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opencc-zeroJun 2021View details →

A lightweight backpack harness for tracking hummingbirds

Hummingbirds and other lightweight bird species are challenging to track because they have limited capacity to carry devices for data-logging. We present a simple and customizable three-loop 'backpack' harness for studying hummingbird migration and movement, with step-by-step instructions for harness construction and attachment. The harness has negligible weight and cost (< $0.50 USD/each), is easy for a single person to make and apply in the field, and it requires no complicated setup or equipment. We have field-tested this harness on 74 Giant Hummingbirds (Patagona gigas) with three different types of tracking devices (geolocators, GPS tags, and satellite transmitters) in Chile and Peru from 2017–2020. Based on recaptures to date, we report that harnesses last at least two years, even under high UV-light conditions. We found no evidence of adverse effects of the harnesses on birds after one to two years and apparent survival of geolocator-tracked Giant Hummingbirds was in line with published estimates for other hummingbird species. This harness method is a practical and effective option for mounting ultra-light devices on hummingbirds, and it can be readily modified for other species with short legs, prominent sternal keels, and long wings.

zenodo40/100

Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition using Wrist-worn Inertial Sensors

In this paper we present a benchmark dataset for evaluation of physical human activity recognition from wrist-worn sensors, for the specific setting of basketball training, drills, and games. Basketball activities lend themselves well for measurement by wrist-worn inertial sensors, and systems that are able to detect such sport-relevant activities could be used in applications toward game analysis, guided training, and personal physical activity tracking. The dataset was recorded for two teams from separate countries (USA and Germany) with a total of 24 players who wore an inertial sensor on their wrist and spanned both repetitive basketball training sessions and full games. Particular features of this dataset include an inherent variance through cultural differences in game rules and styles as the data was recorded in two countries, as well as different sport skill levels, since the participants were heterogeneous in terms of prior basketball experience. We illustrate the datasets' features in several time-series analyses and report on a baseline classification performance study with a state-of-the-art deep learning architecture.

opencc-by-4.0May 2023View details →

zenodo40/100

REMODEL. WP5. Cable Manipulation Planning, Execution and Interactive Perception. T5-4. Wiring harness manipulation. Data related to a paper for the conference CoDIT 2023

The datasets contain data recorded during the experiment reported in the paper:A.  Govoni, G. Laudante, M. Mirto, C. Natale, and S. Pirozzi (2023). "Towards the automation of wire harness manufacturing: a robotic manipulator with sensorized fingers". In: 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT)

opencc-by-4.0Oct 2023View details →

publicJul 2021View details →

A lightweight backpack harness for tracking hummingbirds

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publicDec 2023View details →

Data from: harnessing the power of regional baselines for broad-scale genetic stock identification: a multistage, integrated, and cost-effective approach

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publicJun 2025View details →

Data from: Harnessing natural embodied intelligence for spontaneous jellyfish cyborgs

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opencc-byNov 2020View details →

Medieval Horse Harness Frag, Castilla-La Mancha

Cross-shaped horse browband from the site of Calatrava la Vieja, Carrión de Calatrava, Castilla-La Mancha, Spain. Middle Ages. In the collection of the Ciudad Real Museum. Catalog number DO000154. Processed in Reality Capture from 594 images. Christian period (13th century) Citation: Proyecto Dorados. Tecnología, conservación y restauración de los metales dorados medievales, 2010: 36-37 This work is part of the collaboration agreement signed in 2020 between the Junta de Comunidades de Castilla-La Mancha and Global Digital Heritage for the digitization of the Cultural Heritage of Castilla-La Mancha region. We want to publicly thank the collaboration of the Museum of Ciudad Real, especially by its director José Ignacio de la Torre. Source: Objaverse 1.0 / Sketchfab

dryad36/100

Harnessing waterfleas for water reclamation: A nature-based tertiary wastewater treatment technology

Urbanisation, population growth, and climate change have put unprecedented pressure on water resources, leading to a global water crisis and the need for water reuse. However, water reuse is unsafe unless persistent chemical pollutants are removed from reclaimed water.. State-of-the-art technologies for the reduction of persistent chemical pollutants in wastewater typically involves high operational and energy costs and potentially generates toxic by-products (e.g., bromate from ozonation). Nature-base solutions are preferred to these technologies for their lower environmental impact. However, so far, bio-based tertiary wastewater treatments have been inefficient for industrial-scale applications. Moreover, they often demand significant financial investment and large infrastructure, undermining sustainability objectives. Here, we present a scalable, low-cost, low-carbon, and retrofittable nature-inspired solution that could be retrofitted into current wastewater treatment systems to remove persistent chemical pollutants. The technology uses the water flea Daphnia to non-selectively uptake and retain persistent chemical pollutants (pharmaceutical, pesticides and industrial chemicals). We showed Daphnia's removal efficiency at laboratory scale ranging between 50% for PFOS and 90% for diclofenac. We validate the removal efficiency of diclofenac at prototype scale showing sustained performance over four weeks in outdoor seminatural conditions. A techno-commercial analysis on the Daphnia-based technology suggests several technical, commercial and sustainability advantages over established and emerging treatments at comparable removal efficiency, benchmarked on available data on individual chemicals. Further testing of the technology is underway in open flow environments holding real wastewater. The technology has the potential to improve the quality of wastewater effluent meeting requirements to produce water appropriate for reuse in irrigation, industrial application, and household use. By preventing persistent chemicals from entering waterways, this technology has the potential to maximise the shift to clean growth, enabling water reuse, reducing resource depletion and preventing environmental pollution.

