cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2023-12.2976207/2495063
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976208/2495064
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976209/2495065
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976211/2495066
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976213/2495067
title: Data for EMSL Project 51287 from December 2023
DOI: 10.6084/m9.figshare.28029515
title: MCLP_quantum_annealer_V0.5

description: Geospatial optimization problems are fundamental research issues in geographic information science modeling, characterized by high dimensionality, dynamics, and discreteness. These problems often involve inequality-constrained discrete optimization, such as the maximum coverage location problem. Currently, classical high-performance and parallel spatial computing architectures are commonly employed to solve geospatial optimization problems. Under Noisy Intermediate-Scale Quantum (NISQ) conditions, quantum computing has demonstrated computational advantages, such as quantum parallelism, quantum tunneling, and quantum entanglement. The application of quantum computing to solve the maximum coverage location problem presents numerous challenges in areas like problem transformation, quantum sampling solutions, and spatial relationship verification. This paper first proposes the QUBO-MCLP algorithm workflow and designs the Transformation Operator for Inequality Constraints Considering the Capacity of Accessible Providers (TOICCAP), which accounts for the scale of accessible supply points. The paper then addresses the challenge that the scale of slack variables increases significantly as the number of service facilities increases during the TOICCAP transformation process. To mitigate this, an improved Logarithm Transformation Operator for Inequality Constraints Considering the Capacity of Accessible Providers (LTOICCAP) is introduced. Finally, for spatial relationship verification, a Spatial Coverage Consistency Checking Operator for MCLP Results (SCCCOMR) is designed. Theoretical and applied experiments are conducted using four solvers: QBSolv, D-Wave Hybrid binary quadratic model 2, D-Wave Advantage system 4.1, and Gurobi. These experiments involve location optimization calculations and result analysis on the TGD and SJC datasets. The results demonstrate the effectiveness of the QUBO-MCLP algorithm workflow, with TOICCAP successfully converting slack variables in inequality-constrained functions for p&gt;2, and LTOICCAP effectively reducing the number of linear and quadratic variables. This paper presents a quantum computing path for Transformation-to-Sampling-to-Verification of geospatial optimization problems, adaptable to the controlled qubit scale and coherence constraints under current NISQ conditions. This approach is applicable to urban service facility planning, Internet of Things (IoT) and communication facility spatial deployment, smart city governance, and resource allocation.
DOI: 10.6084/m9.figshare.28029515.v3
title: MCLP_quantum_annealer_V0.5

description: Geospatial optimization problems are fundamental research issues in geographic information science modeling, characterized by high dimensionality, dynamics, and discreteness. These problems often involve inequality-constrained discrete optimization, such as the maximum coverage location problem. Currently, classical high-performance and parallel spatial computing architectures are commonly employed to solve geospatial optimization problems. Under Noisy Intermediate-Scale Quantum (NISQ) conditions, quantum computing has demonstrated computational advantages, such as quantum parallelism, quantum tunneling, and quantum entanglement. The application of quantum computing to solve the maximum coverage location problem presents numerous challenges in areas like problem transformation, quantum sampling solutions, and spatial relationship verification. This paper first proposes the QUBO-MCLP algorithm workflow and designs the Transformation Operator for Inequality Constraints Considering the Capacity of Accessible Providers (TOICCAP), which accounts for the scale of accessible supply points. The paper then addresses the challenge that the scale of slack variables increases significantly as the number of service facilities increases during the TOICCAP transformation process. To mitigate this, an improved Logarithm Transformation Operator for Inequality Constraints Considering the Capacity of Accessible Providers (LTOICCAP) is introduced. Finally, for spatial relationship verification, a Spatial Coverage Consistency Checking Operator for MCLP Results (SCCCOMR) is designed. Theoretical and applied experiments are conducted using four solvers: QBSolv, D-Wave Hybrid binary quadratic model 2, D-Wave Advantage system 4.1, and Gurobi. These experiments involve location optimization calculations and result analysis on the TGD and SJC datasets. The results demonstrate the effectiveness of the QUBO-MCLP algorithm workflow, with TOICCAP successfully converting slack variables in inequality-constrained functions for p&gt;2, and LTOICCAP effectively reducing the number of linear and quadratic variables. This paper presents a quantum computing path for Transformation-to-Sampling-to-Verification of geospatial optimization problems, adaptable to the controlled qubit scale and coherence constraints under current NISQ conditions. This approach is applicable to urban service facility planning, Internet of Things (IoT) and communication facility spatial deployment, smart city governance, and resource allocation.
DOI: 10.6084/m9.figshare.28029515.v2
title: MCLP_quantum_annealer_V0.5

