cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.2qucqx
title: Occurrence Download

description: A dataset containing 23556 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Neovison vison (Schreber, 1777)",
 "Country is Sweden",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 23556 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030496-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ftdsqj
title: Occurrence Download

description: A dataset containing 3056 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rhododendron simsii Planch."
 ]
}

 The dataset includes 3056 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030497-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a2ksej
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rosa berberifolia Pall.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation)"
 ]
}

 The dataset includes 16 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030499-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25592/uhhfdm.17551
title: Research Data for Dissertation Project of Thomas Mayer

description: This upload cointains the structured overview of the research data related to the dissertation project titled "Driving Digital Transformation in Industrial Organizations: Agility, Architecture, and AI in the Automotive Industry" by Thomas Mayer from the Professorship of "Information Systems and Digital Innovation" at the "University of Hamburg Business School". The research data includes:

(1) Interview transcripts

(2) Trace data from a feedback channel

(3) Templates for consent forms of the interviews

(4) Ethical approvals from the University of Hamburg and the case organization

(5) Data analyses
DOI: 10.25592/uhhfdm.17552
title: Research Data for Dissertation Project of Thomas Mayer

description: This upload cointains the structured overview of the research data related to the dissertation project titled "Driving Digital Transformation in Industrial Organizations: Agility, Architecture, and AI in the Automotive Industry" by Thomas Mayer from the Professorship of "Information Systems and Digital Innovation" at the "University of Hamburg Business School". The research data includes:

(1) Interview transcripts

(2) Trace data from a feedback channel

(3) Templates for consent forms of the interviews

(4) Ethical approvals from the University of Hamburg and the case organization

(5) Data analyses
DOI: 10.15468/dl.zrq7wr
title: Occurrence Download

description: A dataset containing 748997 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinus sylvestris L.",
 "Country is one of (Holy See, Jersey, Guernsey, Isle of Man, United Kingdom of Great Britain and Northern Ireland, Ukraine, Switzerland, Sweden, Spain, Slovakia, Slovenia, Serbia, San Marino, Russian Federation, Romania, Portugal, Poland, Netherlands, Montenegro, Moldova, Republic of, Monaco, Malta, North Macedonia, Luxembourg, Lithuania, Liechtenstein, Latvia, Italy, Ireland, Iceland, Hungary, Greece, Germany, Åland Islands, Finland, Estonia, Faroe Islands, Denmark, Czechia, Croatia, Bulgaria, Bosnia and Herzegovina, Belgium, Belarus, Austria, Andorra, Albania)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 748997 records from 766 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030482-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/11804852
title: NonSpeech7kDS
DOI: 10.23729/24df28ed-f7b4-482d-9458-b708485e7cb8
title: FASTory Energy Consumption Data

description: This data consists of more than 4K data samples of power consumed by the conveyor belt motor driver. This data is collected from FASTory Assembly Line.   During data collection, belt tension and load on the conveyor belt (the number of pallets residing on the conveyor belt) varies from zero to maximum. This data describes how belt tension and load on the conveyor affect the power consumption of the conveyor belt motor driver. This data is used to develop a prognostic model which takes power consumed by the conveyor belt motor driver and load on the conveyor and predicts belt tension class and alerts users when belt tension deviates from optimal class. For more information contact, mahboob.elahi@tuni.fi
DOI: 10.15468/dl.vqwyjs
title: Occurrence Download

description: A dataset containing 17117 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Catalogue of Afrotropical Bees"
 ]
}

 The dataset includes 17117 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030501-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6gvxra
title: Occurrence Download

description: A dataset containing 634 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Grampus griseus (G.Cuvier, 1812)",
 "HasCoordinate is true",
 "Geometry POLYGON ((-98.05392 3.850193, -49.40613 3.850193, -49.40613 30.8906, -98.05392 30.8906, -98.05392 3.850193))"
 ]
}

