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.
829
datasets available to search
ShareScore release 0.9.0
Dataset results
829 results for “functional responses”
Transcriptomic and functional responses of the cystic fibrosis airway epithelium to CFTR modulator therapy
GEO Series GSE306517. Homo sapiens. 76 samples. Type: Expression profiling by high throughput sequencing.
Reprogramming Transcriptional Responses through Functionally-Distinct Classes of Enhancers in Prostate Cancer Cells [ChIP-Seq, Gro-Seq]
GEO Series GSE27823. Homo sapiens. 30 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Nuclear access of DNlg3 c-terminal fragment and its function in regulating innate immune response genes
GEO Series GSE219064. Drosophila melanogaster. 6 samples. Type: Expression profiling by high throughput sequencing.
Transcriptomic and functional deficits in human TREM2-/- microglia impair response to Alzheimer's pathology in vivo
GEO Series GSE158470. Homo sapiens. 46 samples. Type: Expression profiling by high throughput sequencing.
Functional genomic analysis of the 68-1 RhCMV-Mycobacteria tuberculosis vaccine reveals an IL-15 response signature that is conserved with vector attenuation
GEO Series GSE273911. Macaca mulatta. 320 samples. Type: Expression profiling by high throughput sequencing.
Attenuated IL-2 muteins promote regulatory T cell adaptation of the effector phenotype and function in vivo via modified IL-2 responses
GEO Series GSE216130. Mus musculus. 48 samples. Type: Expression profiling by high throughput sequencing.
THE INTEGRATIVE GENOMIC AND FUNCTIONAL IMMUNOLOGICAL ANALYSES OF COLORECTAL CANCER INITIATING CELLS TO MODULATE STEMNESS PROPERTIES AND THE SUSCEPTIBILIY TO IMMUNE RESPONSES [miRNA]
GEO Series GSE287585. Homo sapiens. 23 samples. Type: Non-coding RNA profiling by array.
Tracing functional (epi)genomic imprints and their evolutionary origins in human defense antiviral cellular response
GEO Series GSE229445. Homo sapiens; Mus musculus. 156 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Adult nucleus accumbens single-nucleus RNA-seq dataset for "A dopamine-induced coordinated gene expression program regulates neuronal function and cocaine response"
GEO Series GSE137763. Rattus norvegicus. 4 samples. Type: Expression profiling by high throughput sequencing.
Impaired CHD6 function links misregulation of autophagy and DNA damage response to premature ageing [RNA-Seq]
GEO Series GSE135832. Homo sapiens. 32 samples. Type: Expression profiling by high throughput sequencing.
A luteal phase deficiency model in normal women: structural and functional responses to varying concentrations of progesterone
GEO Series GSE56980. Homo sapiens. 28 samples. Type: Expression profiling by array.
Critical Role of STAT5 Transcription Factor Tetramerization for Cytokine Responses and Normal Immune Function
GEO Series GSE36890. Mus musculus. 68 samples. Type: Expression profiling by array; Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Soil variation response is mediated by growth trajectories rather than functional traits in a widespread pioneer Neotropical tree
<p>Description of Soil_DataTrees.csv</p> <ul> <li>Tree_label: Label of trees on the field, there are 70 trees</li> <li>Tree_site: Site on which the tree has been sampled; COU: Counami; SPA: Sparouine</li> <li>Descr_date: Date of tree sampling</li> <li>Soil_type: Type of soil; FS: ferralitic soils; WS: white-sand soils</li> <li>Soil_sample: Label of soil sample</li> <li>H2Osoil: Soil water content (g kg<sup>-1</sup>)</li> <li>Clay: Soil clay content (g kg<sup>-1</sup>)</li> <li>SiltTh: Soil thin silt content (g kg<sup>-1</sup>)</li> <li>SiltCo: Soil coarse silt content (g kg<sup>-1</sup>)</li> <li>SandTh: Soil thin sand content (g kg<sup>-1</sup>)</li> <li>SandCo: Soil coarse sand content (g kg<sup>-1</sup>)</li> <li>Csoil: Soil carbon content (g kg<sup>-1</sup>)</li> <li>Nsoil: Soil nitrogen content (g kg<sup>-1</sup>)</li> <li>CNsoil: Soil carbon:nitrogen ratio</li> <li>MOsoil: Soil organic matter content (g kg<sup>-1</sup>)</li> <li>Ptotsoil: Soil total phosphorus content (g 