cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/rs2z-zz54
title: Heat energy

description: Heating in a building involves generating and distributing heat to maintain a comfortable indoor temperature. Here's an overview of how heating works in a building: 1. **Heat Generation**:   - A heat source, such as a boiler, furnace, or electric heating element, produces heat energy. This source is typically fueled by natural gas, oil, electricity, or another energy source. 2. **Heat Transfer**:   - The heat generated is transferred to a medium, often water or air, through a process like combustion, electrical resistance, or heat exchange. 3. **Distribution**:   - In a water-based system, hot water is pumped through pipes to radiators or underfloor heating systems. In an air-based system, warm air is circulated through ducts to various rooms. 4. **Radiation and Convection**:   - Once the heat is in the room, it radiates out from the radiators or heating elements, warming nearby surfaces. Additionally, warm air rises, creating convection currents that help distribute heat throughout the space. 5. **Thermostat Control**:   - A thermostat monitors the indoor temperature. When it detects a drop below the set temperature, it signals the heating system to start generating more heat. As for calculating heat energy, it involves understanding the principles of heat transfer and the specific characteristics of the building: 1. **Heat Load Calculation**:   - This involves determining the amount of heat energy required to maintain a comfortable temperature inside the building. Factors include the outdoor temperature, insulation levels, building materials, windows, and occupancy patterns. 2. **Specific Heat Capacity**:   - This property quantifies how much heat energy is needed to raise the temperature of a substance. For example, water has a specific heat capacity of about 4.18 Joules per gram per degree Celsius. 3. **Heat Transfer Equations**:   - Different equations are used depending on whether conduction (direct contact), convection (movement of fluid), or radiation (electromagnetic waves) is the dominant mode of heat transfer. 4. **Efficiency Considerations**:   - The efficiency of the heating system affects how much energy is actually used to achieve the desired level of comfort. Remember, these are simplified explanations, and actual calculations can get quite complex, often involving computer modeling and simulation for precise results. It's also important to consider local building codes and standards when designing or upgrading a heating system.
DOI: 10.6084/m9.figshare.24131697
title: Sakshi Mathur - M22SLAP0003.xlsx

description: Description
DOI: 10.6084/m9.figshare.24131697.v1
title: Sakshi Mathur - M22SLAP0003.xlsx

description: Description
DOI: 10.6084/m9.figshare.24131694
title: Adarsh Nim M23SLAP0003 Assignment.xlsx

description: The data is just a sample to understand how figshare work.
DOI: 10.6084/m9.figshare.24131694.v1
title: Adarsh Nim M23SLAP0003 Assignment.xlsx

description: The data is just a sample to understand how figshare work.
DOI: 10.5281/zenodo.8062655
title: Counting and Sequential Information Processing in Mechanical Metamaterials

description: This dataset contains images and driving protocols used in the paper: "Counting and Sequential Information Processing in Mechanical Metamaterials", published in Physical Review Letters. In this paper we demonstrate "beam counters"; metamaterials that count driving cycles. We demonstrate the counters sensitivity to various driving amplitudes and show how this might be used to infer more information from the applied driving and how to construct a "lock and key" metamaterial with an internal state that can only be reached with one unique input sequence.<br> <br> The data in this replication package is hierarchically organized by making use of a directory per figure in the paper. Contained in this replication package are a number of files used in the figure as described below. Experimental data from the measurement setup is stored in matched subdirectorys with a name, for example "001/" and a file "times_001.csv". Here the subdirectory contains images taken of the sample and the csv file contains the times at which the images were taken, the change in pixel intensity from one image to the next (sampled at a shorter interval than the saved images) and the position of the driving stage and a measured inductive position.<br> fig1 001/<br> - directory containing the original unedited figures comparing above and below D* 001_cropped_selection/<br> - directory containing a cropped selection of the images used in the figure times_001.csv fig2 002/<br> - directory containing images of the ten counter being compressed with marked m-beams times_002.csv Kymograph.tif<br> - A kymograph image calculated from 002 by filtering the colored m-beams and taking stacking a single horizontal slice from all of the images. kymograph.svg<br> - The plotted horizontal position of the beam traces visible in Kymograph.tif fig3 000/<br> - directory containing images of the compression of counter with uncut a-beams 000_cropped/ times_000.csv 004/<br> - directory containing images of the same counter with the a-beams slit cut 004_cropped/ times_004.csv comparison/<br> - directory containing a selection of cropped images at comparable driving used in the figure fig4 Alternative starting condition/ 001/<br> - directory containing images of the ten-counter compressed in an alternative starting state 001_cropped/<br> - directory with cropped versions of the images in 001/ and further cropped versions in further subdirectories 001_selection/ times_001.csv BBAC machine 006/<br> - directory containing images of the four counters making up the BBAC machine under a driving of BAC 006_state/<br> - selection of images used in the paper with a subdirectory contining cropped versions times_006.csv
DOI: 10.5281/zenodo.8062656
title: Counting and Sequential Information Processing in Mechanical Metamaterials

