cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.pf887h
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Sinobambusa intermedia"
 ]
}

 The dataset includes 23 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005703-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/yky98xv8rm.2
title: Document Supplementary Tables

description: Supplementary materials of manuscript
DOI: 10.17632/fm9myphk9g
title: BIM Spatial Models for Construction Dependency Inference and Scheduling Optimization

description: This dataset contains three Building Information Modeling (BIM) projects in IFC format, along with their corresponding processed semantic data used for spatial reasoning, dependency inference, and scheduling research. Each BIM model folder includes both the original IFC file and several CSV files that provide structured geometric, spatial, and semantic information extracted from the IFC model.The dataset consists of:
1. BIM_meeting_room_data
A small-scale architectural BIM model representing a typical meeting room.
Included files:
BIM_meeting_room.ifc – the original IFC model
ifc_family_data.csv – component family/type attributes
ifc_space_centroids_data.csv – centroid coordinates of spatial units
ifc_space_data.csv – geometric and semantic space-level attributes
lowest_points.csv – elevation and placement reference points
spatial_relationships_detailed.csv – detailed adjacency, containment, and interaction relationships
2. BIM_residential_building_data
A medium-scale multi-room residential building model.
Included files:
BIM_residential_building.ifc – the original IFC model
ifc_family_data.csv – families and object classification
ifc_space_centroids_data.csv – geometric centers of all rooms
ifc_space_data.csv – IFC-derived geometric features
lowest_points.csv – base elevation and alignment information
spatial_relationships_detailed.csv – full spatial interaction matrix
3. rac_advanced_sample_project_data
A large and complex BIM project commonly used in industrial design workflows.
Included files:
rac_advanced_sample_project.ifc – the complete IFC model
ifc_family_data.csv – component types and metadata
ifc_space_centroids_data.csv – centroid positions for rooms and areas
ifc_space_data.csv – extended geometric/semantic features
lowest_points.csv – elevation anchors
spatial_relationships_detailed.csv – multi-type spatial relationship details
This dataset supports research in:
BIM-based spatial reasoning
Graph neural network (GNN) dependency inference
Construction scheduling optimization
Automated semantic enrichment
IFC-to-graph conversion workflows
All models and CSV files in this dataset were used to evaluate the proposed TSG–GNN–DLSA scheduling framework in the associated publication.
DOI: 10.17632/fm9myphk9g.1
title: BIM Spatial Models for Construction Dependency Inference and Scheduling Optimization

description: This dataset contains three Building Information Modeling (BIM) projects in IFC format, along with their corresponding processed semantic data used for spatial reasoning, dependency inference, and scheduling research. Each BIM model folder includes both the original IFC file and several CSV files that provide structured geometric, spatial, and semantic information extracted from the IFC model.The dataset consists of:
1. BIM_meeting_room_data
A small-scale architectural BIM model representing a typical meeting room.
Included files:
BIM_meeting_room.ifc – the original IFC model
ifc_family_data.csv – component family/type attributes
ifc_space_centroids_data.csv – centroid coordinates of spatial units
ifc_space_data.csv – geometric and semantic space-level attributes
lowest_points.csv – elevation and placement reference points
spatial_relationships_detailed.csv – detailed adjacency, containment, and interaction relationships
2. BIM_residential_building_data
A medium-scale multi-room residential building model.
Included files:
BIM_residential_building.ifc – the original IFC model
ifc_family_data.csv – families and object classification
ifc_space_centroids_data.csv – geometric centers of all rooms
ifc_space_data.csv – IFC-derived geometric features
lowest_points.csv – base elevation and alignment information
spatial_relationships_detailed.csv – full spatial interaction matrix
3. rac_advanced_sample_project_data
A large and complex BIM project commonly used in industrial design workflows.
Included files:
rac_advanced_sample_project.ifc – the complete IFC model
ifc_family_data.csv – component types and metadata
ifc_space_centroids_data.csv – centroid positions for rooms and areas
ifc_space_data.csv – extended geometric/semantic features
lowest_points.csv – elevation anchors
spatial_relationships_detailed.csv – multi-type spatial relationship details
This dataset supports research in:
BIM-based spatial reasoning
Graph neural network (GNN) dependency inference
Construction scheduling optimization
Automated semantic enrichment
IFC-to-graph conversion workflows
All models and CSV files in this dataset were used to evaluate the proposed TSG–GNN–DLSA scheduling framework in the associated publication.
DOI: 10.17632/vrsvmzhx27
title: Molecular Dynamics Study on the Migration and Segregation Behavior of Re in W

