cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/3d85sw6mhg.1
title: AI_Communication Student

description: The dataset is about the perceptions of Communication Students' on AI in general and AI in media use.
DOI: 10.17632/3zzcc3bfwy.1
title: CRIS-Cotton

description: Cotton crop row centerline detection dataset.
DOI: 10.17632/gw9hjn4v65
title: Enriched Tourism Dataset London (POIs)

description: This dataset contains the London subset of the Tourpedia dataset, specifically focusing on points of interest (POIs) categorized as attractions (dataset available at http://tour-pedia.org/download/london-attraction.csv). The original dataset comprises 20,727 entries that encompass a variety of attractions across London, providing details on several attributes for each POI. These attributes include a unique identifier, POI name, category, location information (address), latitude, longitude, specific details, and user-generated reviews. The review fields contain textual feedback from users, aggregated from platforms such as Google Places, Foursquare, and Facebook, offering a qualitative insight into each location.

However, due to the initial dataset's high proportion of incomplete or inconsistently structured entries, a rigorous cleaning process was implemented. This process entailed the removal of erroneous and incomplete data points, ultimately refining the dataset to 2,341 entries that meet criteria for quality and structural coherence. These selected entries were subjected to further validation to ensure data integrity, enabling a more accurate representation of London's attractions.

- London.csv
It contains columns including a unique identifier, POI name, category, location information (address), latitude, longitude, specific details, and user-generated reviews. Those reviews have been previously retrieved and pre-processed from Google Places, Foursquare, and Facebook, and have different formats: all words, only nouns, nouns + verbs, noun + adjectives and nouns + verbs + adjectives.

- London_annotated.csv
It contains the ground truth relating to the previous dataset, with manual annotations made by humans on the categorisation of each of the POIs into 12 different pre-defined categories.
It has the following columns:
 * POI name
 * POI's address
 * One column for each of the above categories. 1 means that the POI belongs to the category while blank indicates that it does not.
DOI: 10.17632/gw9hjn4v65.2
title: Enriched Tourism Dataset London (POIs)

description: This dataset contains the London subset of the Tourpedia dataset, specifically focusing on points of interest (POIs) categorized as attractions (dataset available at http://tour-pedia.org/download/london-attraction.csv). The original dataset comprises 20,727 entries that encompass a variety of attractions across London, providing details on several attributes for each POI. These attributes include a unique identifier, POI name, category, location information (address), latitude, longitude, specific details, and user-generated reviews. The review fields contain textual feedback from users, aggregated from platforms such as Google Places, Foursquare, and Facebook, offering a qualitative insight into each location.

However, due to the initial dataset's high proportion of incomplete or inconsistently structured entries, a rigorous cleaning process was implemented. This process entailed the removal of erroneous and incomplete data points, ultimately refining the dataset to 2,341 entries that meet criteria for quality and structural coherence. These selected entries were subjected to further validation to ensure data integrity, enabling a more accurate representation of London's attractions.

- London.csv
It contains columns including a unique identifier, POI name, category, location information (address), latitude, longitude, specific details, and user-generated reviews. Those reviews have been previously retrieved and pre-processed from Google Places, Foursquare, and Facebook, and have different formats: all words, only nouns, nouns + verbs, noun + adjectives and nouns + verbs + adjectives.

- London_annotated.csv
It contains the ground truth relating to the previous dataset, with manual annotations made by humans on the categorisation of each of the POIs into 12 different pre-defined categories.
It has the following columns:
 * POI name
 * POI's address
 * One column for each of the above categories. 1 means that the POI belongs to the category while blank indicates that it does not.
DOI: 10.17632/wxxkwjk9hm.1
title: Age-related differences in learning, memory and brain plasticity in workers of the common wasp Vespula vulgaris

description: Raw data files, figures & tables and R-script of the manuscript of "Age-related differences in learning, memory and brain plasticity in workers of the common wasp Vespula vulgaris".
DOI: 10.17632/43nfv26gpk.1
title: UKMDDoSDN: A DDoS Attacks Dataset for Software-Defined Networks