description: This dataset contains images and driving protocols used in the paper: "Counting and Sequential Information Processing in Mechanical Metamaterials", published in Physical Review Letters. In this paper we demonstrate "beam counters"; metamaterials that count driving cycles. We demonstrate the counters sensitivity to various driving amplitudes and show how this might be used to infer more information from the applied driving and how to construct a "lock and key" metamaterial with an internal state that can only be reached with one unique input sequence.<br> <br> The data in this replication package is hierarchically organized by making use of a directory per figure in the paper. Contained in this replication package are a number of files used in the figure as described below. Experimental data from the measurement setup is stored in matched subdirectorys with a name, for example "001/" and a file "times_001.csv". Here the subdirectory contains images taken of the sample and the csv file contains the times at which the images were taken, the change in pixel intensity from one image to the next (sampled at a shorter interval than the saved images) and the position of the driving stage and a measured inductive position.<br> fig1 001/<br> - directory containing the original unedited figures comparing above and below D* 001_cropped_selection/<br> - directory containing a cropped selection of the images used in the figure times_001.csv fig2 002/<br> - directory containing images of the ten counter being compressed with marked m-beams times_002.csv Kymograph.tif<br> - A kymograph image calculated from 002 by filtering the colored m-beams and taking stacking a single horizontal slice from all of the images. kymograph.svg<br> - The plotted horizontal position of the beam traces visible in Kymograph.tif fig3 000/<br> - directory containing images of the compression of counter with uncut a-beams 000_cropped/ times_000.csv 004/<br> - directory containing images of the same counter with the a-beams slit cut 004_cropped/ times_004.csv comparison/<br> - directory containing a selection of cropped images at comparable driving used in the figure fig4 Alternative starting condition/ 001/<br> - directory containing images of the ten-counter compressed in an alternative starting state 001_cropped/<br> - directory with cropped versions of the images in 001/ and further cropped versions in further subdirectories 001_selection/ times_001.csv BBAC machine 006/<br> - directory containing images of the four counters making up the BBAC machine under a driving of BAC 006_state/<br> - selection of images used in the paper with a subdirectory contining cropped versions times_006.csv
DOI: 10.6084/m9.figshare.24131718
title: Heat energy

description: Heating in a building involves generating and distributing heat to maintain a comfortable indoor temperature. Here's an overview of how heating works in a building:<br>1. **Heat Generation**: - A heat source, such as a boiler, furnace, or electric heating element, produces heat energy. This source is typically fueled by natural gas, oil, electricity, or another energy source.<br>2. **Heat Transfer**: - The heat generated is transferred to a medium, often water or air, through a process like combustion, electrical resistance, or heat exchange.<br>3. **Distribution**: - In a water-based system, hot water is pumped through pipes to radiators or underfloor heating systems. In an air-based system, warm air is circulated through ducts to various rooms.<br>4. **Radiation and Convection**: - Once the heat is in the room, it radiates out from the radiators or heating elements, warming nearby surfaces. Additionally, warm air rises, creating convection currents that help distribute heat throughout the space.<br>5. **Thermostat Control**: - A thermostat monitors the indoor temperature. When it detects a drop below the set temperature, it signals the heating system to start generating more heat.<br>As for calculating heat energy, it involves understanding the principles of heat transfer and the specific characteristics of the building:<br>1. **Heat Load Calculation**: - This involves determining the amount of heat energy required to maintain a comfortable temperature inside the building. Factors include the outdoor temperature, insulation levels, building materials, windows, and occupancy patterns.<br>2. **Specific Heat Capacity**: - This property quantifies how much heat energy is needed to raise the temperature of a substance. For example, water has a specific heat capacity of about 4.18 Joules per gram per degree Celsius.<br>3. **Heat Transfer Equations**: - Different equations are used depending on whether conduction (direct contact), convection (movement of fluid), or radiation (electromagnetic waves) is the dominant mode of heat transfer.<br>4. **Efficiency Considerations**: - The efficiency of the heating system affects how much energy is actually used to achieve the desired level of comfort.<br>Remember, these are simplified explanations, and actual calculations can get quite complex, often involving computer modeling and simulation for precise results. It's also important to consider local building codes and standards when designing or upgrading a heating system.
DOI: 10.5281/zenodo.7778612
title: DUX: A dataset of User Interactions and User Emotions

