cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30492857.v2
title: Supervised Predictive Modeling of High-Dimensional Data with Group ℓ<sup>0</sup>-Norm Constrained Neural Networks

description: To flexibly handle high-dimensional data, this paper develops a new sparsity-constrained optimization approach. By leveraging group ℓ0-norm constrained neural networks, the proposed approach aims to simultaneously extract crucial features and estimate the underlying model function with statistically guaranteed accuracy. Under some mild conditions, we establish statistical theories of the proposed method for a general class of nonparametric regression-type loss functions. Moreover, two iterative greedy selection algorithms, which iterate between a standard gradient descent step and a hard thresholding step with or without debiasing, are presented to implement the computation. Convergence guarantees and sparsity recovery capabilities of these algorithms are rigorously examined. Empirical validation through a series of comprehensive experiments conducted on both real-world and synthetic datasets underscores the superior performance of our proposed estimator. In various scenarios, the proposed approach surpasses conventional Lasso-based sparse learning methods in terms of variable selection accuracy and prediction performance, thereby highlighting its efficacy in practical applications.
DOI: 10.5281/zenodo.17630132
title: An individual-based model to quantify the non-breeding season impact of wind farms on seabirds

description: 1. Many countries are developing offshore wind farms to provide renewable energy, yet such developments can harm biodiversity. Seabirds are a highly threatened group of birds and can be impacted by wind farms through lethal collisions and via sub-lethal displacement effects. However, we do not have a comprehensive understanding of the impacts of offshore wind farms on seabird populations, particularly outside of the breeding season.

2. We developed an individual-based model to predict the non-breeding season impacts of offshore wind farms on seabirds. We used long-term tracking data obtained from geolocation-immersion loggers to estimate population-level distributions and activity budgets. We simulated individual behaviour, movement, wind farm interactions (collision and displacement), and any resulting lethal or sub-lethal effects.

3. We demonstrated our model by assessing the impact of ten simulated offshore wind farms on two populations that breed in Norway: common guillemots Uria aalge (Sklinna) and black-legged kittiwakes Rissa tridactyla (Ålesund). We quantified collision risk in kittiwakes and sub-lethal displacement effects in guillemots and converted these effects into a change in survival or end of season body mass, as a proxy for condition. 

4. We predicted that 49.6% of guillemots breeding at Sklinna would experience displacement effects during the non-breeding season. As the energetic impact of displacement is relatively unknown, we modelled a range of possible displacement costs and present several impact scenarios, with adult mortality levels ranging from 0-5.32% and end of season body masses of 97.12-99.84% compared to those resulting from an unimpacted scenario. Despite 98.9% of kittiwakes flying through at least one wind farm footprint, we only predicted collisions in 0.055% of the population; this low mortality was primarily driven by low overlap between the modelled height of the turbine rotors and the probable flight height of kittiwakes.

5. Practical implications. Our model provides a tool that can be used to assess the non-breeding season impacts of OWFs on seabird populations, improving sustainability when developing renewable energy infrastructure. We highlight several key limitations as areas of research that are required to reduce uncertainty when predicting impacts. Our model is reproducible and adaptable for use on other species or for other marine threats.
DOI: 10.5281/zenodo.17630133
title: An individual-based model to quantify the non-breeding season impact of wind farms on seabirds

description: 1. Many countries are developing offshore wind farms to provide renewable energy, yet such developments can harm biodiversity. Seabirds are a highly threatened group of birds and can be impacted by wind farms through lethal collisions and via sub-lethal displacement effects. However, we do not have a comprehensive understanding of the impacts of offshore wind farms on seabird populations, particularly outside of the breeding season.

2. We developed an individual-based model to predict the non-breeding season impacts of offshore wind farms on seabirds. We used long-term tracking data obtained from geolocation-immersion loggers to estimate population-level distributions and activity budgets. We simulated individual behaviour, movement, wind farm interactions (collision and displacement), and any resulting lethal or sub-lethal effects.

