Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
62
datasets available to search
ShareScore release 0.9.0
Dataset results
62 results for “interconnect”
Data and codes for "A cryogenic electro-optic interconnect for superconducting devices"
<p>Here you find all raw data files and processing Python scripts for plots presented in "A cryogenic electro-optic interconnect for superconducting devices" Amir Youssefi, et.al. Nature Electronics 2021</p> <p> </p>
IFC Submarine Interconnection Projects Data Model
<p>Technical specification of the O&G Subsea Flexible Interconnections IFC Data Model, containing class relationships diagram and data model tables.</p>
Supporting information - A value creation model from science-society interconnections: Components and archetypes
<p>Data protocol and datasets used for the study entitled 'A value creation model from science-society interconnections: Components and archetypes'. </p> <p><strong>Abstract of the paper:</strong></p> <p>The interplay between science and society takes place through a wide range of intertwined relationships and mutual influences that shape each other and facilitate continuous knowledge flows. Stylised consequentialist perspectives on valuable knowledge moving from public science to society in linear and recursive pathways, whilst informative, cannot fully capture the broad spectrum of value creation possibilities. As an alternative we experiment with an approach that gathers together diverse science-society interconnections and reciprocal research-related knowledge processes that can generate valorisation. Our approach to value creation attempts to incorporate multiple facets, directions and dynamics in which constellations of scientific and societal actors generate value from research. The paper develops a conceptual model based on a set of nine value components derived from four key research-related knowledge processes: production, translation, communication, and utilization. The paper conducts an exploratory empirical study to investigate whether a set of archetypes can be discerned among these components that structure science-society interconnections. We explore how such archetypes vary between major scientific fields. Each archetype is overlaid on a research topic map, with our results showing that different archetypes correspond to distinctive topic areas. The paper finishes by discussing the significance and limitations of our results and the potential of both our model and our empirical approach for further research.</p>
Dataset for the article "Can we use seismic reflection data to infer the interconnectivity of fracture networks?"
<p>This package contains the effective stiffness coefficients of the fractured rock samples explored in the paper of Rubino et al. "Can we use seismic reflection data to infer the interconnectivity of fracture networks?".</p>
Data collected from interconnect operated 40'000 hours in SOFC mode
<p>Complete set of data acquired from the characterization of interconnect extracted from SOFC stack operated 40'000 hours</p>
AD ASTRA project - WP4 Characterization of Interconnects extracted from operated SUN stacks
<p>Report on the complete investigation performed on interconnects extracted from SOFC stacks operated from 5'000 to 20'000 hours</p>
The Interconnected Nature of Multiple Threats are Impacting Freshwater Biodiversity
<p><span>Freshwater biodiversity is in crisis across the globe: the significant extraction, modification, and pollution of freshwater resources puts these communities and systems at great risk. Here, using probabilistic network analysis, we show that globally and across all taxonomic groups and geographic regions, threats to freshwater species are interconnected and do not occur in isolation. However, we also find that species in higher risk categories are more acutely threatened by single, dominant threats as compared to species at lower risk of global extinction. Determining when and which species are threatened by isolated threats or a suite of co-occurring threats provides important information for designing effective freshwater conservation strategies.</span></p>
A Versatile and Secure Data Management System API facilitating the Interconnection of any software component and Easy Data Access.
