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.
13,618
datasets available to search
ShareScore release 0.7.1
Dataset results
13,618 results for “biology”
Fig. 9 in Taxonomy, systematics and biology of the Australian halotolerant wolf spider genus Tetralycosa (Araneae: Lycosidae: Artoriinae)
Fig. 9. Tetralycosa oraria (L. Koch, 1876), T. arabanae (Framenau, Gotch & Austin, 1976) and T. caudex sp. nov., distribution records in Australia.
Mass spectrometry output SILAC labelled (F/Y) biological replicate 1 - anti-HLA-A29 antibody DK1G8
<p>Mass spectrometry output from SILAC labelled (F/Y) ERAP2 wildetype versus (CRISPR Cas9-mediated) ERAP2-KO lymphoblastoid cell line from a Birdshot Uveitis patient (ERAP1 hap10/10 ERAP2 hapA/A). Peptides were eluted from immuno-purifications with anti-HLA-A29 antibody DK1G8. The dataset was used for subsequent filtering and differential expression analysis by <em>limma</em>. </p>
Fig. 6. Ocyale ghost Jocque M in A new species of Ocyale (Araneae, Lycosidae) from Madagascar, with first observations on the biology of a representative in the genus
Fig. 6. Ocyale ghost Jocque M. & Jocqué R. sp. nov., palp, ♂, paratype (MRAC 245361), scanning electron micrographs. A. Right palp, ventral view. B. Distal end of bulbus, ventral view. C. Main part of bulbus ventro-prolateral view. Abbreviations: MA = median apophysis; P = palea; T = tegulum; * = embolus. Scale bars: A = 0.5 mm; B–C = 0.1 mm.
Fig. 4. Ocyale ghost Jocque M in A new species of Ocyale (Araneae, Lycosidae) from Madagascar, with first observations on the biology of a representative in the genus
Fig. 4. Ocyale ghost Jocque M. & Jocqué R. sp. nov. A–B. Holotype, ♂. A. Dorsal habitus. B. Ventral habitus. C–D. Paratype, ♀ (MRAC 245338). C. Dorsal habitus. D. Ventral habitus. Scale bars = 0.5 mm.
Fig. 2 in A new species of Ocyale (Araneae, Lycosidae) from Madagascar, with first observations on the biology of a representative in the genus
Fig. 2. Habitat on type locality of Ocyale ghost Jocque M. & Jocqué R. sp. nov. (photo by MJ, July 2012).
Fig. 7. Ocyale ghost Jocque M in A new species of Ocyale (Araneae, Lycosidae) from Madagascar, with first observations on the biology of a representative in the genus
Fig. 7. Ocyale ghost Jocque M. & Jocqué R. sp. nov. A–B. Palp (holotype, ♂). A. Retrolateral view. B. Ventral view. C. Epigyne (paratype, ♀, MRAC 245338), ventral view. Scale bar = 1mm. Abbreviations: MA = median apophysis; P = palea; T = tegulum; * = embolus.
Fig. 3. Ocyale ghost Jocque M in A new species of Ocyale (Araneae, Lycosidae) from Madagascar, with first observations on the biology of a representative in the genus
Fig. 3. Ocyale ghost Jocque M. & Jocqué R. sp. nov. photographed at type locality. A. Female habitus. B. Same, detail. C. Female in sand retreat. D. Female with spiderlings on abdomen. E. Two males, one being eaten by the other. F. Female with white grasshopper prey. Photos A–B: MJ (2012), C–F: SW (2016).