description: User experience evaluation is becoming increasingly important, and so is emotion recognition. Recognizing users' emotions based on their interactions alone would not be intrusive to users and could be easily implemented in most applications. This is still an area of active research and requires data containing both the user interactions and the corresponding emotions. Currently, there is no public dataset for emotion recognition from keystroke, mouse and touchscreen dynamics. We have created such a dataset for keyboard and mouse interactions through a dedicated user study and made it publicly available for other researchers. This paper examines our study design and the process of creating the dataset. We conducted the study using a test application for travel expense reports with 50 participants. We want to be able to detect predominantly negative emotions, so we added emotional triggers to our test application. However, further research is needed to determine the relationship between user interactions and emotions.
DOI: 10.5281/zenodo.7778611
title: DUX: A dataset of User Interactions and User Emotions

description: User experience evaluation is becoming increasingly important, and so is emotion recognition. Recognizing users' emotions based on their interactions alone would not be intrusive to users and could be easily implemented in most applications. This is still an area of active research and requires data containing both the user interactions and the corresponding emotions. Currently, there is no public dataset for emotion recognition from keystroke, mouse and touchscreen dynamics. We have created such a dataset for keyboard and mouse interactions through a dedicated user study and made it publicly available for other researchers. This paper examines our study design and the process of creating the dataset. We conducted the study using a test application for travel expense reports with 50 participants. We want to be able to detect predominantly negative emotions, so we added emotional triggers to our test application. However, further research is needed to determine the relationship between user interactions and emotions.
DOI: 10.17028/rd.lboro.21513774
title: Dataset for "Enhanced Accumulation of Colloidal Particles in Microgrooved Channels via Diffusiophoresis and Steady-State Electrolyte Flows"

description: Experimental particle concentration fields used to generate the results shown in Fig. 2, Fig. 3 and Fig. 4 of "<em>Enhanced Accumulation of Colloidal Particles in Microgrooved Channels via Diffusiophoresis and Steady-State Electrolyte Flows</em>" by N. Singh, G. Vladisavljevic, F. Nadal, C. Cottin-Bizonne, C. Pirat, G. Bolognesi <br> Capitalized keywords are from Loterre's Chemistry Vocabulary.
DOI: 10.17028/rd.lboro.21513774.v1
title: Dataset for "Enhanced Accumulation of Colloidal Particles in Microgrooved Channels via Diffusiophoresis and Steady-State Electrolyte Flows"

description: Experimental particle concentration fields used to generate the results shown in Fig. 2, Fig. 3 and Fig. 4 of "<em>Enhanced Accumulation of Colloidal Particles in Microgrooved Channels via Diffusiophoresis and Steady-State Electrolyte Flows</em>" by N. Singh, G. Vladisavljevic, F. Nadal, C. Cottin-Bizonne, C. Pirat, G. Bolognesi <br> Capitalized keywords are from Loterre's Chemistry Vocabulary.
DOI: 10.57745/kqsljz
title: DVS dataset.tab

description: This file contains the results of the DVS measurements
DOI: 10.57745/qveidm
title: readme.txt

description: Read me first
DOI: 10.57745/slaboh
title: Rosetta stone-sample name code.tab