<p>In the context of the EU-funded project PILOTING (No. 871542), a versatile Data Management System (DMS) was deployed and facilitated the easy integration of nine different robotic systems and various payloads and the storing of all the data observations produced during the inspections. The DMS is designed under the DMS-Data Model (DMS-DM) and operates on top of a representational state transfer (REST) application programming interface (API) that provides a simplified way to exchange data through HTTP(S) requests from a client to the server. One of its key advantages is that it provides a great deal of flexibility so the model could accommodate extensions if needed. Data is not tied to resources or methods, so REST can handle multiple types of calls, return different data formats and even change structurally with the correct implementation of hypermedia. <br> It supports CRUD operations via GET, POST, PUT, PATCH, and DELETE HTTP methods and stores the data on a PostgreSQL object-relational database system. The REST API has been created with Python's Django (web framework) and Django REST Framework, a powerful and flexible toolkit for building Web APIs. <br> The constructed document presents the communication endpoints (DMS API’s Uniform Resource Identifiers (URIs)) under which an authorized user can have access to the DMS API and subsequently to the collected data. </p>
The impact of secondary channels on the wetting properties of interconnected hydrophobic nanopores
<p>Data from Molecular Dynamics simulations.</p> <p>Files ending in .xyz are trajectory files and relate to total or partical simulation trajectories. The normal sintax is eqX.xyz where X is the imposed filling of the main cavity. </p> <p>Files ending in .dat are the "measurement" files and are the files required to compute the free energy from the simulation data. The normal sintax is eqX.dat where X is the imposed filling of the main cavity. </p> <p>Files ending in .data are LAMMPS output files. The normal sintax is eqX.data where X is the imposed filling of the main cavity. </p> <p>Other auxiliary files exist, but they are part of the analysis of the data and can be reconstructed with just the above mentioned files.</p> <p>The simulation data for specific configurations of the system is separated in different zipped files (either .tar.gz or .zip). 2A.tar.gz, 4A.tar.gz, 6A.tar.gz, 10.zip and 12.zip correspond to the configurations of lateral channels corresponding to 0.2, 0.4, 0.6,1 and 1.2 nm respectively. empty.zip and no.zip correspond to the configurations where the lateral channels where forcefully left empty (by evacuating the 6 lateral pores) and to the simple cylindrical pore with no lateral channels. all.zip, edges.zip and middle.zip correspond to the configurations of cavities with water molecules on all channel sites, only the external rings and in the middle ring, as described in the supplementary note 7.</p>
Temporally specific patterns of neural activity in interconnected corticolimbic structures during reward anticipation
<p>Temporally specific patterns of neural activity in interconnected corticolimbic structures during reward anticipation</p> <p>Megan E. Young, Camille Spencer-Salmon, Clayton Mosher, Sarita Tamang, Kanaka Rajan, and Peter H. Rudebeck</p> <p>This dataset contains peripheral physiology (heart rate) and single neuron activity data from the paper entitled “Temporally specific patterns of neural activity in interconnected corticolimbic structures during reward anticipation” by Young, Spencer-Salmon and colleagues.</p> <p>The study investigated how neurons in macaque subcallosal anterior cingulate cortex, basolateral amygdala, and rostromedial striatum encoded anticipated reward during Pavlovian and instrumental tasks.</p> <p>Heart rate data were pre-processed using methods described in the paper and were downsampled to 50 Hz for analysis. Neural activity data were pre-processed using steps as described in the paper.</p> <p>The dataset is saved as .MAT files.</p> <p>Files included:</p> <p>‘Pavlovian_task_neurons.mat’ – single neuron data from the Pavlovian task. Each of the 656 rows represents a single neuron and its associated information.