100g<sup>-1</sup>)</li> <li>Kcec: Soil potassium:CEC[cation-exchange capacity] ratio</li> <li>Cacec: Soil calcium:CEC ratio</li> <li>Mgcec: Soil magnesium:CEC ratio</li> <li>Nacec: Soil sodium:CEC ratio</li> <li>Alcec: Soil aluminum:CEC ratio</li> <li>Fecec: Soil iron:CEC ratio</li> <li>Mncec: Soil manganese:CEC ratio</li> <li>Hcec: Soil hydrogen:CEC ratio</li> <li>pHsoil: Soil pH (cmol kg<sup>-1</sup>)</li> <li>CECsoil: Soil cation-exchange capacity (cmol kg<sup>-1</sup>)</li> <li>Indexsoil: Soil index of fertility = (K+Ca+Mg+Na)/CEC</li> </ul> <p>K, Ca, Mg, Na, Al, Fe, Mn, H were initially measured in cmol kg<sup>-1</sup></p> <p> </p> <p>Description of Trait_DataTrees.csv</p> <ul> <li>Tree_label: Label of the tree on the field. There are 70 trees</li> <li>Tree_site: Site of sampling; COU: Counami; SPA: Sparouine</li> <li>Descr_date: Date of tree sampling</li> <li>Calendar_day: Day of the year (between 1 and 365) of tree sampling</li> <li>Soil_type: Type of the soil; FS: ferralitic soils; WS: white-sand soils</li> <li>PCA1_soil: Coordinates of the trees along the first axis of PCA (principal component analysis) with soil data, used as a quantitative soil index on FS-WS soil gradient</li> <li>mesHeight: Measured tree height (m)</li> <li>Height: Tree height based on the sum of all internodes length (m)</li> <li>Dbh: Tree diameter at height breast (cm)</li> <li>Age: Tree age (year)</li> <li>Order: Number of branching order</li> <li>Brtot: Total number of branches branching from the trunk</li> <li>Leaftot: Total number of leaves</li> <li>Fltot: Total number of inflorescences</li> <li>Acrown: Total estimated crown area (m²)</li> <li>INA1: Number of trunk internodes</li> <li>Brbear: Number of A2 bearing branches</li> <li>Brdead: Number of A2 dead branches</li> <li>Br1stH: First branching height</li> <li>Fl1stH: First flowering height</li> <li>Br1stIN: First branching node rank</li> <li>Fl1stIN: First flowering node rank</li> <li>Br1stAge: First branching age</li> <li>Fl1stAge: First flowering age</li> <li>LL: Leaf lifespan (day)</li> <li>Lpet: Petiole length (cm)</li> <li>Apet: Petiole cross-sectional area (mm²)</li> <li>Nlobe: Number of leaf lobes</li> <li>LMA: Leaf mass area (g m<sup>-2</sup>)</li> <li>Thleaf: Leaf thickness (µm)</li> <li>Aleaf: Estimated individual leaf area (cm²)</li> <li>Chlleaf: Leaf chlorophyll content (mg ml<sup>-1</sup>)</li> <li>H20resleaf: Leaf residual water content (%)</li> <li>dC13leaf: δ<sup>13</sup>C content (‰)</li> <li>Cleaf: Leaf carbon content (g kg<sup>-1</sup>)</li> <li>Nleaf: Leaf nitrogen content (g kg<sup>-1</sup>)</li> <li>CNleaf: Leaf carbon:nitrogen ratio</li> <li>Pleaf: Leaf phosphorus content (g kg<sup>-1</sup>)</li> <li>Kleaf: Leaf potassium content (g kg<sup>-1</sup>)</li> <li>WSG: Wood specific gravity (g cm<sup>-3</sup>)</li> </ul> <p> </p> <p> </p> <ul> <li>Tree_label: Label of the tree</li> <li>Soil_type: Type of the soil; FS: ferralitic soils; WS: white-sand soils</li> <li>rank_base: Rank of the internode from the base of the tree</li> <li>rank_top: Rank of the internode from the apex of the tree</li> <li>phyllochron: Phyllochron, number of days for the production of one leaf</li> <li>date: Estimated date of tree germination</li> <li>nb_day_base: Number of days since estimated germination</li> <li>nb_day_top: Age of the internode in days at tree sampling</li> <li>AS_rank_base: Rank of the annual shoot from the base of the tree</li> <li>As_rank_top: Rank of the annual shoot from the apex of the tree</li> <li>AS_nodes_base: Number of internodes per annual shoot</li> <li>AS_length_base: Length of the annual shoot (cm)</li> <li>AS_br_base: Number of A2 branches on the annual shoot</li> <li>AS_flo_base: Number of inflorescences on the annual shoot</li> <li>lg_en: Internode length (cm)</li> <li>ht_en: Cumulated height of the tree based on the sum of internode length (cm)</li> <li>ma_lgen: Moving average of internode length</li> <li>resi_lgen: Residuals of internode length</li> </ul> <p> </p>
Data from: Assessment of grain quality in terms of functional group response to elevated [CO2], water and nitrogen using a meta-analysis: Grain protein, zinc and iron under future climate