description: :unav
DOI: 10.57745/nmzdvh
title: ss-NMR dataset.zip

description: :unav
DOI: 10.57745/ucstoa
title: TD-NMR dataset.zip

description: :unav
DOI: 10.6084/m9.figshare.23462505
title: The efficacy and safety of Capivasertib (AZD5363) in the treatment of patients with solid tumor: a systematic review and meta-analysis of randomized clinical trials

description: To evaluate the clinical efficacy and safety of Capivasertib on patients with solid tumors. Data from four RCTs were pooled to create a systematic review and meta-analysis focusing on Capivasertib-treated patients with solid tumor. Progression-free survival (PFS) and adverse events (AE) were the primary outcomes. A total of 540 individuals from four RCTs were included. The analysis showed that Capivasertib improved PFS for the ITT population with an HR of 0.75 (95% CI = 0.62–0.90, p = 0.002), whereas it did not show improvement in PFS of the PI3K/AKT/PTEN-altered group with an HR = 0.61 (95% CI = 0.32–1.16, p = 0.13). The analysis also showed that Capivasertib improved OS for the ITT population with an HR = 0.61 (95% CI = 0.47–0.78, p = 0.0001). For safety, four studies were included; statistical differences between Capivasertib and placebo were found in discontinuation of Capivasertib due to toxicity or AE (RR = 2.37, 95% CI = 1.37–4.10, p = 0.002). Capivasertib plus chemotherapy or hormonal therapy combination has shown promising antitumor efficacy and promising safety profile in the treatment of individuals with solid tumor.
DOI: 10.6084/m9.figshare.23462505.v2
title: The efficacy and safety of Capivasertib (AZD5363) in the treatment of patients with solid tumor: a systematic review and meta-analysis of randomized clinical trials

description: To evaluate the clinical efficacy and safety of Capivasertib on patients with solid tumors. Data from four RCTs were pooled to create a systematic review and meta-analysis focusing on Capivasertib-treated patients with solid tumor. Progression-free survival (PFS) and adverse events (AE) were the primary outcomes. A total of 540 individuals from four RCTs were included. The analysis showed that Capivasertib improved PFS for the ITT population with an HR of 0.75 (95% CI = 0.62–0.90, p = 0.002), whereas it did not show improvement in PFS of the PI3K/AKT/PTEN-altered group with an HR = 0.61 (95% CI = 0.32–1.16, p = 0.13). The analysis also showed that Capivasertib improved OS for the ITT population with an HR = 0.61 (95% CI = 0.47–0.78, p = 0.0001). For safety, four studies were included; statistical differences between Capivasertib and placebo were found in discontinuation of Capivasertib due to toxicity or AE (RR = 2.37, 95% CI = 1.37–4.10, p = 0.002). Capivasertib plus chemotherapy or hormonal therapy combination has shown promising antitumor efficacy and promising safety profile in the treatment of individuals with solid tumor.
DOI: 10.6084/m9.figshare.23462505.v1
title: The efficacy and safety of Capivasertib (AZD5363) in the treatment of patients with solid tumor: a systematic review and meta-analysis of randomized clinical trials

description: To evaluate the clinical efficacy and safety of Capivasertib on patients with solid tumors. Data from four RCTs were pooled to create a systematic review and meta-analysis focusing on Capivasertib-treated patients with solid tumor. Progression-free survival (PFS) and adverse events (AE) were the primary outcomes. A total of 540 individuals from four RCTs were included. The analysis showed that Capivasertib improved PFS for the ITT population with an HR of 0.75 (95% CI = 0.62–0.90, p = 0.002), whereas it did not show improvement in PFS of the PI3K/AKT/PTEN-altered group with an HR = 0.61 (95% CI = 0.32–1.16, p = 0.13). The analysis also showed that Capivasertib improved OS for the ITT population with an HR = 0.61 (95% CI = 0.47–0.78, p = 0.0001). For safety, four studies were included; statistical differences between Capivasertib and placebo were found in discontinuation of Capivasertib due to toxicity or AE (RR = 2.37, 95% CI = 1.37–4.10, p = 0.002). Capivasertib plus chemotherapy or hormonal therapy combination has shown promising antitumor efficacy and promising safety profile in the treatment of individuals with solid tumor.
DOI: 10.5281/zenodo.8166116
title: Model uncertainty versus variability in the life cycle assessment of commercial fisheries (under review)