 The dataset includes 634 records from 91 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030502-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15126/surreydata.901655
title: LSP Website Study Data Set

description: This exploratory study of translation company websites introduces the concept of translation service diversification and concludes that it is a strong feature in the marketing translation sector, with translation, post-editing, localisation, and transcreation as primary services. It highlights the difficulty in distinguishing between translation as a concept and standard translation as a service, and companies’ tendency to describe other services as superior to translation. It suggests that the dominance of negative descriptions of translation might influence companies’ ability to signal the value of translation to uninformed clients, which is vital in an industry climate characterised by the growth of freely available MT. It identifies the inverse scale between efficiency and text customisation as the primary differentiator between services. They are hypothesised to be based on different perceived client needs and require different sets of professional expertise to deliver. In providing their definition of translation, do Carmo and Moorkens underline that the notions of efficiency and text customisation (adaptability, and effectiveness) represent added values in demand in the language industry, but also concepts that have always been part of translation (2022, p.19). This reveals a potential gap between translation studies theory and industry realities which warrant further investigation.
DOI: 10.5281/zenodo.15407539
title: Code, data and analysis for construction and network analysis of chemokine signalling networks in inflammatory disease and further structural analysis of chemokine network inhibitors with AF3

description: Code, data, and analysis for "Robust and redundant chemokine signalling networks drive immune cell trafficking in human immunoinflammatory disease"

 

Some large */data directories will need to be unzipped to run the code.

 

Order to execute code directories in:

 

1. scRNA-seq

2. networks

3. quantify_redundancy

4. correlate_abundance_with_redundancy

5. show_extinction_lines_overlapping

6. compare_robustness_from_redundnacy

7. drugs

8. create_drugs_comparison_fig

9. af3_runs

10. model_complexes_compare_with_experiment

11. make_msa_fig

12. evasins_flex

 

Machine environment files used to run scripts in each directory can be found in each directory as env_info.json. Note: running the scripts will overwrite these.

 

Conda environment files used to run scripts in each directory can be found in each directory as conda_env.yaml. Note: running the scripts will overwrite these.

 

In scRNA_seq/data/raw/geo the following data must be secured from the GEO to perform the scRNAseq analysis. (~30GB)

 

GSE173706_RAW

GSE202379_RAW

GSE214695_RAW

GSE234077_RAW
DOI: 10.5281/zenodo.15407540
title: Code, data and analysis for construction and network analysis of chemokine signalling networks in inflammatory disease and further structural analysis of chemokine network inhibitors with AF3

description: Code, data, and analysis for "Robust and redundant chemokine signalling networks drive immune cell trafficking in human immunoinflammatory disease"

 

Some large */data directories will need to be unzipped to run the code.

 

Order to execute code directories in:

 

1. scRNA-seq

2. networks

3. quantify_redundancy

4. correlate_abundance_with_redundancy

5. show_extinction_lines_overlapping

6. compare_robustness_from_redundnacy

7. drugs

8. create_drugs_comparison_fig

9. af3_runs

10. model_complexes_compare_with_experiment

11. make_msa_fig

12. evasins_flex

 

Machine environment files used to run scripts in each directory can be found in each directory as env_info.json. Note: running the scripts will overwrite these.

 

Conda environment files used to run scripts in each directory can be found in each directory as conda_env.yaml. Note: running the scripts will overwrite these.