The increasing [CO2] in the atmosphere increases crop productivity. However, grain quality of cereals and pulses are substantially decreased and consequently compromise human health. Meta-analysis techniques were employed to investigate the effect of elevated [CO2] (e[CO2]) on protein, zinc (Zn) and iron (Fe) concentrations of major food crops (542 experimental observations from 135 studies) including wheat, rice, soybean, field peas and corn considering different levels of water and nitrogen (N). Each crop, except soybean, had decreased protein, Zn and Fe concentrations when grown at e[CO2] concentration ( ≥550 μmol mol-1) compared ambient [CO2] (a[CO2]) concentration (≤ 380 μmol mol-1). Grain protein, Zn and Fe concentrations were reduced under e[CO2], however, the responses of protein, Zn and Fe concentrations to e[CO2 ] were modified by water stress and N. There was an increase in Fe concentration in soybean under medium N and wet conditions but non-significant. The reductions in protein concentrations for wheat and rice were ~ 5-10%, and the reductions in Zn and Fe concentrations were ~ 3-12%. For soybean, there was a small and non-significant increase of 0.37% in its protein concentration under medium N and dry water, while Zn and Fe concentrations were reduced by ~ 2-5%. The protein concentration of field peas decreased by 1.7%, and the reductions in Zn and Fe concentrations were ~ 4-10%. The reductions in protein, Zn and Fe concentrations of corn were ~ 5-10%. Bias in the dataset was assessed using a regression test and rank correlation. The analysis indicated that there are medium levels of bias within published meta-analysis studies of crops responses to Free Air [CO2]Enrichment (FACE). However, the integration of the influence of reporting bias did not affect the significance or the direction of the [CO2] effects.
Public Baseline and Shared Response Structures Support the Theory of Antibody Repertoire Functional Commonality
<p>These four datasets accompany the original preprint "Evidence of Antibody Repertoire Functional Convergence through Public Baseline and Shared Response Structures", now revised to "Public Baseline and Shared Response Structures Support the Theory of Antibody Repertoire Functional Commonality".</p> <p>URL to preprint before revisions: https://www.biorxiv.org/content/10.1101/2020.03.17.993444v2.<br> <br> Included are both Antibody Model Libraries and two further files generated during peer-review.</p>
Functional response and egg distribution among hosts by <i>Pseudapanteles dignus</i> (Hymenoptera: Braconidae), a larval endoparasitoid of <i>Tuta absoluta</i> (Lepidoptera: Gelechiidae)
<p>This dataset contains the raw data that support the results obtained in the research</p>
Data from: Exotic species enhance response diversity to land-use change but modify functional composition
Two main mechanisms may buffer ecosystem functions despite biodiversity loss. First, multiple species could share similar ecological roles, thus providing functional redundancy. Second, species may respond differently to environmental change (response diversity). However, ecosystem function would be best protected when functionally redundant species also show response diversity. This linkage has not been studied directly, so we investigated whether native and exotic pollinator species with similar traits (functional redundancy) differed in abundance (response diversity) across an agricultural intensification gradient. Exotic pollinator species contributed most positive responses, which partially stabilized overall abundance of the pollinator community. However, although some functionally redundant species exhibited response diversity, this was not consistent across functional groups and aggregate abundances within each functional group were rarely stabilized. This shows functional redundancy and response diversity do not always operate in concert. Hence, despite exotic species becoming increasingly dominant in human-modified systems, they cannot replace the functional composition of native species.
