propagation

description: Replication Data for: Tropical Cyclones Unevenly Influence Soil Moisture Droughts
DOI: 10.7910/dvn/y0uopd
title: Replication Data for: The Impact of ESG Gaps on Green Exports in Bilateral Trade: A Linear and Nonlinear Analysis

description: Abstract: Drawing on unbalanced panel data with a maximum of 271,656 bilateral trade flow observations from 1996 to 2021, this study investigates both the linear and nonlinear influence of national ESG performance gaps on green exports. When the ESG performance of the exporting country exceeds that of the destination country, the results indicate that an increase in the ESG gap significantly stimulates green exports. And there is evidence that this stimulating effect is achieved by widening green innovation gaps. However, the marginal effect diminishes as environmental regulations in the destination country become more stringent. Conversely, when the exporting country's ESG performance is lower, narrowing the ESG gap leads to an N-shaped relationship with green exports, which remains U-shaped after removing the extremes. This research provides empirical evidence and policy implications for the trade effects of ESG performance from a macro perspective, while supporting the rationality and necessity of the ESG concept.
DOI: 10.15468/dl.9y8thg
title: Occurrence Download

description: A dataset containing 1147 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Crocus biflorus",
 "Country is Italy"
 ]
}

 The dataset includes 1147 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054584-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18710/gdt85x/s1gzhf
title: Exp3_HydrateSat.nc
DOI: 10.18710/gdt85x/leljp8
title: Exp1_Scope_x_time.nc
DOI: 10.18710/gdt85x/pkzlir
title: Exp4_HydrateSat.nc
DOI: 10.18710/gyvnel/d8xbt8
title: 08_Figure-7.jpg

description: The energy savings compared with using a gas electric boiler and the proportion of the utilized waste heat in the total heating load of the studied HTHP systems in different scenarios
DOI: 10.18710/gdt85x/bnqtdr
title: Exp6_Scope_y_voltage.nc
DOI: 10.18710/gyvnel/jlunvm
title: 01_Data.txt

description: COPs and energy savings
DOI: 10.18710/hk6cv6/jkhnut
title: 01_ReadData.py
DOI: 10.18710/gdt85x/hna1ab
title: Exp3_Scope_y_voltage.nc
DOI: 10.18710/gdt85x/pue069
title: Exp8_HydrateSat.nc
DOI: 10.18710/hk6cv6/moeub0
title: M6_fields030.dat
DOI: 10.18710/gdt85x/2lpxl1
title: Exp5_Scope_y_voltage.nc
DOI: 10.18710/gdt85x/e47fb6
title: Exp6_HydrateSat.nc
DOI: 10.18710/gdt85x/fn2sht
title: 00_ReadMe.txt
DOI: 10.18710/gdt85x/rlrkqg
title: Exp1_HydrateSat.nc
DOI: 10.18710/q3iqsm/vuolsk
title: Figure 3.png
DOI: 10.15468/dl.mq57ne
title: Occurrence Download

description: A dataset containing 279 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tityus stigmurus",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 279 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054585-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18710/5f3qgt/rhmike
title: 00_README.txt
DOI: 10.18710/wqwo24/ignur8
title: SIA_environmental_data.txt
DOI: 10.18710/2x6i2s/zauins
title: Figures.R
DOI: 10.18710/kmzbzi/owo4x2
title: 01_ReadMeFile.txt
DOI: 10.18710/2x6i2s/eyaxax
title: waterborne.tab
DOI: 10.18710/4yn9sz/lyrbxp
title: patient_dataset.tar.0005
DOI: 10.18710/4yn9sz/rbqt2l
title: 0_README.txt
DOI: 10.18710/7sngix/fxi0k3
title: Fibre Measurement_metadata_example_case2.csv

description: Second example of metadata used for the Fibre Measurement
DOI: 10.18710/anan9e
title: Replication Data for: Finite element-based diffraction correction for piezoelectric transducers accounting for diffraction at transmission, propagation, and reception

description: &lt;p&gt;This dataset contains modelled diffraction corrections for an example ultrasonic transmit-receive measurement system with piezoelectric transducers, and the underlying raw data used to calculate these diffraction corrections. Three different diffraction correction models are used: the finite element diffraction correction model (FEDC), the simplified finite element diffraction correction model (SFDC) and the baffled piston diffraction correction model (BPDC).&lt;/p&gt;

&lt;p&gt;The example modelling case is an ultrasonic measurement system in air at ambient conditions, with two coaxially aligned cylindrical piezoelectric ceramic disk transducers of the material Pz27 (CTS Ferroperm Piezoceramics, Denmark) with dimensions 20x2 mm. The main output parameters are voltage-to-voltage transfer functions and diffraction corrections, as a function of frequency and axial distance.&lt;/p&gt;

&lt;p&gt;The data was produced as part of a PhD funded by the the Research Council of Norway, under the project "Subsea gas energy and quality measurement using ultrasonic flow meters" with project number 303169.&lt;/p&gt;
description: COMSOL Multiphysics, 5.6
description: Matlab, R2022a
DOI: 10.18710/b7bwxo/jictri
title: LEM-OPX.zip
DOI: 10.18710/hk6cv6/mmbodm
title: M4_fields034.dat