description: The EAM pot of WReHe.
DOI: 10.17632/vrsvmzhx27.1
title: Molecular Dynamics Study on the Migration and Segregation Behavior of Re in W

description: The EAM pot of WReHe.
DOI: 10.17632/yc9g38kh5w
title: green procurement and sustainability

description: 1. Sustainability and Green Sourcing
a. Explain what it means by Sustainability and Green Sourcing.
b. Why it is important to implement sustainability and green sourcing. 
c. Issues and challenges faced in adopting sustainability and green sourcing. 
d. Best practices used in implementing sustainability and green sourcing
DOI: 10.17632/yc9g38kh5w.1
title: green procurement and sustainability

description: 1. Sustainability and Green Sourcing
a. Explain what it means by Sustainability and Green Sourcing.
b. Why it is important to implement sustainability and green sourcing. 
c. Issues and challenges faced in adopting sustainability and green sourcing. 
d. Best practices used in implementing sustainability and green sourcing
DOI: 10.17632/vzsg6rrscp
title: SD-IoT MQTT/CoAP DDoS Dataset

description: The dataset comprises Mininet simulations of Distributed Denial of Service (DDoS) and standard traffic, utilizing public-domain IP addresses. These simulations were gathered from four distinct Internet of Things (IoT) networks managed by L3 Forwarding application, where the IoT servers facilitate MQTT and CoAP services. The DDoS attacks were executed using Tcpreplay on a Packet Capture (PCAP) file, and the dataset was compiled through a single Software-Defined IoT (SD-IoT) controller (RYU) in accordance with the OpenFlow standard. This process included the extraction of PacketIn header information as well as port statistics data. The dataset encompasses 26 features and 10 labels, concentrating specifically on MQTT services (CONNECT, SUBSCRIBE, and PUBLISH) and CoAP services (GET and POST).
DOI: 10.17632/vzsg6rrscp.1
title: SD-IoT MQTT/CoAP DDoS Dataset

description: The dataset comprises Mininet simulations of Distributed Denial of Service (DDoS) and standard traffic, utilizing public-domain IP addresses. These simulations were gathered from four distinct Internet of Things (IoT) networks managed by L3 Forwarding application, where the IoT servers facilitate MQTT and CoAP services. The DDoS attacks were executed using Tcpreplay on a Packet Capture (PCAP) file, and the dataset was compiled through a single Software-Defined IoT (SD-IoT) controller (RYU) in accordance with the OpenFlow standard. This process included the extraction of PacketIn header information as well as port statistics data. The dataset encompasses 26 features and 10 labels, concentrating specifically on MQTT services (CONNECT, SUBSCRIBE, and PUBLISH) and CoAP services (GET and POST).
DOI: 10.5281/zenodo.17699910
title: Raster map (resolution 25x25m) of agricultural landuse in the Netherlands based on  LGN2024.

description: Units: grasland=1, arable land=2
DOI: 10.5281/zenodo.17699911
title: Raster map (resolution 25x25m) of agricultural landuse in the Netherlands based on  LGN2024.

description: Units: grasland=1, arable land=2
DOI: 10.17632/mz4dcwmnwg
title: Ubiquitination and degradation of CD47 enhances macrophage phagocytosis of hemolytic erythrocytes

description: Original western blot images of the article of Ubiquitination and degradation of CD47 enhances macrophage phagocytosis of hemolytic erythrocytes
DOI: 10.17632/mz4dcwmnwg.1
title: Ubiquitination and degradation of CD47 enhances macrophage phagocytosis of hemolytic erythrocytes

description: Original western blot images of the article of Ubiquitination and degradation of CD47 enhances macrophage phagocytosis of hemolytic erythrocytes
DOI: 10.17632/xvgvhchwsm
title: Work Meaning and Fair Wages

description: This replication package contains the code to replicate the results Work Meaning and Fair Wages by T. De Schouwer, E. Gsottbauer, I. Kesternich, and H. Schumacher, published in Labour Economics 97 (2025). The file main.R runs all the code to clean the data and construct the figures and tables for the paper.
DOI: 10.17632/xvgvhchwsm.1
title: Work Meaning and Fair Wages