description: The UKMDDoSDN dataset is a comprehensive benchmark designed to advance research in Distributed Denial-of-Service (DDoS) attack detection within Software-Defined Networks (SDNs). It supports multi-attack dataset generation, including SYN flood (1), UDP flood (2), and ICMP flood (3) attacks, alongside normal benign traffic (0), providing a diverse and realistic mix of network traffic. Built on a realistic enterprise network topology, the architecture consists of four isolated subnets: an External Network (h1: 192.168.10.x), a Corporate Internal Network (h2-h5: 192.168.20.x), a Server/DMZ Network (h6: 192.168.30.x), and a Management Network (C0: 192.168.0.x), ensuring proper segmentation and accurate representation of enterprise environments.

UKMDDoSDN produces three complementary CSV datasets, , captured simultaneously to support multi-perspective analysis:
1. packet_features.csv, a packet-level dataset optimized for real-time detection; 
2. flow_features.csv, containing flow-level statistical features extracted from the SDN controller; and 
3. cicflow_features.csv, offering advanced flow-based features generated using CICFlowMeter.

In addition to the CSV datasets, raw PCAP files are provided, allowing for deep packet inspection, custom feature extraction, and traffic replay for extended experimentation. 

The dataset was generated in batch mode over approximately 52 hours, ensuring sufficient volume and diversity for robust model training and evaluation. Leveraging the Ryu SDN controller, it enables intelligent traffic management and realistic attack simulations. With high-quality labeling for both multi-class (0–3) and binary (0–1) classification, UKMDDoSDN provides a versatile and practical resource for intrusion detection, traffic analysis and network security research in SDN environments.
DOI: 10.17632/djt2xjx7ks
title: Nano-Newton sensitive MEMS Fx sensors made using CMOS-derived piezoresistors and HAR 3D-printed metal and polymer beams_IshaLodhi et al.

description: Force characterization data of MEMS pillar-based in-plane force sensors. Active stress-sensing elements are CMOS-derived diffused N-type silicon piezoresistors of widths ranging 300 nm to 1 micron, (length =1 micron for all). The silicon-based, substrate-diffused, piezoresistors have a fan-out "butterfly" design which allows connection of larger metal contacts (1x1 um2 to 2x2um2) to sub-micron width devices. The Fx sensor pillars are made by two different approaches, and both using additive technologies. The first MEMS Fx sensor type is made with 3D-metal-printed high-aspect-ratio (HAR) pillars. We use local metal electrodeposition for 3D-metal-printing (Exaddon Ceres AG, Switzerland). The active piezoresistor microdevices and chips were modified for compatibility with the acidic electrochemical metal deposition process and connection of print surfaces to the electrodeposition chamber work electrode. (WE). The second MEMS Fx sensor presented have pillars exposed in a two-photon polymerization process (Nanoscribe GmbH), and open, unfilled, trenches around the piezoresistor for mechanical stress-concentration in the piezoresistor body. The pillar material for the second type of sensor pillars is the photo-sensitive acrylic-based polymer, IP-Dip (Nanoscribe). This dataset includes experiment data from electromechanical characterization of both sensor types, with (1) HAR copper (Cu) pillars and, (2) polymer TPP pillars. Sinusoidal force (Fx) waveforms are applied against pillar tips, and sensor resistance is recorded simultaneously (sample interval =0.2s). Sensitivity is measured as the fractional change of resistance (delta-R/R0) of the piezoresistive Fx sensors for approaching, and retreating force directions, as well as for varying force application speeds from 0.01 micro-Newton/s to 1.7 micro-Newton/s. A separate sheet includes all COMSOL FEA simulation study results for the 3D piezoresistive multiphysics models of both sensors. Sampling rate of the applied pillar deflection (displacement of the piezoactuated stage, delta-X) depends on wave-table-rate applied. Force (Fx) is extracted by scaling deflection applied on pillar tip with COMSOL simulated pillar spring constants in K(N/m) using SEM-measured pillar geometries and Young's modulus of 130 GPa from literature. Fx sensitivity responses are repeatable with low hysteresis, and have maximum Fx range determined by pillar delamination force (&gt; 3 micro-Newton). Highest sensitivity measured for the HAR Cu pillar sensors was 0.26% per micro-Newton. Best sensitivity measured with the open trenches and TPP pillars was 0.44% per micro-Newton. At the highest Fx force application rate, sensitivities only drops up to 30%.
DOI: 10.21334/npolar.2024.7b5b8509
title: CTD profiles with auxiliary sensors from NPI cruise AO-II-2023 in the northern Svalbard shelf slope and in the Nansen Basin in August 2023
DOI: 10.17632/djt2xjx7ks.1
title: Nano-Newton sensitive MEMS Fx sensors made using CMOS-derived piezoresistors and HAR 3D-printed metal and polymer beams_IshaLodhi et al.