3. We demonstrated our model by assessing the impact of ten simulated offshore wind farms on two populations that breed in Norway: common guillemots Uria aalge (Sklinna) and black-legged kittiwakes Rissa tridactyla (Ålesund). We quantified collision risk in kittiwakes and sub-lethal displacement effects in guillemots and converted these effects into a change in survival or end of season body mass, as a proxy for condition. 

4. We predicted that 49.6% of guillemots breeding at Sklinna would experience displacement effects during the non-breeding season. As the energetic impact of displacement is relatively unknown, we modelled a range of possible displacement costs and present several impact scenarios, with adult mortality levels ranging from 0-5.32% and end of season body masses of 97.12-99.84% compared to those resulting from an unimpacted scenario. Despite 98.9% of kittiwakes flying through at least one wind farm footprint, we only predicted collisions in 0.055% of the population; this low mortality was primarily driven by low overlap between the modelled height of the turbine rotors and the probable flight height of kittiwakes.

5. Practical implications. Our model provides a tool that can be used to assess the non-breeding season impacts of OWFs on seabird populations, improving sustainability when developing renewable energy infrastructure. We highlight several key limitations as areas of research that are required to reduce uncertainty when predicting impacts. Our model is reproducible and adaptable for use on other species or for other marine threats.
DOI: 10.6084/m9.figshare.30492857.v1
title: Supervised Predictive Modeling of High-dimensional Data with Group l0-norm Constrained Neural Networks

description: To flexibly handle high-dimensional data, this paper develops a new sparsity-constrained optimization approach. By leveraging group l0-norm constrained neural networks, the proposed approach aims to simultaneously extract crucial features and estimate the underlying model function with statistically guaranteed accuracy. Under some mild conditions, we establish statistical theories of the proposed method for a general class of nonparametric regression-type loss functions. Moreover, two iterative greedy selection algorithms, which iterate between a standard gradient descent step and a hard thresholding step with or without debiasing, are presented to implement the computation. Convergence guarantees and sparsity recovery capabilities of these algorithms are rigorously examined. Empirical validation through a series of comprehensive experiments conducted on both real-world and synthetic datasets underscores the superior performance of our proposed estimator. In various scenarios, the proposed approach surpasses conventional Lasso-based sparse learning methods in terms of variable selection accuracy and prediction performance, thereby highlighting its efficacy in practical applications.
DOI: 10.6084/m9.figshare.30369334
title: Variational Estimation for Multidimensional Graded Response Model

description: Likert-type items with ordinal responses are frequently used in tests to assess multiple latent traits. The multidimensional graded response model (MGRM) is the preferred model for describing the relationship between these ordinal items and latent traits. In this article, we propose a novel Gaussian variational expectation maximization (GVEM) method for parameter estimation in MGRM. Rather than relying on direct numerical approximations for intractable integrals over multidimensional latent traits, our GVEM employs a carefully derived variational lower bound to approximate the marginal log-likelihood function, resulting in closed-form estimates. This method significantly improves the computational efficiency and is viable when dealing with high-dimensional latent variables. Additionally, an importance-weighted GVEM (IW-GVEM) algorithm is developed for MGRM to address the bias issue. Simulation studies show that our GVEM and IW-GVEM run significantly faster than the MH-RM algorithm and are of competitiveness in both confirmatory and exploratory analysis. Our proposed algorithms are illustrated by analyzing a real dataset from the Big-Five Personality test. Supplemental materials for the article are available online.
DOI: 10.6084/m9.figshare.30369334.v1
title: Variational Estimation for Multidimensional Graded Response Model