description: This replication package contains the code to replicate the results Work Meaning and Fair Wages by T. De Schouwer, E. Gsottbauer, I. Kesternich, and H. Schumacher, published in Labour Economics 97 (2025). The file main.R runs all the code to clean the data and construct the figures and tables for the paper.
DOI: 10.17632/rmg7zhfxpy
title: Dataset for GCA-D-25-00733

description: Ce anomaly and elemental ratios in the leachate fraction of carbonates from OAE 2 sections
DOI: 10.17632/rmg7zhfxpy.1
title: Dataset for GCA-D-25-00733

description: Ce anomaly and elemental ratios in the leachate fraction of carbonates from OAE 2 sections
DOI: 10.17632/9jzgts58xx
title: Data

description: Repellent data
DOI: 10.17632/9jzgts58xx.1
title: Data

description: Repellent data
DOI: 10.17632/mshf46sd88
title: Data for"Association Between Preoperative Sleep Disorders and Chronic Postoperative Pain in Hysterectomy Patients: A Prospective Cohort Study"

description: This dataset contains individual-level data from a prospective cohort study investigating the association between preoperative sleep disorders and chronic postsurgical pain. A total of 275 patients were stratified into two groups based on preoperative sleep quality scores: a no sleep disorders group （n=145）and a sleep disorders group（n=130）, with subsequent follow-up evaluations conducted to assess pain outcomes.

This dataset originates from a prospective cohort study that enrolled 275 women aged 18 to 65 years, scheduled for elective hysterectomy at a single center, in accordance with predefined inclusion and exclusion criteria. Data1 includes preoperative information such as baseline characteristics (sex, age, body mass index, underlying diseases, and surgical history), pain intensity (Numeric Rating Scale, NRS) and sleep quality (Pittsburgh Sleep Quality Index, PSQI), as well as postoperative acute pain scores, chronic pain assessments, and analgesic usage. Data2 contains detailed PSQI component scores related to sleep quality. Data3 comprises perioperative hemodynamic measurements of the patients.

All data have been fully de-identified: only study identification numbers and group labels are provided, with no personal identifiers such as names, dates of birth, or contact information included. All variables, units, and coding schemes are clearly annotated to facilitate data reuse and replication of analyses reported in the associated publication.
DOI: 10.17632/mshf46sd88.1
title: Data for"Association Between Preoperative Sleep Disorders and Chronic Postoperative Pain in Hysterectomy Patients: A Prospective Cohort Study"

description: This dataset contains individual-level data from a prospective cohort study investigating the association between preoperative sleep disorders and chronic postsurgical pain. A total of 275 patients were stratified into two groups based on preoperative sleep quality scores: a no sleep disorders group （n=145）and a sleep disorders group（n=130）, with subsequent follow-up evaluations conducted to assess pain outcomes.

This dataset originates from a prospective cohort study that enrolled 275 women aged 18 to 65 years, scheduled for elective hysterectomy at a single center, in accordance with predefined inclusion and exclusion criteria. Data1 includes preoperative information such as baseline characteristics (sex, age, body mass index, underlying diseases, and surgical history), pain intensity (Numeric Rating Scale, NRS) and sleep quality (Pittsburgh Sleep Quality Index, PSQI), as well as postoperative acute pain scores, chronic pain assessments, and analgesic usage. Data2 contains detailed PSQI component scores related to sleep quality. Data3 comprises perioperative hemodynamic measurements of the patients.

All data have been fully de-identified: only study identification numbers and group labels are provided, with no personal identifiers such as names, dates of birth, or contact information included. All variables, units, and coding schemes are clearly annotated to facilitate data reuse and replication of analyses reported in the associated publication.
DOI: 10.17632/ch5dnspd79
title: AutoSpectral: Full Workflow Example

description: Mouse tissue samples from spleen, lung and liver. Stained with key cell type-defining markers with well separated fluorophores to identify and measure spillover artefacts associated with autofluorescence.
DOI: 10.17632/ch5dnspd79.1
title: AutoSpectral: Full Workflow Example

description: Mouse tissue samples from spleen, lung and liver. Stained with key cell type-defining markers with well separated fluorophores to identify and measure spillover artefacts associated with autofluorescence.
DOI: 10.17632/g53ysmc4k6
title: Concrete and SHAP

description: Concrete
DOI: 10.17632/g53ysmc4k6.1
title: Concrete and SHAP

description: Concrete
DOI: 10.17632/x2h8m9p5m4
title: Differentiation of Mesenchymal Stem/Stromal Cells into CD45+ Macrophage-like Cells: Expanding Insights into MSC Plasticity