description: Geospatial optimization problems are fundamental research issues in geographic information science modeling, characterized by high dimensionality, dynamics, and discreteness. These problems often involve inequality-constrained discrete optimization, such as the maximum coverage location problem. Currently, classical high-performance and parallel spatial computing architectures are commonly employed to solve geospatial optimization problems. Under Noisy Intermediate-Scale Quantum (NISQ) conditions, quantum computing has demonstrated computational advantages, such as quantum parallelism, quantum tunneling, and quantum entanglement. The application of quantum computing to solve the maximum coverage location problem presents numerous challenges in areas like problem transformation, quantum sampling solutions, and spatial relationship verification. This paper first proposes the QUBO-MCLP algorithm workflow and designs the Transformation Operator for Inequality Constraints Considering the Capacity of Accessible Providers (TOICCAP), which accounts for the scale of accessible supply points. The paper then addresses the challenge that the scale of slack variables increases significantly as the number of service facilities increases during the TOICCAP transformation process. To mitigate this, an improved Logarithm Transformation Operator for Inequality Constraints Considering the Capacity of Accessible Providers (LTOICCAP) is introduced. Finally, for spatial relationship verification, a Spatial Coverage Consistency Checking Operator for MCLP Results (SCCCOMR) is designed. Theoretical and applied experiments are conducted using four solvers: QBSolv, D-Wave Hybrid binary quadratic model 2, D-Wave Advantage system 4.1, and Gurobi. These experiments involve location optimization calculations and result analysis on the TGD and SJC datasets. The results demonstrate the effectiveness of the QUBO-MCLP algorithm workflow, with TOICCAP successfully converting slack variables in inequality-constrained functions for p&gt;2, and LTOICCAP effectively reducing the number of linear and quadratic variables. This paper presents a quantum computing path for Transformation-to-Sampling-to-Verification of geospatial optimization problems, adaptable to the controlled qubit scale and coherence constraints under current NISQ conditions. This approach is applicable to urban service facility planning, Internet of Things (IoT) and communication facility spatial deployment, smart city governance, and resource allocation.
DOI: 10.25582/data.2023-12.2976214/2495068
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976215/2495069
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976350/2495070
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976351/2495071
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976352/2495072
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976353/2495073
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976354/2495074
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976355/2495075
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976356/2495076
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976357/2495077
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976358/2495078
title: Data for EMSL Project 51287 from December 2023
DOI: 10.15468/dl.pr4tbf
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Engraulis encrasicolus (Linnaeus, 1758)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057521-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2023-12.2976359/2495079
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976360/2495080
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976361/2495081
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976362/2495082
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976363/2495083
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976364/2495084
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976365/2495085
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976366/2495086
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976367/2495087
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976368/2495088
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976369/2495089
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976370/2495090
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976371/2495091
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976372/2495092
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976373/2495093
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976374/2495094
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976375/2495095
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2023-12.2976376/2495096
title: Data for EMSL Project 51287 from December 2023
DOI: 10.15468/dl.8ubyus
title: Occurrence Download

description: A dataset containing 3113 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Opuntia monacantha Haw."
 ]
}