 

In scRNA_seq/data/raw/geo the following data must be secured from the GEO to perform the scRNAseq analysis. (~30GB)

 

GSE173706_RAW

GSE202379_RAW

GSE214695_RAW

GSE234077_RAW
DOI: 10.6084/m9.figshare.29064287
title: AHOD: Adaptive Hybrid Object Detector for Context-Awareed Item

description: We evaluated our AHOD model using two well-known datasets in the field of object detection:<b>COCO (Common Objects in Context)</b>One of the most widely used benchmarks for object detection.Contains over 200,000 images and more than 80 object categories.<br>Includes objects in varied and sometimes cluttered contexts, allowing the robustness of detectors to be evaluated.<b>Pascal VOC</b>Another reference dataset, often used for classification, detection and segmentation tasks.Includes 20 object categories, with precise bounding box annotations.<br>Less complex than COCO, but useful for comparing performance on more conventional objects.<b>Tools, techniques and innovations used</b>The AHOD architecture is based on <b>three main modules</b>:<b>Feature Pyramid Enhancement (FPE)</b>Multi-scale feature processing tool.Improves the representation of objects of various sizes in the same image.<br>Inspired by architectures such as FPN (Feature Pyramid Networks), but optimised for better performance.<b>Dynamic Context Module (DCM)</b>Intelligent contextual module.Capable of dynamically adjusting the extracted features according to the context (e.g. by adapting the features according to urban or rural areas in a road image).Enhances the model's ability to understand the overall context of the scene.<br><b>Fast and Accurate Detection Head (FADH)</b>Optimised detection head.Seeks a compromise between the speed of YOLO and the accuracy of Faster R-CNN.Probably uses lightweight convolution layers or optimisations such as MobileNet/Depthwise Convolutions.<b>Probable technologies used</b><br>Although the summary does not specify this, we can reasonably assume that the following tools are used:<b>Deep learning frameworks</b>: PyTorch or TensorFlow, which are standard in object detection research.<b>GPUs</b> for training and inference, particularly for measuring inference times (essential in real-time applications).<b>Standard evaluation techniques</b>:<b>mAP (mean Average Precision)</b>: measure of average precision.<b>FPS (Frames Per Second)</b> or <b>inference time</b> for real-time performance.
DOI: 10.6084/m9.figshare.29064287.v2
title: AHOD: Adaptive Hybrid Object Detector for Context-Awareed Item

description: We evaluated our AHOD model using two well-known datasets in the field of object detection:<b>COCO (Common Objects in Context)</b>One of the most widely used benchmarks for object detection.Contains over 200,000 images and more than 80 object categories.<br>Includes objects in varied and sometimes cluttered contexts, allowing the robustness of detectors to be evaluated.<b>Pascal VOC</b>Another reference dataset, often used for classification, detection and segmentation tasks.Includes 20 object categories, with precise bounding box annotations.<br>Less complex than COCO, but useful for comparing performance on more conventional objects.<b>Tools, techniques and innovations used</b>The AHOD architecture is based on <b>three main modules</b>:<b>Feature Pyramid Enhancement (FPE)</b>Multi-scale feature processing tool.Improves the representation of objects of various sizes in the same image.<br>Inspired by architectures such as FPN (Feature Pyramid Networks), but optimised for better performance.<b>Dynamic Context Module (DCM)</b>Intelligent contextual module.Capable of dynamically adjusting the extracted features according to the context (e.g. by adapting the features according to urban or rural areas in a road image).Enhances the model's ability to understand the overall context of the scene.<br><b>Fast and Accurate Detection Head (FADH)</b>Optimised detection head.Seeks a compromise between the speed of YOLO and the accuracy of Faster R-CNN.Probably uses lightweight convolution layers or optimisations such as MobileNet/Depthwise Convolutions.<b>Probable technologies used</b><br>Although the summary does not specify this, we can reasonably assume that the following tools are used:<b>Deep learning frameworks</b>: PyTorch or TensorFlow, which are standard in object detection research.<b>GPUs</b> for training and inference, particularly for measuring inference times (essential in real-time applications).<b>Standard evaluation techniques</b>:<b>mAP (mean Average Precision)</b>: measure of average precision.<b>FPS (Frames Per Second)</b> or <b>inference time</b> for real-time performance.
DOI: 10.6084/m9.figshare.29064287.v1
title: UntitlAHOD: Adaptive Hybrid Object Detector for Context-Awareed Item