description: Force characterization data of MEMS pillar-based in-plane force sensors. Active stress-sensing elements are CMOS-derived diffused N-type silicon piezoresistors of widths ranging 300 nm to 1 micron, (length =1 micron for all). The silicon-based, substrate-diffused, piezoresistors have a fan-out "butterfly" design which allows connection of larger metal contacts (1x1 um2 to 2x2um2) to sub-micron width devices. The Fx sensor pillars are made by two different approaches, and both using additive technologies. The first MEMS Fx sensor type is made with 3D-metal-printed high-aspect-ratio (HAR) pillars. We use local metal electrodeposition for 3D-metal-printing (Exaddon Ceres AG, Switzerland). The active piezoresistor microdevices and chips were modified for compatibility with the acidic electrochemical metal deposition process and connection of print surfaces to the electrodeposition chamber work electrode. (WE). The second MEMS Fx sensor presented have pillars exposed in a two-photon polymerization process (Nanoscribe GmbH), and open, unfilled, trenches around the piezoresistor for mechanical stress-concentration in the piezoresistor body. The pillar material for the second type of sensor pillars is the photo-sensitive acrylic-based polymer, IP-Dip (Nanoscribe). This dataset includes experiment data from electromechanical characterization of both sensor types, with (1) HAR copper (Cu) pillars and, (2) polymer TPP pillars. Sinusoidal force (Fx) waveforms are applied against pillar tips, and sensor resistance is recorded simultaneously (sample interval =0.2s). Sensitivity is measured as the fractional change of resistance (delta-R/R0) of the piezoresistive Fx sensors for approaching, and retreating force directions, as well as for varying force application speeds from 0.01 micro-Newton/s to 1.7 micro-Newton/s. A separate sheet includes all COMSOL FEA simulation study results for the 3D piezoresistive multiphysics models of both sensors. Sampling rate of the applied pillar deflection (displacement of the piezoactuated stage, delta-X) depends on wave-table-rate applied. Force (Fx) is extracted by scaling deflection applied on pillar tip with COMSOL simulated pillar spring constants in K(N/m) using SEM-measured pillar geometries and Young's modulus of 130 GPa from literature. Fx sensitivity responses are repeatable with low hysteresis, and have maximum Fx range determined by pillar delamination force (&gt; 3 micro-Newton). Highest sensitivity measured for the HAR Cu pillar sensors was 0.26% per micro-Newton. Best sensitivity measured with the open trenches and TPP pillars was 0.44% per micro-Newton. At the highest Fx force application rate, sensitivities only drops up to 30%.
DOI: 10.17632/cf3vpvvpgd
title: Service Trade, Regional Specialization, and Welfare

description: This package provides the replication files for the paper “Service Trade, Regional Specialization, and Welfare”.
DOI: 10.17632/cf3vpvvpgd.1
title: Service Trade, Regional Specialization, and Welfare

description: This package provides the replication files for the paper “Service Trade, Regional Specialization, and Welfare”.
DOI: 10.5281/zenodo.17193390
title: Extending digital biology: bacterial survival and morphological heterogeneity under antibiotic stress