description: Single-Cell RNA-Seq supports transition of PαS cells into macrophage and fibrocyte-like
lineages.
To further investigate these results, single-cell RNA-Seq (scRNA-seq) was performed with freshly
isolated PαS cells, which were cultured for 1 week in DMEM or RM. First, the t-distributed stochastic
neighbor embedding was investigated for the positive isolation markers PDGFRα (denoted as
PDGFRα) and Ly6a (Sca-1) Ptprc, Cd45 (PTPRCATXN1) (Figure 4A). Although both samples
displayed a similar distribution, only the RM-cultured sample clearly contained CdD45 (PTPRC)-
expressing cells.
Next, the cells were clustered into 10 clusters using the Louvain Clustering of BBrowser (Figure 4B).
However, owing to the distinct location and different levels of key marker expression, cluster 6 was
manually subdivided into clusters 6 and cluster 6a (Figure 4B, and 4C). The isolation markers Sca-1
(denoted as LY6A) (LY6A), PDGFRα (PDGFRA), CdD73 (NT5E), CdD45 (PTPRC), as well as CdD68
(CD68), CdD11b (ITGAM), CsfSF1r (CSF1R)R, CdD34 (CD34), and Ccollagen 1 (COL1A1) were
blotted in a bubble heatmap (Figure 4C). Clusters 4, 5, and 6a–10 all highly expressed Sca-1 and
PDGFRα, whereas clusters 1, 2, and 3 expressed low levels of Sca-1 and PDGFRα, while cluster 6
did not express PDGFRα and expressed low levels of Sca-1. CdD73 was only notably expressed in
clusters 3 and 4. Cluster 3 was the only cluster with high CdD34 expression. While devoid in the
other clusters, clusters 6 and 6a both expressed high levels of Csf1rCSF1R, CdD11b, CdD68, and
CdD45. Notably, Ccollagen 1 expression was high in all clusters except for cluster 6, which was
approximately half of that in the others (Figure 4C).
Based on these markers and co-expression within clusters, cells were divided into MSCs (Figure 4E,
blue, clusters 5, 7–10) based on Sca-1 and PDGFRα expression; adipocyte progenitors (yellow,
cluster 4) based on CdD73 expression; fibroblast progenitors (green, cluster 3) based on CdD73 and
CdD34 expression; macrophages (red, cluster 6) based on CdD45, CdD68, CdD11b, and Csf1CSF1R
expression; and fibrocyte-like macrophages (violet, cluster 6a) based on the low expression of
macrophage markers and high expression of collagen I. Both cluster (macrophages and fibrocyte-like

macrophages) clearly express macrophage relevant genes such as CdD14 (CD14), CdD48 (CD48),
CdD53 (CD53), Stab1 (STAB1), Ccl9 (CCL9), and CdD206 (MRC1), and lose the characteristics of
MSCs (Figure 4F). These data strongly suggest that freshly isolated PαS cells contain heterogeneous
subpopulations, including macrophages and fibrocyte-like macrophages, that emerge in response to
M-CSF stimulation.
DOI: 10.5517/ccdc.csd.cc2p95z5
title: CCDC 2481487: Experimental Crystal Structure Determination

description: Related Article: Krystyna A. Deresz, Artem Mikhailov, Joanna Jankowska, Lorenzo Donà, Bartolomeo Civalleri, Adam Krówczyński, Radosław Kamiński, Dominik Schaniel, Katarzyna N. Jarzembska|2025|ACS Omega|10|51940|doi:10.1021/acsomega.5c08267
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.17632/x2h8m9p5m4.1
title: Differentiation of Mesenchymal Stem/Stromal Cells into CD45+ Macrophage-like Cells: Expanding Insights into MSC Plasticity

