Find research datasets worth reusing

opencc-by-4.0Nov 2018View details →

Fig. 1 in Two hundred years in the dark: A type for the moss Encalypta crispata

Fig. 1. – The rediscovered holotype of Hedwig's Encalypta crispata Hedw at G.

opencc-zeroNov 2021View details →

Data from: From microbes to mammals: pond biodiversity homogenization across different land-use types in an agricultural landscape

Local biodiversity patterns are expected to strongly reflect variation in topography, land use, dispersal boundaries, nutrient supplies, contaminant spread, management practices and other anthropogenic influences. In contrast, studies focusing on specific taxa revealed a biodiversity homogenization effect in areas subjected to long-term intensive industrial agriculture. We investigated whether land use affects biodiversity levels and community composition (α & β diversity) in 67 kettle holes (KH) representing small aquatic islands embedded in the patchwork matrix of a largely agricultural landscape comprising grassland, forest, and arable fields. These KH, similar to millions of standing water bodies of glacial origin, spread across northern Europe, Asia, and North America, are physico-chemically diverse, differ in the degree of coupling with their surroundings. We assessed biodiversity patterns of eukaryotes, Bacteria and Archaea in relation to environmental features of the KH, using deep-amplicon-sequencing of environmental DNA (eDNA). First, we asked whether deep sequencing of eDNA provides a representative picture of KH biodiversity across the Bacteria, Archaea, and Eukaryotes. Second, we investigated if and to what extent KH biodiversity is influenced by the surrounding land-use. Our data shows that deep eDNA amplicon sequencing is useful for in-depth assessments of cross-domain biodiversity comprising both micro- and macro-organisms, but, has limitations with respect to single-taxa conservation studies. Using this broad method, we show that sediment eDNA, integrating several years to decades, depicts the history of agricultural land-use intensification. The latter, coupled with landscape wide nutrient enrichment (including by atmospheric deposition), groundwater connectivity between KH and organismal (active and passive) dispersal in the tight network of ponds, resulted in a biodiversity homogenization in the KH water, levelling off today's detectable differences in KH biodiversity between land-use types.

opencc-zeroNov 2021View details →

Mercury accumulation in leaves of different plant types – the significance of tissue age and specific leaf area

Mercury, Hg, is one of the most problematic metals from an environmental perspective. To assess the problems caused by Hg in the environment it is crucial to understand the processes of Hg biogeochemistry, but the exchange of Hg between the atmosphere and vegetation is not sufficiently well characterised. We explored the mercury concentration, [Hg], in foliage from a diverse set of plant types, locations and sampling periods to study whether there is a continuous accumulation of Hg in leaves/needles over time. Measurements of [Hg] were made in deciduous and conifer trees in Gothenburg, Sweden (Botanical Garden and city area) as well as of evergreen trees in Rwanda. In addition, data for wheat from an ozone experiment conducted at Östad, Sweden, were included. Conifer data were quantitatively compared with literature data. In every case where older foliage was directly compared with younger, [Hg] was higher in older tissue. Covering the range of current year up to four-year old needles, there was no sign of Hg saturation in conifer needles with age. Thus, over time scales of approximately one month to several years, the Hg uptake in foliage from the atmosphere always dominated over Hg evasion. Rwandan broadleaved trees had generally older leaves due to lack of seasonal abscission and higher [Hg] than Swedish broadleaved trees. The significance of atmospheric Hg uptake in plants was shown in a wheat experiment where charcoal filtrated air lead to significantly lower leaf [Hg]. To search for general patterns, the accumulation rates of Hg in the diverse set of tree species in the Gothenburg area were related to the specific leaf area (SLA). Leaf area based [Hg] was strongly negatively and non-linearly correlated with SLA, while mass-based [Hg] had a somewhat weaker positive relationship with SLA (both relationships with p < 0.001). An elaborated understanding of the relationship behind [Hg] and SLA would support large-scale modelling of Hg uptake by vegetation and Hg circulation in general.

opencc-by-4.0Dec 2020View details →

Fig. 2. Ephemera glaucops F.-J. Pictet, 1843 in Mayfly types and additional material (Insecta: Ephemeroptera) examined by F.-J. Pictet and A.-E. Pictet, housed in the Museums of Natural History of Geneva and Vienna

Fig. 2. Ephemera glaucops F.-J. Pictet, 1843, labels of one male imago syntype.

opencc-by-4.0Dec 2020View details →

Fig. 8 in Mayfly types and additional material (Insecta: Ephemeroptera) examined by F.-J. Pictet and A.-E. Pictet, housed in the Museums of Natural History of Geneva and Vienna