opencc-zeroMar 2024View details →

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how. To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field. Data : 1. Table1_full_version.docx : Marker genes for cell clusters of global Seurat analysis 2. Table2_full_version.docx : List of the Immgen samples used to generate the reference compendium for CMAP signature generation 3. Table5_full_version.docx : Top 20 marker genes for cell clusters of the Seurat analysis on selected cDC1s

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how. To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.   Data: 1. Immgen_cell_types.cls : Microarray Phase 1 expression 2. Immgen_norm_exp_data.gct : Microarray Phase 1 class

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how. To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field. Data: cDC1_maturation_loom_file.rds : Loom file used for RNA Velocity Analysis

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how. To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field. Data:  MDAlab_cDC1_maturation.tar : Docker image used for the analysis

opencc-by-4.0Jun 2021View details →

Tell Me, What Do You See?—Interpretable Classification of Wiring Harness Branches with Deep Neural Networks- dataset

Dataset associated with the paper "Tell Me, What Do You See?—Interpretable Classification of Wiring Harness Branches with Deep Neural Networks"

opencc-zeroDec 2020View details →

Parade harness for a horse

Inventory number: MT-E/1430/2 a District Museum in Tarnów https://muzea.malopolska.pl/en/objects-list/1415 Source: Objaverse 1.0 / Sketchfab

opencc-byMar 2022View details →

Wmid-3a538c - Harness fitting

A copper alloy probable harness fitting dating to the Late Iron Age/Early Roman period circa 100 BC to 100 AD. The object has a slight curvature in profile. The upper face is slightly convex and the reverse is flat. It consists of a circular perforated loop at one end. This projects at 90 degrees from a larger broadly oval shaped loop. Below the oval loop is a triangular loop that then extends to a circular loop. Extending from the circular loop appears to be a further now incomplete triangular loop. This could suggest that originally the circular loop was once in the centre and the remaining triangular and oval loops were mirrored on the other side. The object has a well developed dark brown coloured smooth patina that has also developed over the broken terminal. For more details, please visit the database record available at: https://finds.org.uk/database/artefacts/record/id/1053202 Source: Objaverse 1.0 / Sketchfab

opencc-by-4.0Apr 2024View details →

Harnessing interactions between traits and the environment to improve predictions of ecosystem functioning

The data correspond to the manuscript entitled "Harnessing interactions between traits and the environment to improve predictions of ecosystem functioning". Data needed to reproduce the figure 2 about springtails colonisation of defaunated soil blocks, from two different environments (forest or meadow), with the springtail species classified according to the supposed dispersal ability. Species with long legs and antennae, a well-developed jumping apparatus (furcula), and a complete visual apparatus were considered to be able to disperse more rapidly on their own and were thus categorized as “fast” dispersers. Other species with reduced locomotor and vision organs were categorized as “slow” dispersers (Ponge et al. 2006a). The "number of individuals" corresponds to the number of individuals found in each defaunated soil block after one week in the initial data set (Auclarc et al., 2009). Ponge J-F, Dubs F, Gillet S, et al. 2006a. Decreased biodiversity in soil springtail communities: the importance of dispersal and landuse history in heterogeneous landscapes. Soil Biol Biochem 38: 1158–61. Auclerc A, Ponge JF, Barot S, and Dubs F. 2009. Experimental assessment of habitat preference and dispersal ability of soil springtails. Soil Biol Biochem 41: 1596–604.

opencc-by-4.0Sep 2024View details →

Postseismic Deformation Due to the 2021 MW 7.4 Maduo (China) Earthquake and Implications for Regional Rheology and Seismic Hazards around the Bayan Har block

input.sh:  Model input file in RELAX to simulate the coupled afterslip and viscoelastic contributions in the study of Tian et al. (2024), EPSL ****.txt: Observed postseismic time series following the 2021 MW 7.4 Maduo (China) Earthquake   Title: Postseismic Deformation Due to the 2021 MW 7.4 Maduo (China) Earthquake and Implications for Regional Rheology and Seismic Hazards around the Bayan Har block

opencc-by-4.0Aug 2024View details →

Supporting information and Figures of the article "Harnessing the Biomimetic Effect of Macromolecular Crowding in the Cell-Derived Model of Clubfoot Fibrosis"

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