Scripts from: A framework to diagnose the causes of river ecosystem deterioration using biological symptoms
<ol> <li>River assessments are predominantly based upon biological metrics and indices selected or designed to integrate the impact of multiple causes of deterioration (stressors) operating at various spatial scales. Yet, the integrative nature of many bioassessment systems does not allow for tracing back individual stressors and their influence on the overall assessment result. Thus, river managers often fail to link bioassessment with programmes of management measures, to improve ecological quality.</li> <li>Here, we present a novel diagnostic approach that allows to estimate the probability of individual stressors being causal for biological degradation at the scale of individual riverine ecosystems. Similar to medical diagnosis, we use various <i>symptoms</i> (macroinvertebrate metrics) and probabilistically link them to various potential <i>causes</i> of ecological status degradation (stressors). Symptoms and causes are informed by a training dataset of 157 samples (stressors, taxa lists) from central European lowland rivers and are linked through a Bayesian Network (BN). Three separate BNs addressing three different spatial scales (catchment, reach and site) are presented. </li> <li>Water quality-related causes are most influential at the catchment scale, while hydromorphological causes prevail at finer scales. Causes indicating riparian degradation are most influential at the reach scale. Many symptoms show strong linkages to causes and reveal ecologically meaningful relationships, thus pointing at the potential diagnostic utility of the symptoms selected. BNs are validated using an independent dataset of 47 samples. Overall, model accuracies range 53–58% for the three BNs, while for individual nodes (causes and symptoms) up to 100% concordance of predicted and actual node states in the validation data is achieved. The BNs are implemented as interactive online diagnostic tools to allow end users an easy application. </li> <li> <i>Synthesis and applications.</i> Our results confirm that Bayesian inference can greatly assist the diagnosis of potential causes of river deterioration based upon a selection of diagnostic biological metrics. If integrated into a Bayesian Network, symptoms and potential causes can be linked and inform management decisions on appropriate measures, to improve ecological quality. Diagnostic Bayesian Networks thus support end users bridge the gap between biological monitoring and appropriate programmes of management measures. 28 July 2020</li> </ol>
Data from: A framework to diagnose the causes of river ecosystem deterioration using biological symptoms
<ol> <li>River assessments are predominantly based upon biological metrics and indices selected or designed to integrate the impact of multiple causes of deterioration (stressors) operating at various spatial scales. Yet, the integrative nature of many bioassessment systems does not allow for tracing back individual stressors and their influence on the overall assessment result. Thus, river managers often fail to link bioassessment with programmes of management measures, to improve ecological quality.</li> <li>Here, we present a novel diagnostic approach that allows to estimate the probability of individual stressors being causal for biological degradation at the scale of individual riverine ecosystems. Similar to medical diagnosis, we use various <i>symptoms</i> (macroinvertebrate metrics) and probabilistically link them to various potential <i>causes</i> of ecological status degradation (stressors). Symptoms and causes are informed by a training dataset of 157 samples (stressors, taxa lists) from central European lowland rivers and are linked through a Bayesian Network (BN). Three separate BNs addressing three different spatial scales (catchment, reach and site) are presented. </li> <li>Water quality-related causes are most influential at the catchment scale, while hydromorphological causes prevail at finer scales. Causes indicating riparian degradation are most influential at the reach scale. Many symptoms show strong linkages to causes and reveal ecologically meaningful relationships, thus pointing at the potential diagnostic utility of the symptoms selected. BNs are validated using an independent dataset of 47 samples. Overall, model accuracies range 53–58% for the three BNs, while for individual nodes (causes and symptoms) up to 100% concordance of predicted and actual node states in the validation data is achieved. The BNs are implemented as interactive online diagnostic tools to allow end users an easy application. </li> <li> <i>Synthesis and applications.</i> Our results confirm that Bayesian inference can greatly assist the diagnosis of potential causes of river deterioration based upon a selection of diagnostic biological metrics. If integrated into a Bayesian Network, symptoms and potential causes can be linked and inform management decisions on appropriate measures, to improve ecological quality. Diagnostic Bayesian Networks thus support end users bridge the gap between biological monitoring and appropriate programmes of management measures. 28-Jul-2020</li> </ol>
Figure 31 in Rocky-intertidal cheilostome bryozoans from the vicinity of the Sesoko Biological Station, west-central Okinawa, Japan
Figure 31. Bryopesanser latesco Tilbrook: (a) NSMT-Te 1167, autozooids; (b) NSMT-Te 1100, orifice; (c) NSMT-Te 1054, ovicelled and non-ovicelled autozooids; (d) NSMT-Te 1103, ovicelled and nonovicelled autozooids; note sharp projection on proximal peristomial rim in lower-right zooid, and size difference in avicularia between lower right and lower left zooids. Panels are scanning electron microscopic images of dried, unbleached specimen (a) or bleached specimens (b–d). Scale bars: a, c, d = 300 µm; b = 100 µm.