description: Likert-type items with ordinal responses are frequently used in tests to assess multiple latent traits. The multidimensional graded response model (MGRM) is the preferred model for describing the relationship between these ordinal items and latent traits. In this article, we propose a novel Gaussian variational expectation maximization (GVEM) method for parameter estimation in MGRM. Rather than relying on direct numerical approximations for intractable integrals over multidimensional latent traits, our GVEM employs a carefully derived variational lower bound to approximate the marginal log-likelihood function, resulting in closed-form estimates. This method significantly improves the computational efficiency and is viable when dealing with high-dimensional latent variables. Additionally, an importance-weighted GVEM (IW-GVEM) algorithm is developed for MGRM to address the bias issue. Simulation studies show that our GVEM and IW-GVEM run significantly faster than the MH-RM algorithm and are of competitiveness in both confirmatory and exploratory analysis. Our proposed algorithms are illustrated by analyzing a real dataset from the Big-Five Personality test. Supplemental materials for the article are available online.
DOI: 10.17605/osf.io/qyrwe
title: Low Back Pain

description: The research generates new evidence in the field of low back pain management with particular focus on the role of Core Stability (CST) and McKenzie training (MCK).
DOI: 10.6084/m9.figshare.31113366
title: Connecting remote sensing, Inuit Knowledge, and <i>in-situ</i> observations for monitoring landfast sea ice fracture development in Admiralty Inlet, Nunavut

description: Fractures in landfast ice in the Canadian Arctic range from a few cm to several km wide. Currently, few remote sensing methods identify fractures at spatial and temporal scales relevant for on-ice travelers (meter and synoptic scales, respectively). We connect remotely sensed data (interferometric synthetic aperture radar (InSAR), optical imagery), Inuit Knowledge, and in-situ observations to identify fractures in landfast ice in Admiralty Inlet, Nunavut throughout the ice season (December–July) from 2018–2023. Community partnerships enabled the collection of in-situ observations of fractures and interpretation of remote sensing data using Inuit Knowledge. This approach facilitated a broader understanding of how fractures appear across different types of remote sensing data and throughout the ice season. We observed a clear seasonal cycle in the formation, refreezing or ridging, and (re)opening of fractures in all years. Locations of recurring features identified from remote sensing matched well with recurring features identified from Inuit Knowledge, but emergent fractures not yet embedded in Inuit Knowledge were also observed in the remote sensing data. Inuit project partners emphasized that the recent emergence of fractures in unexpected locations poses a safety hazard for sea ice travel and stressed the need for near real-time monitoring of these features.
DOI: 10.6084/m9.figshare.31113366.v1
title: Connecting remote sensing, Inuit Knowledge, and <i>in-situ</i> observations for monitoring landfast sea ice fracture development in Admiralty Inlet, Nunavut

description: Fractures in landfast ice in the Canadian Arctic range from a few cm to several km wide. Currently, few remote sensing methods identify fractures at spatial and temporal scales relevant for on-ice travelers (meter and synoptic scales, respectively). We connect remotely sensed data (interferometric synthetic aperture radar (InSAR), optical imagery), Inuit Knowledge, and in-situ observations to identify fractures in landfast ice in Admiralty Inlet, Nunavut throughout the ice season (December–July) from 2018–2023. Community partnerships enabled the collection of in-situ observations of fractures and interpretation of remote sensing data using Inuit Knowledge. This approach facilitated a broader understanding of how fractures appear across different types of remote sensing data and throughout the ice season. We observed a clear seasonal cycle in the formation, refreezing or ridging, and (re)opening of fractures in all years. Locations of recurring features identified from remote sensing matched well with recurring features identified from Inuit Knowledge, but emergent fractures not yet embedded in Inuit Knowledge were also observed in the remote sensing data. Inuit project partners emphasized that the recent emergence of fractures in unexpected locations poses a safety hazard for sea ice travel and stressed the need for near real-time monitoring of these features.
DOI: 10.15468/dl.hz42jj
title: Occurrence Download

description: A dataset containing 80 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Glandora nitida"
 ]
}

 The dataset includes 80 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002537-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n8ng8j
title: Occurrence Download

description: A dataset containing 35 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((27.01062 41.25368,41.97407 40.34561,41.6977 47.76811,28.77667 46.93177,27.01062 41.25368))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Engraulis encrasicolus",
 "Year 2017-2026"
 ]
}