</p> <p>‘Instrumental_task_neurons.mat’ – single neuron data from the instrumental task. Each of the 425 rows represents a single neuron and its associated information.</p> <p>“heart_rate.mat” – heart rate data from monkeys D and H.</p> <p><br> File structure and information:</p> <p>Pavlovian_task_neurons.mat</p> <p>Structure “Pavlovian_task_neurons”<br> - “Pavlovian_task_neurons.unit_name” – neuron specific identifier<br> - “Pavlovian_task_neurons.monkeynumber” – subject specific #<br> - “Pavlovian_task_neurons.monkeyname” – subject specific name<br> - “Pavlovian_task_neurons.date” – date on which data were recorded<br> - “Pavlovian_task_neurons.session” – session identifier from date (a-d)<br> - “Pavlovian_task_neurons.channel” – recording channel data recorded from<br> - “Pavlovian_task_neurons.wavemark” – waveform number (a-e)<br> - “Pavlovian_task_neurons.brainarea” – brain area where neuron recorded (SC = subcallosal ACC, AMY = basolateral amygdala, VS = rostromedial striatum).<br> - “Pavlovian_task_neurons.areanum” – # brain area (subcallosal ACC = 1, BLA = 2, rostromedial striatum = 3)<br> - “Pavlovian_task_neurons.condition” – condition # from Monkey Logic for each of the trials (1 by n trials)<br> - “Pavlovian_task_neurons.stimspikes” – smoothed spike rate from -600 to 2500ms after stimulus onset (trials by time matrix)<br> - “Pavlovian_task_neurons.rewardspikes” – smoothed spike rate from -600 to 2500ms after reward onset (trials by time matrix)<br> - “Pavlovian_task_neurons.stimID” – stimulus shown on that trial (1 = neutral, 3 = CS+ juice, 4 = CS+ water, 5 = CS-) (1 by n trials).</p> <p><br> Instrumental_task_neurons.mat</p> <p>Structure “Instrumental_task_neurons”<br> - “instrumental_task_neurons.unit_name” – neuron specific identifier<br> - “instrumental_task_neurons.monkeynumber” – subject specific #<br> - “instrumental_task_neurons.monkeyname” – subject specific name<br> - “instrumental_task_neurons.date” – date on which data were recorded<br> - “instrumental_task_neurons.session” – session identifier from date (a-d)<br> - “instrumental_task_neurons.channel” – recording channel data recorded from<br> - “instrumental_task_neurons.wavemark” – waveform number (a-e)<br> - “instrumental_task_neurons.brainarea” – brain area where neuron recorded (SC = subcallosal ACC, AMY = basolateral amygdala, VS = rostromedial striatum).<br> - “instrumental_task_neurons.areanum” – # brain area (subcallosal ACC = 1, BLA = 2, rostromedial striatum = 3)<br> - “instrumental_task_neurons.condition” – condition 7-18 from Monkey Logic for each of the trials (1 by n trials). CNDs 7,8,13,14= CS+ juice vs CS+ water; CNDs 9,10,15,16= CS+ juice vs CS-; CNDs 11,12,17,18= CS+ water vs CS-.<br> - “instrumental_task_neurons.choice” – outcome associated with chosen option (0=nothing, 1=juice, 2=water) (1 by n trials)<br> - “instrumental_task_neurons.unchosen” - outcome associated with unchosen option (0=nothing, 1=juice, 2=water) (1 by n trials)<br> - “instrumental_task_neurons.chosenside” – side of the screen chosen (left [0] or right [1]) (1 by n trials)<br> - “instrumental_task_neurons.stimspikes” – smoothed spike rate from -600 to 2500ms after stimulus onset (trials by time matrix)<br> - “instrumental_task_neurons.rewardspikes” – smoothed spike rate from -600 to 2500ms after reward onset (trials by time matrix)</p> <p>heart_rate.mat</p> <p>Structures – “monkey_d_hr” – monkey D heart rate data<br> – “monkey_h_hr” – monkey H heart rate data</p> <p>Structure of monkey_d/h_hr<br> - “monkey_d/h_hr.trial_type” – trial type presented (1 = neutral, 2 = unsignaled, 3 = CS+ juice, 4 = CS+ water, 5 = CS-) (1 by n trials).<br> - “monkey_d/h_hr.trials” – number of trials in each session by trial type<br> - “monkey_d/h_hr.session – percent change in heart rate for each trial from -200 ms to 3500ms after stimulus onset. Column 1 = session; Column 2 = trial type; Column 3 = trial number; Columns 4 – 3703 = percent change in heart rate.</p> <p> </p>
Modeling Attack Resistant Strong PUF Exploiting Stagewise Obfuscated Interconnections With Improved Reliability [DATASET and Source Code]