Primary SARS-CoV-2 variant of concern infections elicit broad antibody Fc-mediated effector functions and memory B cell responses
<p><span>Neutralization of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) by human sera is a strong correlate of protection against symptomatic and severe Coronavirus Disease 2019 (COVID-19). The emergence of antigenically distinct SARS-CoV-2 variants of concern (VOCs) and the relatively rapid waning of serum antibody titers, however, raises questions about the sustainability of serum protection. In addition to serum neutralization, other antibody functionalities and the memory B cell (MBC) response are suggested to help maintaining this protection. In this study, we investigate the breadth of spike (S) protein-specific serum antibodies that mediate effector functions by interacting with Fc-gamma receptor IIa (<span>FcγRIIa) and FcγRIIIa,</span> and of the receptor binding domain (RBD)-specific MBCs, following a primary SARS-CoV-2 infection with the D614G, Alpha, Beta, Gamma, Delta, Omicron BA.1 or BA.2 variant. Irrespectively of the variant causing the infection, the breadth of S protein-specific serum antibodies that interact with <span>FcγRIIa and FcγRIIIa and the RBD-specific MBC responses </span>exceeded the breadth of serum neutralization, although the Alpha-induced B cell response seemed more strain-specific<span>. Between VOC groups, both quantitative and qualitative differences in the immune responses were observed, suggesting differences in immunogenicity. Overall</span>, this study contributes to the understanding of protective humoral and B cell responses in the light of emerging antigenically distinct VOCs, and highlights the need to study the immune system beyond serum neutralization <span>to gain a better understanding of the protection against emerging variants. </span></span></p>
Soil variation response is mediated by growth trajectories rather than functional traits in a widespread pioneer Neotropical tree
<p>Description of Soil_DataTrees.csv</p> <ul> <li>Tree_label: Label of trees on the field, there are 70 trees</li> <li>Tree_site: Site on which the tree has been sampled; COU: Counami; SPA: Sparouine</li> <li>Descr_date: Date of tree sampling</li> <li>Soil_type: Type of soil; FS: ferralitic soils; WS: white-sand soils</li> <li>Soil_sample: Label of soil sample</li> <li>H2Osoil: Soil water content (g kg<sup>-1</sup>)</li> <li>Clay: Soil clay content (g kg<sup>-1</sup>)</li> <li>SiltTh: Soil thin silt content (g kg<sup>-1</sup>)</li> <li>SiltCo: Soil coarse silt content (g kg<sup>-1</sup>)</li> <li>SandTh: Soil thin sand content (g kg<sup>-1</sup>)</li> <li>SandCo: Soil coarse sand content (g kg<sup>-1</sup>)</li> <li>Csoil: Soil carbon content (g kg<sup>-1</sup>)</li> <li>Nsoil: Soil nitrogen content (g kg<sup>-1</sup>)</li> <li>CNsoil: Soil carbon:nitrogen ratio</li> <li>MOsoil: Soil organic matter content (g kg<sup>-1</sup>)</li> <li>Ptotsoil: Soil total phosphorus content (g 100g<sup>-1</sup>)</li> <li>Kcec: Soil potassium:CEC[cation-exchange capacity] ratio</li> <li>Cacec: Soil calcium:CEC ratio</li> <li>Mgcec: Soil magnesium:CEC ratio</li> <li>Nacec: Soil sodium:CEC ratio</li> <li>Alcec: Soil aluminum:CEC ratio</li> <li>Fecec: Soil iron:CEC ratio</li> <li>Mncec: Soil manganese:CEC ratio</li> <li>Hcec: Soil hydrogen:CEC ratio</li> <li>pHsoil: Soil pH (cmol kg<sup>-1</sup>)</li> <li>CECsoil: Soil cation-exchange capacity (cmol kg<sup>-1</sup>)</li> <li>Indexsoil: Soil index of fertility = (K+Ca+Mg+Na)/CEC</li> </ul> <p>K, Ca, Mg, Na, Al, Fe, Mn, H were initially measured in