 The dataset includes 35 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002536-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18287971
title: Dataset and code for "OxiMoRa: Oxidation Monitoring of Electrochemical Steel Corrosion based on Harmonic Radar and Deep Learning"

description: Dataset and code for "OxiMoRa: Oxidation Monitoring of Electrochemical Steel Corrosion based on Harmonic Radar and Deep Learning". IQ samples collected with harmonic radar and converted to grayscale images for computer vision classification. Varying quantities of corroded items (Screws) were displayed in front of the SDR-based radar, and their electromagnetic fingerprints were recorded.

 

OxiMoRa-main.zip contains all the code for reproducibility of the results.

The rest of the dataset has been split into subfiles for upload.

 

Scenario 1 - Detection has a bag of rusty screws displayed in front of a radar 

Scenario 2- Screws amount has different recordings with a different number of rusty screws in front of a radar. The purpose is to see if the radar can quantify to some extent the corrosion severity.

Scenario 3: 

Pipe_corroded are files with rusty screws inside to mimic internal corrosion.

Pipe_empty are files for baseline knowledge of the pipe.

Pipe_normal_nails are files where healthy screws are introduced in the pipe (galvanized steel for both the screws and the pipe)

NB: Dataset will be fully available in the coming days
DOI: 10.6084/m9.figshare.31113371
title: Mechanisms associated with temozolomide resistance in U87MG cell line: <i>in silico</i> and <i>in</i> <i>vitro</i> approaches

description: IDH-wildtype grade diffuse glioma is the most aggressive primary brain tumor in adults, with limited survival despite standard therapy with temozolomide (TMZ). Resistance to TMZ is driven by DNA repair pathways, tumor heterogeneity, and microenvironmental adaptations, indicating the need for novel therapeutic strategies. Using a systems biology approach, a compound-protein and protein–protein interaction networks was constructed with STRING 12.0 and STITCH 5.0 databases, identifying hub-bottleneck proteins associated with TMZ resistance. Topological and gene ontology analyses revealed 5 functional modules enriched in DNA repair and apoptosis, with <i>PARP1</i>, <i>PCNA</i>, <i>TP53</i>, <i>MSH2</i>, and <i>HSPA1a</i> emerging as key regulators. <i>HSPA1a</i> modulated survival by inhibiting <i>TP53</i> and caspase signaling, while also blocking mismatch repair via <i>MSH2</i> and <i>MLH1</i> upregulation. <i>In vitro</i> studies using U87MG cells exhibited greater sensitivity to demethoxycurcumin (DMC) compared to TMZ. Importantly, DMC synergized with TMZ to reduce the concentration required for growth inhibition. Comet assays data confirmed TMZ-induced DNA damage and transient base excision repair (BER) impairment, while DMC enhanced both <i>PCNA</i> expression and caspase-mediated apoptosis. Co-treatment with DMC and TMZ sustained <i>PARP1</i> downregulation, prolonged DNA damage, and amplified apoptosis. These findings indicated that DMC functions as an effective chemosensitizer, elevating TMZ efficacy. Combining DMC with DNA repair inhibitors may represent a promising therapeutic strategy to overcome resistance and improve IDH-wildtype grade diffuse glioma treatment outcomes.
DOI: 10.6084/m9.figshare.31113371.v1
title: Mechanisms associated with temozolomide resistance in U87MG cell line: <i>in silico</i> and <i>in</i> <i>vitro</i> approaches