<p>Thanks for your interest in our work!</p> <p>In order to facilitate your assessment and replication, we provides the dataset and source codes (verilog/python model/matlab) of our work (OIPUF) here. </p> <p>By the way, our latest work (SOI PUF and cSOI PUF) published in IEEE TIFS (2024) is based on OIPUF. </p> <blockquote> <p>If you have any questions, please feel free to contact with us: <a href="mailto:chongyaoxu@126.com">chongyaoxu@126.com</a> / <a href="mailto:mklaw@um.edu.mo">mklaw@um.edu.mo</a></p> <p>Full text about OIPUF can be downloaded from <a href="https://ieeexplore.ieee.org/document/10103139">https://ieeexplore.ieee.org/document/10103139</a></p> <p>Full text about SOI PUF and cSOI PUF can be downloaded from <a href="https://ieeexplore.ieee.org/document/10458688">https://ieeexplore.ieee.org/document/10458688</a></p> <p>Source code and FPGA project of SOI PUF and cSOI PUF can be download from <a href="https://github.com/yg99992/SOI_PUF">https://github.com/yg99992/SOI_PUF</a>. </p> </blockquote> <p> </p> <p>Matlab code</p> <p><code>matlab/Generate_OI_block.m</code><br>This is a matlab manuscript used for generating the verilog code of random OI block.</p> <p><code>matlab/OIPUF_64x4_placement.m</code><br>This is a matlab function used for generating XDC file for constraining the placement of (64,4)-OI block</p> <p><code>matlab/OIPUF_64x8_placement.m</code><br>This is a matlab function used for generating XDC file for constraining the placement of (64,8)-OI block</p> <p><code>matlab/OIPUF_placement_example.m</code><br>An example manuscript used for demonstrating the usage of OIPUF_64x4_placement.m and OIPUF_64x8_placement.m</p> <p> </p> <p>Python code</p> <p><code>python/puf_models.py</code><br>The python models of XOR PUFs and OIPUFs, which can be used to generate CRPs.</p> <p>for example:</p> <pre><code>from puf_models import oi_puf # generate a (64,4)-OIPUF and further use the generated OIPUF to generate 1M CRPs crps, puf_instance = oi_puf.gen_CRPs_PUF(64, 4, 1_000_000) </code></pre> <p> </p> <p><code>python/attack_pypuf.py</code><br>A manuscript used to conduct to ANN attack on XOR PUF and OIPUF ('pypuf' package should be installed correctly).</p> <p> </p> <p>Verilog code</p> <p><code>verilog/OIPUF_64_4/</code><br>All the verilog files of (64, 4)-OIPUF</p> <p><code>verilog/OIPUF_64_8/</code><br>All the verilog files of (64, 8)-OIPUF</p> <p> </p> <p>CRP datasets extracted from FPGA</p> <p>It consists of 13 CRP files (All the CRPs are extracted from FPGA):</p> <p><code>FPGA_CRPs/FPGA3_CHAL_100M.csv</code><br>The 100 million 64-bit challenges</p> <p><code>FPGA_CRPs/FPGA3_k4_PUF0.csv</code><br>The 100 million 1-bit responses extracted from (64,4)-OIPUF0</p> <p><code>FPGA_CRPs/FPGA3_k4_PUF1.csv</code><br>The 100 million 1-bit responses extracted from (64,4)-OIPUF1</p> <p><code>FPGA_CRPs/FPGA3_k4_PUF2.csv</code><br>The 100 million 1-bit responses extracted from (64,4)-OIPUF2</p> <p><code>FPGA_CRPs/FPGA3_k4_PUF3.csv</code><br>The 100 million 1-bit responses extracted from (64,4)-OIPUF3</p> <p><code>FPGA_CRPs/FPGA3_k4_PUF4.csv</code><br>The 100 million 1-bit responses extracted from (64,4)-OIPUF4</p> <p><code>FPGA_CRPs/FPGA3_k4_PUF5.csv</code><br>The 100 million 1-bit responses extracted from (64,4)-OIPUF5</p> <p><code>FPGA_CRPs/FPGA3_k8_PUF0.csv</code><br>The 100 million 1-bit responses extracted from (64,8)-OIPUF0</p> <p><code>FPGA_CRPs/FPGA3_k8_PUF1.csv</code><br>The 100 million 1-bit responses extracted from (64,8)-OIPUF1</p> <p><code>FPGA_CRPs/FPGA3_k8_PUF2.csv</code><br>The 100 million 1-bit responses extracted from (64,8)-OIPUF2</p> <p><code>FPGA_CRPs/FPGA3_k8_PUF3.csv</code><br>The 100 million 1-bit responses extracted from (64,8)-OIPUF3</p> <p><code>FPGA_CRPs/FPGA3_k8_PUF4.csv</code><br>The 100 million 1-bit responses extracted from (64,8)-OIPUF4</p> <p><code>FPGA_CRPs/FPGA3_k8_PUF5.csv</code><br>The 100 million 1-bit responses extracted from (64,8)-OIPUF5</p>
Data from: 'ILSM': A package to analyze the interconnection structure of tripartite interaction networks
Open the record for dataset details and reuse information.
GCAM-USA Raw Outputs for Khan et al. 2021 - Evolution of energy-water-agriculture interconnectivity across the U.S.