description: Single-Cell RNA-Seq supports transition of PαS cells into macrophage and fibrocyte-like
lineages.
To further investigate these results, single-cell RNA-Seq (scRNA-seq) was performed with freshly
isolated PαS cells, which were cultured for 1 week in DMEM or RM. First, the t-distributed stochastic
neighbor embedding was investigated for the positive isolation markers PDGFRα (denoted as
PDGFRα) and Ly6a (Sca-1) Ptprc, Cd45 (PTPRCATXN1) (Figure 4A). Although both samples
displayed a similar distribution, only the RM-cultured sample clearly contained CdD45 (PTPRC)-
expressing cells.
Next, the cells were clustered into 10 clusters using the Louvain Clustering of BBrowser (Figure 4B).
However, owing to the distinct location and different levels of key marker expression, cluster 6 was
manually subdivided into clusters 6 and cluster 6a (Figure 4B, and 4C). The isolation markers Sca-1
(denoted as LY6A) (LY6A), PDGFRα (PDGFRA), CdD73 (NT5E), CdD45 (PTPRC), as well as CdD68
(CD68), CdD11b (ITGAM), CsfSF1r (CSF1R)R, CdD34 (CD34), and Ccollagen 1 (COL1A1) were
blotted in a bubble heatmap (Figure 4C). Clusters 4, 5, and 6a–10 all highly expressed Sca-1 and
PDGFRα, whereas clusters 1, 2, and 3 expressed low levels of Sca-1 and PDGFRα, while cluster 6
did not express PDGFRα and expressed low levels of Sca-1. CdD73 was only notably expressed in
clusters 3 and 4. Cluster 3 was the only cluster with high CdD34 expression. While devoid in the
other clusters, clusters 6 and 6a both expressed high levels of Csf1rCSF1R, CdD11b, CdD68, and
CdD45. Notably, Ccollagen 1 expression was high in all clusters except for cluster 6, which was
approximately half of that in the others (Figure 4C).
Based on these markers and co-expression within clusters, cells were divided into MSCs (Figure 4E,
blue, clusters 5, 7–10) based on Sca-1 and PDGFRα expression; adipocyte progenitors (yellow,
cluster 4) based on CdD73 expression; fibroblast progenitors (green, cluster 3) based on CdD73 and
CdD34 expression; macrophages (red, cluster 6) based on CdD45, CdD68, CdD11b, and Csf1CSF1R
expression; and fibrocyte-like macrophages (violet, cluster 6a) based on the low expression of
macrophage markers and high expression of collagen I. Both cluster (macrophages and fibrocyte-like

macrophages) clearly express macrophage relevant genes such as CdD14 (CD14), CdD48 (CD48),
CdD53 (CD53), Stab1 (STAB1), Ccl9 (CCL9), and CdD206 (MRC1), and lose the characteristics of
MSCs (Figure 4F). These data strongly suggest that freshly isolated PαS cells contain heterogeneous
subpopulations, including macrophages and fibrocyte-like macrophages, that emerge in response to
M-CSF stimulation.
DOI: 10.5517/ccdc.csd.cc2p9629
title: CCDC 2481490: Experimental Crystal Structure Determination

description: Related Article: Krystyna A. Deresz, Artem Mikhailov, Joanna Jankowska, Lorenzo Donà, Bartolomeo Civalleri, Adam Krówczyński, Radosław Kamiński, Dominik Schaniel, Katarzyna N. Jarzembska|2025|ACS Omega|10|51940|doi:10.1021/acsomega.5c08267
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2p963b
title: CCDC 2481491: Experimental Crystal Structure Determination

description: Related Article: Krystyna A. Deresz, Artem Mikhailov, Joanna Jankowska, Lorenzo Donà, Bartolomeo Civalleri, Adam Krówczyński, Radosław Kamiński, Dominik Schaniel, Katarzyna N. Jarzembska|2025|ACS Omega|10|51940|doi:10.1021/acsomega.5c08267
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2p964c
title: CCDC 2481492: Experimental Crystal Structure Determination

description: Related Article: Krystyna A. Deresz, Artem Mikhailov, Joanna Jankowska, Lorenzo Donà, Bartolomeo Civalleri, Adam Krówczyński, Radosław Kamiński, Dominik Schaniel, Katarzyna N. Jarzembska|2025|ACS Omega|10|51940|doi:10.1021/acsomega.5c08267
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2p965d
title: CCDC 2481493: Experimental Crystal Structure Determination

description: Related Article: Krystyna A. Deresz, Artem Mikhailov, Joanna Jankowska, Lorenzo Donà, Bartolomeo Civalleri, Adam Krówczyński, Radosław Kamiński, Dominik Schaniel, Katarzyna N. Jarzembska|2025|ACS Omega|10|51940|doi:10.1021/acsomega.5c08267
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2pbll7
title: CCDC 2482839: Experimental Crystal Structure Determination

description: Related Article: David Vesseur, Shuo Li, Sonia Mallet-Ladeira, Didier Bourissou|2025|Chem.Commun.|||doi:10.1039/d5cc05744b
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.