description: The morphology of bacteria is intimately linked to their biological state: It is modified by antibiotic stress while also serving to survive the antibiotic. As such knowing which morphologies are selected by the surviving cells would help explain how individual cells are able to escape the antibiotic pressure. However associating morphological descriptors with quantitative measurements of cell survival remain elusive. Here we present a workflow to generate morphological signatures for the progeny of individual cells in the presence or absence of antibiotics. The workflow uses stationary microfluidic droplets, to encapsulate and grow bacteria, and confocal microscopy to image the contents of each droplet. Experiments are performed for 168 antibiotic conditions, corresponding to 82,000 droplet images. This massive data set is treated using a custom image analysis pipeline that enables rapid labeling of the morphologies within a subset of the images. The curated images are then used to train a neural network that assigns a positive or negative label, for each of these non-exclusive features, yielding a multidimensional signature of labels within each droplet. The results show the co-existence of different morphologies, even for the progeny of individual cells. The mix of morphologies changes as a function of antibiotic type and concentration, thus providing a way to distinguish antibiotics by their mode of action. By combining these morphological signatures with the digital detection of survival, this workflow can serve to understand the emergence of antibiotic resistance or to identify antimicrobial activity of unknown substances. This dataset contains the SQL database and imaging data needed to recreate the interactive web viewer. More details are available at the associated GitHub repository.
DOI: 10.17632/3b4n36z4bg
title: Multimodal Deep Receptor Scanning Reveals Constraints on GPCR Biosynthesis

description: G protein-coupled receptors (GPCRs) mediate a variety of signaling pathways and are among the most common pharmacological targets. While advances in structural biochemistry have provided deep functional insights into dozens of key receptors, many of the 800+ human GPCRs remain understudied. In the following, we introduce a versatile “deep receptor scanning” platform that can be used to experimentally characterize 767 human GPCRs and 174 known GPCR splice variants in parallel. We quantitatively characterize the relative abundance of receptor transcripts, their translational efficiency, and the plasma membrane expression of each receptor in the context of a recombinant pool of HEK293T cells expressing individual GPCRs. We then employ machine learning to identify specific structural features that modulate GPCR expression. This experimental platform and informatic approach are compatible with a variety of assays and can be used to efficiently explore the biochemical and pharmacological properties of the GPCRome.
DOI: 10.17632/3b4n36z4bg.1
title: Multimodal Deep Receptor Scanning Reveals Constraints on GPCR Biosynthesis

description: G protein-coupled receptors (GPCRs) mediate a variety of signaling pathways and are among the most common pharmacological targets. While advances in structural biochemistry have provided deep functional insights into dozens of key receptors, many of the 800+ human GPCRs remain understudied. In the following, we introduce a versatile “deep receptor scanning” platform that can be used to experimentally characterize 767 human GPCRs and 174 known GPCR splice variants in parallel. We quantitatively characterize the relative abundance of receptor transcripts, their translational efficiency, and the plasma membrane expression of each receptor in the context of a recombinant pool of HEK293T cells expressing individual GPCRs. We then employ machine learning to identify specific structural features that modulate GPCR expression. This experimental platform and informatic approach are compatible with a variety of assays and can be used to efficiently explore the biochemical and pharmacological properties of the GPCRome.
DOI: 10.5281/zenodo.17235419
title: Observaciones de Biodiversidad de Guatemala, CEAB-UVG, 30 septiembre 2025
DOI: 10.5281/zenodo.17235420
title: Observaciones de Biodiversidad de Guatemala, CEAB-UVG, 30 septiembre 2025
DOI: 10.5281/zenodo.17235304
title: Links between Wikidata, the Thesaurus Nuovo soggettario and other thesauri

description: This dataset collects the results of a series of SPARQL queries run on the SPARQL endpoint of the Nuovo soggettario Thesaurus (https://digitale.bncf.firenze.sbn.it/openrdf-workbench/repositories/NS/query) and on the SPARQL endpoints of Wikidata (the official one, https://query.wikidata.org/, and https://qlever.cs.uni-freiburg.de/wikidata/).