description: Datasets and calculation tables from the manuscript: <em>Model uncertainty versus variability in the life cycle assessment of commercial fisheries</em>. The study analyses the life cycle assessment of fish species landed by Danish trawlers, comparing sources of uncertainty (such as modelling approaches) with sources of variability (vessel length and years). These tables contain the datasets and related calculations to replicate the results described in the paper.
DOI: 10.5281/zenodo.8196893
title: CALLISTO Knowledge Graph

description: The CALLISTO knowledge graph represents various domains of knowledge addressed within the EU-funded CALLISTO project. CALLISTO aims to bridge the gap between Copernicus Data and Data and Information Access Service (DIAS) providers and users in different domains by providing AI solutions that effectively add value to large amounts of satellite data. The project focuses on the Earth Observations (EO) domain and its relationship with four Pilot Use Cases (PUCs): 1) Common Agricultural Policy (CAP) monitoring, 2) water quality assessment, 3) air quality assessment and journalism, and 4) land border surveillance. Each PUC addresses a particular domain and contributes multiple datasets to the project, which come in diverse formats (e.g., XML, JSON, CSV). The CALLISTO ontology has been developed to handle this multi-domain data by offering a semantic representation of each domain and the connections between them. The knowledge graph was then generated by mapping the PUCs datasets to the ontology using RDF Mapping Language (RML). Knowledge graphs aid in linking data from various domains, generating new knowledge, and inspecting recurring patterns that can be used in simulation and prediction models (i.e., using artificial intelligence and deep learning algorithms). Another benefit of employing KGs is the ability to link them to other Linked Open Data (LOD) for data integration and analytics. Applications such as geospatial question answering, geospatial data retrieval, and cross-domain semantic data-driven applications could use it as an underlying data source. The CALLISTO knowledge graph is available in an RDF format; it is associated with the CALLISTO ontology. For further details about the ontology, please refer to deliverable D6.1: The CALLISTO ontologies and semantic indexing. The ontology and knowledge graph were developed by the Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS). In collaboration with PUC members, the ontology and knowledge graph were developed and evaluated.
DOI: 10.5281/zenodo.8196894
title: CALLISTO Knowledge Graph

description: The CALLISTO knowledge graph represents various domains of knowledge addressed within the EU-funded CALLISTO project. CALLISTO aims to bridge the gap between Copernicus Data and Data and Information Access Service (DIAS) providers and users in different domains by providing AI solutions that effectively add value to large amounts of satellite data. The project focuses on the Earth Observations (EO) domain and its relationship with four Pilot Use Cases (PUCs): 1) Common Agricultural Policy (CAP) monitoring, 2) water quality assessment, 3) air quality assessment and journalism, and 4) land border surveillance. Each PUC addresses a particular domain and contributes multiple datasets to the project, which come in diverse formats (e.g., XML, JSON, CSV). The CALLISTO ontology has been developed to handle this multi-domain data by offering a semantic representation of each domain and the connections between them. The knowledge graph was then generated by mapping the PUCs datasets to the ontology using RDF Mapping Language (RML). Knowledge graphs aid in linking data from various domains, generating new knowledge, and inspecting recurring patterns that can be used in simulation and prediction models (i.e., using artificial intelligence and deep learning algorithms). Another benefit of employing KGs is the ability to link them to other Linked Open Data (LOD) for data integration and analytics. Applications such as geospatial question answering, geospatial data retrieval, and cross-domain semantic data-driven applications could use it as an underlying data source. The CALLISTO knowledge graph is available in an RDF format; it is associated with the CALLISTO ontology. For further details about the ontology, please refer to deliverable D6.1: The CALLISTO ontologies and semantic indexing. The ontology and knowledge graph were developed by the Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS). In collaboration with PUC members, the ontology and knowledge graph were developed and evaluated.
DOI: 10.5281/zenodo.8340380
title: TUdi-Vinedivers_Vineyard_Soil_Data_Austria

description: Dataset of soil properties from the TUdi/Vinedivers vineyard management experiments in Austria