description: IDH-wildtype grade diffuse glioma is the most aggressive primary brain tumor in adults, with limited survival despite standard therapy with temozolomide (TMZ). Resistance to TMZ is driven by DNA repair pathways, tumor heterogeneity, and microenvironmental adaptations, indicating the need for novel therapeutic strategies. Using a systems biology approach, a compound-protein and protein–protein interaction networks was constructed with STRING 12.0 and STITCH 5.0 databases, identifying hub-bottleneck proteins associated with TMZ resistance. Topological and gene ontology analyses revealed 5 functional modules enriched in DNA repair and apoptosis, with <i>PARP1</i>, <i>PCNA</i>, <i>TP53</i>, <i>MSH2</i>, and <i>HSPA1a</i> emerging as key regulators. <i>HSPA1a</i> modulated survival by inhibiting <i>TP53</i> and caspase signaling, while also blocking mismatch repair via <i>MSH2</i> and <i>MLH1</i> upregulation. <i>In vitro</i> studies using U87MG cells exhibited greater sensitivity to demethoxycurcumin (DMC) compared to TMZ. Importantly, DMC synergized with TMZ to reduce the concentration required for growth inhibition. Comet assays data confirmed TMZ-induced DNA damage and transient base excision repair (BER) impairment, while DMC enhanced both <i>PCNA</i> expression and caspase-mediated apoptosis. Co-treatment with DMC and TMZ sustained <i>PARP1</i> downregulation, prolonged DNA damage, and amplified apoptosis. These findings indicated that DMC functions as an effective chemosensitizer, elevating TMZ efficacy. Combining DMC with DNA repair inhibitors may represent a promising therapeutic strategy to overcome resistance and improve IDH-wildtype grade diffuse glioma treatment outcomes.
DOI: 10.6084/m9.figshare.29585907
title: Historical manufacturing census of Sweden: Data description and quality assessment

description: This article presents a comprehensive database featuring the digitized, cleaned, geocoded, and linked data of the Swedish manufacturing censuses between 1863 and 1900. The data covers close to the universe of Swedish manufacturing activity and includes establishment-level information on workers, the sum of production value, and toll as output value. The article describes how the data was originally collected and the steps taken to go from raw data to the digital database. We discuss each variable’s definition, how it changed over time, and provide an assessment of the reliability of the data pertaining to each variable. We also assess the quality of the data by comparing it to various other data sources from the same time period. The level of detail in the data makes the users able to both detect and address potential weaknesses of the data. The database offers a unique resource for scholars to study the manufacturing sector during a time of significant transformation in the Swedish industry. To the best of our knowledge, this is among the earliest sources of annual, establishment-level data worldwide. We discuss potential applications for researchers and potential extensions of the database.
DOI: 10.6084/m9.figshare.29585907.v1
title: Historical manufacturing census of Sweden: Data description and quality assessment

description: This article presents a comprehensive database featuring the digitized, cleaned, geocoded, and linked data of the Swedish manufacturing censuses between 1863 and 1900. The data covers close to the universe of Swedish manufacturing activity and includes establishment-level information on workers, the sum of production value, and toll as output value. The article describes how the data was originally collected and the steps taken to go from raw data to the digital database. We discuss each variable’s definition, how it changed over time, and provide an assessment of the reliability of the data pertaining to each variable. We also assess the quality of the data by comparing it to various other data sources from the same time period. The level of detail in the data makes the users able to both detect and address potential weaknesses of the data. The database offers a unique resource for scholars to study the manufacturing sector during a time of significant transformation in the Swedish industry. To the best of our knowledge, this is among the earliest sources of annual, establishment-level data worldwide. We discuss potential applications for researchers and potential extensions of the database.
DOI: 10.6084/m9.figshare.29528112
title: Addressing and Reducing Complexity in Metadata Mappings: RDA to DC and MARC 21

description: This study investigates whether introducing hierarchical and inclusion relationships to mappings between RDA elements and DC terms/MARC 21 fields can reduce complexity and enhance comprehensibility. The results indicate that applying these relationships elucidates the mapping process, using a proposed data visualization method. Quantitative analysis showed 98.4% and 92.2% consistency in DC and MARC mappings, respectively, with reduced inconsistencies when criteria were adjusted consistently with these relationships. However, certain cases remained unresolved, requiring further investigation into contextual factors affecting mapping inconsistencies. These findings help improve metadata interoperability, helping catalogers understand large-scale mappings.