Fig. 8. Teloganopsis hispanica (Eaton, 1888). Male imago holotype. Scale bar: 1 mm.

opencc-by-4.0Dec 2020View details →

Fig. 10 in Mayfly types and additional material (Insecta: Ephemeroptera) examined by F.-J. Pictet and A.-E. Pictet, housed in the Museums of Natural History of Geneva and Vienna

Fig. 10. Rhithrogena cincta Navás, 1921. Male imago in lateral view. Scale bar: 1 mm.

opencc-by-4.0Nov 2020View details →

scPretrain: Multi-task self-supervised learning for cell type classification

The dataset and code for paper, scPretrain: Multi-task self-supervised learning for cell type classification.

opencc-zeroDec 2021View details →

Bryosphere loss impairs litter decomposition consistently across moss species, litter types, and micro-arthropod abundance

The bryosphere (i.e., ground mosses and their associated biota) is a key driver of nutrient and carbon dynamics in many terrestrial ecosystems, in part because it regulates litter decomposition. However, we have a poor understanding of how litter decomposition responds to changes in the bryosphere, including changes in bryosphere cover, moss species, and bryosphere-associated biota. Specifically, the contribution of micro-arthropods to litter decomposition in the bryosphere is unclear. Here, we used a 16-month litterbag field experiment in two boreal forests to investigate bryosphere effects on litter decomposition rates among two moss species (Pleurozium schreberi and Hylocomium splendens), and two litter types (higher-quality Betula pendula litter and lower-quality P. schreberi litter). Additionally, we counted all micro-arthropods in the litterbags and identified them to functional groups. We found that bryosphere removal reduced litter decomposition rates by 28% and micro-arthropod abundance by 29%, and led to a colder micro-climate. Litter decomposition rates and micro-arthropod abundance were uncorrelated overall, but were positively correlated in B. pendula litterbags. Bryosphere effects on litter decomposition rates were consistent across moss species, litter types, and micro-arthropod abundances and community compositions. These findings suggest that micro-arthropods play a minor role in litter decomposition in the boreal forest floor, suggesting that other factors (e.g., micro-climate, nutrient availability) likely drive the positive effect of the bryosphere on decomposition rates. Our results point to a substantial and consistent impairment of litter decomposition in response to loss of moss cover, which could have important implications for nutrient and carbon cycling in moss-dominated ecosystems.

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-zeroJan 2022View details →

Relative forelimb-hindlimb investment is associated with flight style, foraging strategy, and nestling period, but not nest type

We investigated Dial's 2003 hypothesis that birds that rely more heavily upon flight as their primary mode of locomotion and thus invest more in their forelimbs than hindlimbs will experience selection for smaller body sizes, greater altriciality, and more complex nests. To test this hypothesis, we examined the skeletons of over 2,000 individuals from 313 species representing the majority of avian families and all major branches of the avian tree. We used the lengths of the sternal keel and long bones of the wing relative to the lengths of the leg long bones as an index of relative locomotor investment. We found that locomotor investment was predicted by flight style, foraging method, and length of nestling period, supporting Dial's hypothesis. Soaring birds and birds with more acrobatic flight styles, birds whose foraging methods were heavily reliant upon flight, and whose young spent more time in the nest tended to invest more in their forelimbs relative to hindlimbs. Nest type and body size were not significant predictors of relative forelimb-hindlimb investment, however, suggesting that the relationships among flight style, locomotor investment, and life history are not as tightly intertwined as Dial originally hypothesized.

Dataset for a manuscript entitled Variation in CO2 and CH4 Fluxes Among Land Cover Types in Heterogeneous Arctic Tundra in Northeastern Siberia

Dataset for the manuscript Variation in CO2 and CH4 Fluxes Among Land Cover Types in Heterogeneous Arctic Tundra in Northeastern Siberia authored by Sari Juutinen, Mika Aurela, Juha-Pekka Tuovinen, Viktor Ivakhov, Maiju Linkosalmi, Aleksi Räsänen, Tarmo Virtanen, Juha Mikola, Johanna Nyman, Emmi Vähä, Marina Loskutova, Alexander Makshtas, and Tuomas Laurila Dataset consists of CO2 and CH4 flux data measured mainly using chamber method in arctic tundra

Contemporary Wooden Watercraft of the Zanzibar Channel, Tanzania: Type and Technology, Continuity, and Innovation. Photogrammetry Files.