 The dataset includes 3113 records from 154 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057522-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2023-12.2976504/2495097
title: Data for EMSL Project 51287 from December 2023
DOI: 10.25582/data.2024-01.3000972/2495098
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3000973/2495099
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3000974/2495100
title: Data for EMSL Project 51287 from January 2024
DOI: 10.15468/dl.kvn2mk
title: Occurrence Download

description: A dataset containing 79 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Katsuwonus pelamis (Linnaeus, 1758)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 79 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057523-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-01.3000976/2495101
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3000977/2495102
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3000978/2495103
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3000979/2495104
title: Data for EMSL Project 51287 from January 2024
DOI: 10.15468/dl.ynnrbf
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Costa Rica",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Holacanthus passer Valenciennes, 1846",
 "Year 1000-2025"
 ]
}

 The dataset's 1 records were derived from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057517-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-01.3000980/2495105
title: Data for EMSL Project 51287 from January 2024
DOI: 10.15468/dl.888dyn
title: Occurrence Download

description: A dataset containing 134 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Corydalis leptocarpa Hook.fil. & Thomson"
 ]
}

 The dataset includes 134 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057524-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-01.3000982/2495106
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3000984/2495107
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3000989/2495108
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3000994/2495109
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3000996/2495110
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3000997/2495111
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3000999/2495112
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001002/2495113
title: Data for EMSL Project 51287 from January 2024
DOI: 10.15468/dl.zkh4gg
title: Occurrence Download

description: A dataset containing 34 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Abelia forrestii (Diels) W.W.Sm.)",
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true"
 ]
}

 The dataset includes 34 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057525-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-01.3001003/2495114
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001004/2495115
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001005/2495116
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001006/2495117
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001007/2495118
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001008/2495119
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001009/2495120
title: Data for EMSL Project 51287 from January 2024
DOI: 10.15468/dl.kbye7t
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Scomber pelamis Brünnich, 1768",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057526-241126133413365/datasets/export for details.
DOI: 10.25582/data.2024-01.3001010/2495121
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001011/2495122
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001012/2495123
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001013/2495124
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001017/2495125
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001018/2495126
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001019/2495127
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001020/2495128
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001022/2495129
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001023/2495130
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001024/2495131
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001025/2495132
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001027/2495133
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001028/2495134
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001029/2495135
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001030/2495136
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001031/2495137
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001033/2495138
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001034/2495139
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001035/2495140
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001036/2495141
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001037/2495142
title: Data for EMSL Project 51287 from January 2024
DOI: 10.15468/dl.443ej4
title: Occurrence Download

description: A dataset containing 242 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Corydalis linarioides Maxim."
 ]
}

 The dataset includes 242 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057527-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-01.3001038/2495143
title: Data for EMSL Project 51287 from January 2024
DOI: 10.15468/dl.pq659e
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Capsala katsuwoni (Ishii, 1936) Price, 1938",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057528-241126133413365/datasets/export for details.
DOI: 10.25582/data.2024-01.3001051/2495144
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001052/2495145
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001055/2495146
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001057/2495147
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001058/2495148
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001061/2495149
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001062/2495150
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001065/2495151
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001066/2495152
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001067/2495153
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001071/2495154
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001072/2495155
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001075/2495156
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001076/2495157
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001081/2495158
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001083/2495159
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001084/2495160
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001085/2495161
title: Data for EMSL Project 51287 from January 2024
DOI: 10.15468/dl.pns7kt
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Caballerocotyla llewelyni Kohn & Justo, 2006",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057529-241126133413365/datasets/export for details.
DOI: 10.25582/data.2024-01.3001087/2495162
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001088/2495163
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001089/2495164
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001090/2495165
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001091/2495166
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001094/2495167
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001096/2495168
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001097/2495169
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001098/2495170
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001099/2495171
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001100/2495172
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001103/2495173
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001105/2495174
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001106/2495175
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001107/2495176
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001108/2495177
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001114/2495178
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001115/2495179
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3001117/2495180
title: Data for EMSL Project 51287 from January 2024