description: We evaluated our AHOD model using two well-known datasets in the field of object detection:<b>COCO (Common Objects in Context)</b>One of the most widely used benchmarks for object detection.Contains over 200,000 images and more than 80 object categories.<br>Includes objects in varied and sometimes cluttered contexts, allowing the robustness of detectors to be evaluated.<b>Pascal VOC</b>Another reference dataset, often used for classification, detection and segmentation tasks.Includes 20 object categories, with precise bounding box annotations.<br>Less complex than COCO, but useful for comparing performance on more conventional objects.<b>Tools, techniques and innovations used</b>The AHOD architecture is based on <b>three main modules</b>:<b>Feature Pyramid Enhancement (FPE)</b>Multi-scale feature processing tool.Improves the representation of objects of various sizes in the same image.<br>Inspired by architectures such as FPN (Feature Pyramid Networks), but optimised for better performance.<b>Dynamic Context Module (DCM)</b>Intelligent contextual module.Capable of dynamically adjusting the extracted features according to the context (e.g. by adapting the features according to urban or rural areas in a road image).Enhances the model's ability to understand the overall context of the scene.<br><b>Fast and Accurate Detection Head (FADH)</b>Optimised detection head.Seeks a compromise between the speed of YOLO and the accuracy of Faster R-CNN.Probably uses lightweight convolution layers or optimisations such as MobileNet/Depthwise Convolutions.<b>Probable technologies used</b><br>Although the summary does not specify this, we can reasonably assume that the following tools are used:<b>Deep learning frameworks</b>: PyTorch or TensorFlow, which are standard in object detection research.<b>GPUs</b> for training and inference, particularly for measuring inference times (essential in real-time applications).<b>Standard evaluation techniques</b>:<b>mAP (mean Average Precision)</b>: measure of average precision.<b>FPS (Frames Per Second)</b> or <b>inference time</b> for real-time performance.
DOI: 10.17863/cam.109929
title: Research data supporting "Purely electrical detection of electrolyte concentration through microfluidic impedance spectroscopy"

description: The data comprises of impedance spectra measured using a Sciospec ISX3v2 impedance analyser, of a novel device consisting of aerosol-jet printed silver interdigitated electrodes integrated into a microfluidic channel. Each impedance spectrum (.spec file) consists of some metadata, followed by the measured real (Re / Ohms) and imaginary (Img / Ohms) components measured at each measurement frequency (Freq / Hz). The subfolders contain experimental details. For example, the concentration of the chemical species (in millimolar, mM), and the species. The final subfolder contains information regarding the measurement channel (e.g. '2-4'). Each channel is connected to one pair of electrodes, which lie under 1 channel csv files are also provided, which have been exported from the code, and contain the impedance spectra and important features (such as the turning point frequency of the capacitance-frequency curves). This enables plotting of the data without running the complete analysis code, if required. The code consists of various Jupyter Notebooks (ipynb) (Python 3.7), which have been used to analyse the data. Each dataset has a separate associated Jupyter Notebook. The 'source' of the code should be input, and must match the directory of the data on the user's computer. Each function contains a short description, and all functions are called by the 'analysis_all()' function at the end of the script. Comments are present to explain specifics of the code where relevant. Together, the data and code have been used to determine the concentration of 'unknown' solutions, and to investigate various properties of the device.
DOI: 10.5281/zenodo.15368446
title: Dogon Language ATR comparative stems

description: Comparative stems across Dogon languages
DOI: 10.5281/zenodo.15368447
title: Dogon Language ATR comparative stems

description: Comparative stems across Dogon languages
DOI: 10.17863/cam.116991
title: Research data supporting "Electrical monitoring of Vibrio vulnificus-host cell membrane interactions in simple and complex matrices"