This upload contains the photogrammetry files of 9 wooden vessels discussed in the paper 'Contemporary Wooden Watercraft of the Zanzibar Channel, Tanzania: Type and Technology, Continuity, and Innovation' published in the International Journal of Nautical Archaeology (IJNA).  The paper documents and presents the range of wooden fishing and cargo-carrying watercraft in use in the contemporary Zanzibar Channel, Tanzania. The work is based on surveys conducted in 2018 on Unguja, the principal island of the Zanzibar archipelago, and in 2019– 2020 in Bagamoyo, mainland Tanzania, and its immediate environs. The authors present a broad typology of the principal wooden vessel types in the form of 3D photogrammetric models, orthographic views, and photographs. The principal construction features and uses of each type are presented and the state of play of contemporary wooden-boat construction in this part of East Africa discussed.

Utilizing Hydrothermal Processing to Align Structure and In Vitro Digestion Kinetics between Three Different Pulse Types

The data used for the graphs in the paper: Pälchen, K.; Van den Wouwer, B.; Duijsens, D.; Hendrickx, M.E.; Van Loey, A.; Grauwet, T. Utilizing Hydrothermal Processing to Align Structure and In Vitro Digestion Kinetics between Three Different Pulse Types. Foods 2021, 11, 206. https://doi.org/10.3390/foods11020206 Abstract Processing results in the transformation of pulses’ structural architecture. Consequently, digestion is anticipated to emerge from the combined effect of intrinsic (matrix-dependent) and extrinsic (processed-induced) factors. In this work, we aimed to investigate the interrelated effect of intrinsic and extrinsic factors on pulses’ structural architecture and resulting digestive consequences. Three commercially relevant pulses (chickpea, pea, black bean) were selected based on reported differences in macronutrient and cell wall composition. Starch and protein digestion kinetics of hydrothermally processed whole pulses were assessed along with microstructural and physicochemical characteristics and compared to the digestion behavior of individual cotyledon cells isolated thereof. Despite different rates of hardness decay upon hydrothermal processing, the pulses reached similar residual hardness values (40 N). Aligning the pulses at the level of this macrostructural property translated into similar microstructural characteristics after mechanical disintegration (isolated cotyledon cells) with comparable yields of cotyledon cells for all pulses (41–62%). We observed that processing to equivalent microstructural properties resulted in similar starch and protein digestion kinetics, regardless of the pulse type and (prolonged) processing times. This demonstrated the capacity of (residual) hardness as a food structuring parameter in pulses. Furthermore, we illustrated that the digestive behavior of isolated cotyledon cells was representative of the digestion behavior of corresponding whole pulses, opening up perspectives for the incorporation of complete hydrothermally processed pulses as food ingredients.

Reanalysed JLA Type Ia Supernovae Data with 2M++ Density Field

These are the data products to supplement Rahman et al. 2021 here https://arxiv.org/abs/2108.12497 The covariance file statssys_JLA_A2_no_colour_off_diag.gz contains the new peculiar velocity corrections and removes the off-digonal colour correlations to be consistent with the selection effects methodlogy applied in Rahman et al. 2021 If you want the color covariances to be included because you want to ignore the selection effects analysis in the paper or apply your own, this version of the covariance matrix is called statssys_JLA_A2.gz     nb: for each 3 x 3 sub matrix, the order of the covariance matrix is color, x1, mb on the diagonal.

Supporting information for v2 of ``A Transparent Window into Early-Type Stellar Variability''

These folders contain grids of main-sequence stellar models computed four different metallicities and masses varying from 0.1-60 MSun. Models were computed using MESA r15140 following commit `964e07d` in this GitHub repo: https://github.com/adamjermyn/conv_trends. Changes from the previous version include: - Fixed a bug in calculating certain averages over the convection zone. - Incorporated the effects of radiation pressure on the Rayleigh number. - Increased time resolution of models.   The metallicities are as follows: main_Z_time_2022_01_18_13_56_00_sha_ef8c - Z = 0.002 (SMC) main_Z_time_2022_01_18_13_56_06_sha_adbd - Z = 0.006 (LMC) main_Z_time_2022_01_18_13_56_13_sha_e814 - Z = 0.01 main_Z_time_2022_01_18_13_56_19_sha_f7ec - Z = 0.014 (MW)

Tables S1 and S2. Structural Diversity, Biosynthesis, and Function of Plant Falcarin-type Polyactylenic Lipids

Table S1. Compounds and structures used to generate structural similarity network in Figure 2. Table S2. List of functional FAD2s used to generate Figures 4 and 5.

The Dark Energy Survey 5-year photometrically identified Type Ia Supernovae classification probabilities with SuperNNova

Classification probabilities obtained for the Dark Energy Survey 5-year photometrically identified SN Ia. Probabilities were obtained with SuperNNova (Möller et al. 2020) using multi-band light-curves and host galaxy redshifts. The classification models were trained to disentangle type Ia vs. non Ia supernovae.