The file "Elenco query.xlsx" contains the list of the queries

The archive "Risultati query.zip" contains the results of these queries, downloaded on 2025-09-30
DOI: 10.5281/zenodo.17235305
title: Links between Wikidata, the Thesaurus Nuovo soggettario and other thesauri

description: This dataset collects the results of a series of SPARQL queries run on the SPARQL endpoint of the Nuovo soggettario Thesaurus (https://digitale.bncf.firenze.sbn.it/openrdf-workbench/repositories/NS/query) and on the SPARQL endpoints of Wikidata (the official one, https://query.wikidata.org/, and https://qlever.cs.uni-freiburg.de/wikidata/).



The file "Elenco query.xlsx" contains the list of the queries

The archive "Risultati query.zip" contains the results of these queries, downloaded on 2025-09-30
DOI: 10.17632/9v8tgnt63x
title: FidoFrame: A Customizable Surface Fiducial for Gradual Deformation Analysis in Sensor Evaluation

description: The implementation of distance sensors in present technology has become common in order to receive information and feedback on the surrounding environment. LiDAR and 3D imaging technology is frequently used in automated vehicles, structural inspections, and topological scans. When selecting these distance sensors, the quantity and quality of the data collected is a priority for many of these uses. Currently, there is no standard to analyze the uncertainty and resolution of the LiDAR sensor’s data points. Every company and researcher use ad hoc methods to quantify the specifications of the sensor. Challenges presented in the current state of the art include inefficient ways to calculate gradual deformations and the reliance on proprietary specifications with no form of verification regarding its accuracy. Common difficulties in sensor evaluations include: (1) unreliable tests overlook small defects in surfaces and (2) the methods used are not readily available to confirm resolution of the data. This paper would create a “control variable” to analyze the quality of the data collected: a mockup capable of presenting varied fiducial geometric dimensions. This solution presents the FidoFrame for a detailed study of distance sensors to detect minor variation in geometric surfaces which can be translated to modern applications.

The design files of the hardware explained in this paper are composed of parts and assemblies. These files are attached as Supplemental Materials. There are a total of 7 parts and 3 assemblies. The purpose of the CAD design is to organize the thought process behind the FidoFrame’s manufacturing and the needed materials. The design files allow for a knowledge of the object’s dimensions and size, how it will be built, and the manufacturing needs to construct the products.
DOI: 10.17632/9v8tgnt63x.1
title: FidoFrame: A Customizable Surface Fiducial for Gradual Deformation Analysis in Sensor Evaluation

description: The implementation of distance sensors in present technology has become common in order to receive information and feedback on the surrounding environment. LiDAR and 3D imaging technology is frequently used in automated vehicles, structural inspections, and topological scans. When selecting these distance sensors, the quantity and quality of the data collected is a priority for many of these uses. Currently, there is no standard to analyze the uncertainty and resolution of the LiDAR sensor’s data points. Every company and researcher use ad hoc methods to quantify the specifications of the sensor. Challenges presented in the current state of the art include inefficient ways to calculate gradual deformations and the reliance on proprietary specifications with no form of verification regarding its accuracy. Common difficulties in sensor evaluations include: (1) unreliable tests overlook small defects in surfaces and (2) the methods used are not readily available to confirm resolution of the data. This paper would create a “control variable” to analyze the quality of the data collected: a mockup capable of presenting varied fiducial geometric dimensions. This solution presents the FidoFrame for a detailed study of distance sensors to detect minor variation in geometric surfaces which can be translated to modern applications.

The design files of the hardware explained in this paper are composed of parts and assemblies. These files are attached as Supplemental Materials. There are a total of 7 parts and 3 assemblies. The purpose of the CAD design is to organize the thought process behind the FidoFrame’s manufacturing and the needed materials. The design files allow for a knowledge of the object’s dimensions and size, how it will be built, and the manufacturing needs to construct the products.