cmol kg<sup>-1</sup></p> <p> </p> <p>Description of Trait_DataTrees.csv</p> <ul> <li>Tree_label: Label of the tree on the field. There are 70 trees</li> <li>Tree_site: Site of sampling; COU: Counami; SPA: Sparouine</li> <li>Descr_date: Date of tree sampling</li> <li>Calendar_day: Day of the year (between 1 and 365) of tree sampling</li> <li>Soil_type: Type of the soil; FS: ferralitic soils; WS: white-sand soils</li> <li>PCA1_soil: Coordinates of the trees along the first axis of PCA (principal component analysis) with soil data, used as a quantitative soil index on FS-WS soil gradient</li> <li>mesHeight: Measured tree height (m)</li> <li>Height: Tree height based on the sum of all internodes length (m)</li> <li>Dbh: Tree diameter at height breast (cm)</li> <li>Age: Tree age (year)</li> <li>Order: Number of branching order</li> <li>Brtot: Total number of branches branching from the trunk</li> <li>Leaftot: Total number of leaves</li> <li>Fltot: Total number of inflorescences</li> <li>Acrown: Total estimated crown area (m²)</li> <li>INA1: Number of trunk internodes</li> <li>Brbear: Number of A2 bearing branches</li> <li>Brdead: Number of A2 dead branches</li> <li>Br1stH: First branching height</li> <li>Fl1stH: First flowering height</li> <li>Br1stIN: First branching node rank</li> <li>Fl1stIN: First flowering node rank</li> <li>Br1stAge: First branching age</li> <li>Fl1stAge: First flowering age</li> <li>LL: Leaf lifespan (day)</li> <li>Lpet: Petiole length (cm)</li> <li>Apet: Petiole cross-sectional area (mm²)</li> <li>Nlobe: Number of leaf lobes</li> <li>LMA: Leaf mass area (g m<sup>-2</sup>)</li> <li>Thleaf: Leaf thickness (µm)</li> <li>Aleaf: Estimated individual leaf area (cm²)</li> <li>Chlleaf: Leaf chlorophyll content (mg ml<sup>-1</sup>)</li> <li>H20resleaf: Leaf residual water content (%)</li> <li>dC13leaf: δ<sup>13</sup>C content (‰)</li> <li>Cleaf: Leaf carbon content (g kg<sup>-1</sup>)</li> <li>Nleaf: Leaf nitrogen content (g kg<sup>-1</sup>)</li> <li>CNleaf: Leaf carbon:nitrogen ratio</li> <li>Pleaf: Leaf phosphorus content (g kg<sup>-1</sup>)</li> <li>Kleaf: Leaf potassium content (g kg<sup>-1</sup>)</li> <li>WSG: Wood specific gravity (g cm<sup>-3</sup>)</li> </ul> <p> </p> <p> </p> <ul> <li>Tree_label: Label of the tree</li> <li>Soil_type: Type of the soil; FS: ferralitic soils; WS: white-sand soils</li> <li>rank_base: Rank of the internode from the base of the tree</li> <li>rank_top: Rank of the internode from the apex of the tree</li> <li>phyllochron: Phyllochron, number of days for the production of one leaf</li> <li>date: Estimated date of tree germination</li> <li>nb_day_base: Number of days since estimated germination</li> <li>nb_day_top: Age of the internode in days at tree sampling</li> <li>AS_rank_base: Rank of the annual shoot from the base of the tree</li> <li>As_rank_top: Rank of the annual shoot from the apex of the tree</li> <li>AS_nodes_base: Number of internodes per annual shoot</li> <li>AS_length_base: Length of the annual shoot (cm)</li> <li>AS_br_base: Number of A2 branches on the annual shoot</li> <li>AS_flo_base: Number of inflorescences on the annual shoot</li> <li>lg_en: Internode length (cm)</li> <li>ht_en: Cumulated height of the tree based on the sum of internode length (cm)</li> <li>ma_lgen: Moving average of internode length</li> <li>resi_lgen: Residuals of internode length</li> </ul> <p> </p>
The Effect of Sodium Nitrite on Renal Function and Blood Pressure in Healthy Humans. A Dose-response Study
ClinicalTrials.gov study NCT02078908. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.