Figure 37 in Rocky-intertidal cheilostome bryozoans from the vicinity of the Sesoko Biological Station, west-central Okinawa, Japan
Figure 37. Rhynchozoon lunifrons sp. nov.: (a) NSMT-Te 1196 (paratype), colony view; (b) enlargement from preceding panel, showing autozooids typically having three processes associated with orifice; (c–f) NSMT-Te 1197 (paratype), (c) marginal autozooids showing shape and orientation of suboral avicularian chamber, (d) marginal autozooids, showing orifice shape, (e) well-calcified ovicelled autozooids, showing large semicircular area of exposed entooecium on ovicell, (f) endozooidal ovicells, with three zooids showing a second ovicell (arrowheads) lateral to orifice. All panels are scanning electron microscopic images of bleached specimens. Scale bars: a = 1.0 mm; b = 500 µm; c–f = 300 µm.
Figure 29 in Rocky-intertidal cheilostome bryozoans from the vicinity of the Sesoko Biological Station, west-central Okinawa, Japan
Figure 29. Arthropoma harmelini sp. nov., NSMT-Te 1159 (holotype): (a) autozooids, showing crescentic suboral umbo, which in top-centre zooid is continuous with peristomial rim; (b) orifice; (c) ovicelled autozooids, with one ovicell showing a lateral extension; note there is no row of pseudopores between orifice and floor of developing ooecium; (d) ovicelled and non-ovicelled autozooids; (e) autozooids with developing ooecia; note lack of pseudopores between orifice and floor of ooecium; (f) colony margin, showing interzooidal connections. All panels are scanning electron microscopic images of bleached specimen. Scale bars: a, c, e, f = 300 µm; b = 100 µm; d = 500 µm.
Figure 34 in Rocky-intertidal cheilostome bryozoans from the vicinity of the Sesoko Biological Station, west-central Okinawa, Japan
Figure 34. Rhynchozoon ferocula Hayward: (a) NSMT-Te 1178, view of smaller colony on SEM stub; (b) NSMT-Te 1179, marginal autozooids; (c–e) NSMT-Te 1178 (larger colony on SEM stub), (c) orifices in (c) mature autozooids, (d) orifice and oral spines, (e) ovicelled autozooids (arrowhead, window in calcified ectooecium; arrow, labellum); (f) NSMT-Te 1180, ancestrula and two daughter zooids. All panels are scanning electron microscopic images of bleached specimens. Scale bars: a = 1.0 mm; b, e, f = 200 µm; c = 100 µm; d = 50 µm.
Figure 32 in Rocky-intertidal cheilostome bryozoans from the vicinity of the Sesoko Biological Station, west-central Okinawa, Japan
Figure 32. (a, b) Crepidacantha longiseta Canu and Bassler: (a) NSMT-Te 1167, autozooids; (b) NSMT- Te 1169, ovicelled and non-ovicelled autozooids. (c, d) Crepidacantha poissonii (Audouin), NSMT-Te 1100: (c) ovicelled autozooids; (d) ovicelled and non-ovicelled autozooids. Panels are scanning electron microscopic images of dried, unbleached (a, c) or bleached (b, d) specimens. Scale bars: a, c = 300 µm; b, d = 200 µm.