description: The data contained supports the Electrical monitoring of Vibrio vulnificus-host cell membrane interactions in simple and complex matrices. It contains data relating to whole cell monolayer and support lipid bilayer resistance changes following infection with Vibrio vulnificus, obtained through electrochemical impedance spectroscopy as well as supported lipid bilayer stability data and confocal/other images.
DOI: 10.5281/zenodo.15410791
title: Extracted datasets and model checkpoints for anonymous submission of: You Have to Be Realistic: On Investigating Feature Emergence in Deep Learning-based Side-channel Analysis
DOI: 10.5281/zenodo.15410792
title: Extracted datasets and model checkpoints for anonymous submission of: You Have to Be Realistic: On Investigating Feature Emergence in Deep Learning-based Side-channel Analysis
DOI: 10.6084/m9.figshare.29064326
title: <b>Coexistence of </b><b><i>bla</i></b><b>NDM-</b><b>1</b><b>, </b><b><i>bla</i></b><b>KPC-2</b><b> and </b><b><i>tmexCD2-toprJ2 </i></b><b>in</b><b> </b><b>Clinical ST</b><b>1545</b><b> </b><b><i>Klebsiella pneumoniae</i></b><b> from China</b>

description: The emergence of pathogens resistant to both carbapenems and tigecycline poses a global threat to public health. Adaptation of resistance determinants to novel hosts has accelerated the resistance crisis. In this study, we identified seven ST1545 <i>Klebsiella pneumoniae</i> isolates co-harboring <i>bla</i>NDM-1 and <i>bla</i>KPC-2, with three of them additionally carrying <i>tmexCD2-toprJ2</i>. These isolates were resistant to multiple antimicrobials. The supplementary data was uploaded here for readers' reference.
DOI: 10.18112/openneuro.ds006128.v1.0.2
title: Data for "Modeling 2D Spatio-Tactile Population Receptive Fields of the Fingertip in Human Primary Somatosensory Cortex"
DOI: 10.18434/m32246
title: macOS Security Compliance Project

description: The macOS security compliance project is an open source effort to provide a programmatic approach to generating security guidance. This project can be used to create customized security baselines of technical security controls by leveraging a library of atomic actions which are mapped to compliance requirements in existing security guides or used to develop customized guidance. Through the use of a library of atomic actions that enhance security, and mapping them back to existing guides and policies, a single project can support multiple security guides and regulated industry policies while also allowing for documentation and QA to be uniformly managed through a single effort. This approach simplifies, and radically accelerates, the updating of annual security guidance through a unification and standardization of effort.Rationale for this project:Normalize and accelerate annual adoption of OS/Hardware by having guidance available to meet the needs of new operating systems on releaseReduce worldwide effort in creating annual guidance by unifying and consolidating compliance efforts into a single projectDevelop a methodology to foster collaboration between baseline authors, reducing overhead and redundancyUnify approach in setting controlsProvide MDM/EMM/security/audit vendors and Apple insight into customer hardening needsImportant note: This project is a programmatic approach to security policy and can produce output content to be used IN CONJUNCTION with management and security tools to achieve compliance.
DOI: 10.15468/dl.unpec5
title: Occurrence Download

description: A dataset containing 835082 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Deschampsia P.Beauv."
 ]
}

 The dataset includes 835082 records from 1702 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030490-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tr5aez
title: Occurrence Download

description: A dataset containing 79706 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (EOD – eBird Observation Dataset, iNaturalist Research-grade Observations)",
 "Geometry POLYGON((-118.57007 34.88324,-118.46872 34.81918,-118.08818 34.82014,-117.92851 34.85552,-117.87688 35.02092,-117.9964 35.20641,-118.02317 35.23414,-118.08914 35.25517,-118.14077 35.25517,-118.27654 35.42441,-118.40944 35.42154,-118.54234 35.26665,-118.59971 35.24083,-118.64082 35.03718,-118.57007 34.88324))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2007-2022"
 ]
}

 The dataset includes 79706 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012899-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.