<p>GCAM-USA outputs for paper: Khan et al. 2021, Evolution of energy-water-agriculture interconnectivity across the U.S.</p>
Data from: The impact of conservation management on the community composition of multiple organism groups in eutrophic interconnected man-made ponds
Ponds throughout the world are subjected to a variety of management measures for purposes of biodiversity conservation. Current conservation efforts typically comprise a combination of multiple measures that directly and indirectly impact a wide range of organism groups. Knowledge of the relative impact of individual measures on different taxonomic groups is important for the development of effective conservation programs. We conducted a field study of 28 man-made ponds, representing four management types differing in the frequency of periodic pond drainage and the intensity of fish stock management. We disentangled the relative importance of direct and indirect effects of pond management measures on the community composition of phytoplankton, zooplankton, aquatic macro-invertebrates, submerged and emergent vascular plants. With the exception of phytoplankton, pond management had strong effects on the community composition of all investigated biota. Whether management affected communities directly or indirectly through its impact on fish communities or local environmental conditions in the pond varied between organism groups. Overall, the impact of pond drainage regime and fish community characteristics on the community composition of target organism groups were more important than local environmental conditions. The majority of taxa were negatively associated with fish density, whereas multiple emergent plant species and several taxa of aquatic macro-invertebrates were positively affected by increased drainage frequency. The effects of fish community and drainage tended to be largely independent. The present study indicates that pond drainage is an important element for biodiversity conservation in eutrophicated shallow and interconnected man-made ponds.
Integrating the interconnections between groundwater and land surface processes through the coupled NASA Land Information System and ParFlow environment
<p>This is a dataset used in the paper entitled "Integrating the interconnections between groundwater and land surface processes through the coupled NASA Land Information System and ParFlow environment" by Maina et al., 2024</p>
Data set for A Novel VNS-based Algorithm for SVC Allocation in the Brazilian Interconnected Power System
<p>This release includes the 107-bus version of the Brazilian Interconnected Power System (available <a href="https://www.sistemas-teste.com.br/">here</a>). The system consists of 107 buses, 104 lines, and 67 transformers distributed across three areas: South, Southeast, and Mato Grosso. This test system provides extensive applications for problems related to steady-state analysis.</p>
Feeding-state dependent neuropeptidergic modulation of reciprocally interconnected inhibitory neurons biases sensorimotor decisions in Drosophila
<p>Raw and source data related to the study "Feeding-state dependent neuropeptidergic modulation of reciprocally interconnected inhibitory neurons biases sensorimotor decisions in Drosophila"</p> <p>Please not that protein-deprived (pd) and sucrose annotation are used interchangeabley in the dataset</p> <p> </p>
Mongird et al. (2022), interconnection vulnerability, water, and energy interdependency data
<p>Data files supporting Mongird et al. (2022).</p>
Dataset of "Detecting Weaknesses and Analyzing Cascading Failures in Critical Infrastructure Networks Using an Interconnected Simulation System"
<p>This article introduces an innovative simulator for critical infrastructure, developed to enable comprehensive<br>simulation of data and power grid and their interconnections. The simulator offers user-input functionality,<br>allowing for detailed modelling and analysis of various impact scenarios on infrastructures. A key feature<br>of this tool is the use of a state matrix to represent the current state of infrastructures, which is updated<br>after each simulation. This update facilitates accurate and dynamic modelling of changes in infrastructure<br>networks and their interconnections.<br>The simulator is designed to test the functionality of both individual components and the overall network<br>integrity, including the analysis of the cascading failures, where a failure in one part can impact other parts<br>of the infrastructure. This capability is essential for a deeper understanding of risks and for developing<br>effective strategies to protect and ensure the resilience of critical infrastructures. The simulator represents a<br>significant advancement in the field of critical infrastructure simulation, providing a tool for better prediction,<br>identification, and mitigation of potential threats, thereby enhancing the security and resilience of critical<br>systems.</p>
Data from the article "Robust Photocatalytic MICROSCAFS® with Interconnected Macropores for Sustainable Solar-Driven Water Purification" https://doi.org/10.3390/ijms25115958
<p>Version 2 - the units of the apparent kinetic rate constants in the flow reactor kinetic models were corrected.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.