Figure 39 in Rocky-intertidal cheilostome bryozoans from the vicinity of the Sesoko Biological Station, west-central Okinawa, Japan
Figure 39. Rhynchozoon ryukyuense sp. nov., NSMT-Te 1202 (a–c), NHMUK 2016.5.13.81 (d, e), NSMT- Te 1201 (f), all paratype specimens: (a) colony view; (b) marginal autozooids, showing aspect of suboral avicularian chamber; (c) marginal autozooids, showing shape and orientation of avicularian rostrum; (d) marginal autozooids, showing orifice shape; note denticles associated with basal pore chambers; (e) autozooids, with diamond-shaped frontal avicularia and hypertrophied suboral avicularia; (f) ovicelled autozooids. All panels are scanning electron microscopic images of bleached specimens. Scale bars: a = 1.0 mm; b–f = 300 µm.
Figure 27 in Rocky-intertidal cheilostome bryozoans from the vicinity of the Sesoko Biological Station, west-central Okinawa, Japan
Figure 27. (a–c) Robertsonidra argentea (Hincks), NSMT-Te 1149: (a) autozooids near colony margin; (b) autozooids, showing two types of avicularia; (c) ovicelled and non-ovicelled autozooids. (d–f) Robertsonidra porifera (Maplestone), NSMT-Te 1155: (d) autozooids near colony margin, one showing more-common large avicularium; (e) autozooids, one showing uncommon smaller avicularium; (f) ovicelled and non-ovicelled autozooids. All panels are scanning electron microscopic images of bleached specimens. Scale bars = 300 µm.
Figure 1 in Rocky-intertidal cheilostome bryozoans from the vicinity of the Sesoko Biological Station, west-central Okinawa, Japan
Figure 1. Partial map of Japan (lower right) showing the location of Okinawa (upper right), with the study area enlarged (left); black circles indicates sampling sites (SES, old breakwater near Sesoko Station; REEF, reef-fringe site; MIN, breakwater on Minna Island); black square indicates the Sesoko Station, Tropical Biosphere Research Centre, University of the Ryukyus; dark grey shading indicates land; light grey shading indicates areas of coral reef flat.
Figure 20 in Rocky-intertidal cheilostome bryozoans from the vicinity of the Sesoko Biological Station, west-central Okinawa, Japan
Figure 20. (a) Calyptotheca reniformis Tilbrook, 2006, NSMT-Te 1125, ovicelled and several nonovicelled autozooids. (b–d) Calyptotheca sesokoensis sp. nov., NSMT-Te 1128 (holotype): (b) autozooids at colony margin; (c) autozooids, one with rare lateral-oral avicularium; (d) ovicelled and nonovicelled autozooids. All panels are scanning electron microscopic images of bleached specimens. Scale bars = 300 µm.
Figure 6 in Rocky-intertidal cheilostome bryozoans from the vicinity of the Sesoko Biological Station, west-central Okinawa, Japan
Figure 6. (a–d) Corbulella extenuata Dick, Tilbrook, and Mawatari, NSMT-Te 1064: (a) autozooids; (b) oblique view of colony margin showing interzooidal connections and presumed vestigial ooecium at early stage of formation (far top right); (c) ancestrula and periancestrular zooids; (d) same ancestrula (asterisk) as in panel (c) after bleaching, with daughter zooids lost from left side. (e, f) Cranosina coronata (Hincks), NSMT-Te 1065: (e) autozooids (the central three showing regenerative, intramurally budded cystids); (f) autozooids at colony margin (central zooid with intramurally budded cystid). Panels are scanning electron microscopic images of dried (a, c) or bleached (b, d–f) specimens. Scale bars: a = 250 µm; b–d = 300 µm; e, f = 400 µm.
Figure 19 in Rocky-intertidal cheilostome bryozoans from the vicinity of the Sesoko Biological Station, west-central Okinawa, Japan
Figure 19. (a, b) Smittina nitidissima (Hincks), NSMT-Te 1119: (a) autozooids at colony margin; (b) ovicelled autozooids, with 0–2 lateral oral avicularia. (c, d) Smittoidea pacifica Soule and Soule, NSMT-Te 1120: (c) zooids at colony margin; (d) ovicelled and non-ovicelled autozooids. All panels are scanning electron microscopic images of bleached specimens. Scale bars = 300 µm.
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.