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.
3,480
datasets available to search
ShareScore release 0.9.0
Dataset results
3,480 results for “a value”
Dataset: iShares Morningstar Mid-Cap Value ETF (IMCV) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
French wine dataset to mapping the expected harvest value by county
<p>This database is built from open data as described in the paper entitled ‘French wine: Combination of multiple open data sources to mapping the expected harvest value’ (2024).</p> <table> <tbody> <tr> <td> <p>CODE_CULTU</p> </td> <td> <p><strong>Crop code of the graphic land registry database </strong></p> </td> </tr> <tr> <td> <p>CodeCdC</p> </td> <td> <p><strong>Crop code in Multi Perils Crop Insurance specification </strong></p> </td> </tr> <tr> <td> <p>Harvest Value B</p> </td> <td> <p><strong>Harvest value (€/ha organic wine)</strong></p> </td> </tr> <tr> <td> <p>Harvest Value C</p> </td> <td> <p><strong>Harvest value (€/ha no-organic wine)</strong></p> </td> </tr> <tr> <td> <p>IDA</p> </td> <td> <p><strong>ID of geographical areas of INAO</strong></p> </td> </tr> <tr> <td> <p>Insee_Com</p> </td> <td> <p><strong>County code (INSEE)</strong></p> </td> </tr> <tr> <td> <p>Label_CdC</p> </td> <td> <p><strong>Crop label in Multi Perils Crop Insurance specification </strong></p> </td> </tr> <tr> <td> <p>Label_Dpt</p> </td> <td> <p><strong>Department</strong></p> </td> </tr> <tr> <td> <p>Label_Insee_com</p> </td> <td> <p><strong>County</strong></p> </td> </tr> <tr> <td> <p>Label_RA</p> </td> <td> <p><strong>Agricultural Region (AGRESTE)</strong></p> </td> </tr> <tr> <td> <p>Label_appellation</p> </td> <td> <p><strong>Appellation (INAO)</strong></p> </td> </tr> <tr> <td> <p>Label_code3</p> </td> <td> <p><strong>Crop (FADN)</strong></p> </td> </tr> <tr> <td> <p>Label_cvi</p> </td> <td> <p><strong>Wine name (vineyard register of customs services) </strong></p> </td> </tr> <tr> <td> <p>Label_idGeo</p> </td> <td> <p><strong>Geographical ID of Quality Sign (INAO)</strong></p> </td> </tr> <tr> <td> <p>PxBaremAOP</p> </td> <td> <p><strong>Price listed in Multi Perils Crop Insurance specification (€/hl no-organic) </strong></p> </td> </tr> <tr> <td> <p>PxBaremAOPBio</p> </td> <td> <p><strong>Price listed in Multi Perils Crop Insurance specification (€/hl organic) </strong></p> </td> </tr> <tr> <td> <p>RdtMOAOP</p> </td> <td> <p><strong>Harvest wine yield (hl/ha)</strong></p> </td> </tr> <tr> <td> <p>SurfaceModel</p> </td> <td> <p><strong>Surface of wine as fitted by model</strong></p> </td> </tr> <tr> <td> <p>code3</p> </td> <td> <p><strong>Crop code (FADN)</strong></p> </td> </tr> <tr> <td> <p>code_dept</p> </td> <td> <p><strong>Department code</strong></p> </td> </tr> <tr> <td> <p>code_regag</p> </td> <td> <p><strong>Code of Agricultural Region (AGRESTE)</strong></p> </td> </tr> <tr> <td> <p>cvi</p> </td> <td> <p><strong>Wine code (vineyard register of customs services)</strong></p> </td> </tr> <tr> <td> <p>id_appellation</p> </td> <td> <p><strong>Appellation code (INAO)</strong></p> </td> </tr> <tr> <td> <p>id_denomination_geo</p> </td> <td> <p><strong>Geographical ID of Quality Sign (INAO)</strong></p> </td> </tr> </tbody> </table> <p> </p> <p>Find here the relative research paper :</p> <p><a href="https://univ-lemans.hal.science/hal-04627672">https://univ-lemans.hal.science/hal-04627672</a></p> <p>Please find below the list of the sites where used data could be found (lasted view the June 26, 2024).</p> <p><a href="https://agreste.agriculture.gouv.fr/agreste-web/methodon/Z.1/!searchurl/listeTypeMethodon/">https://agreste.agriculture.gouv.fr/agreste-web/methodon/Z.1/!searchurl/listeTypeMethodon/</a></p> <p><a href="https://www.casd.eu/source/reseau-dinformation-comptable-agricole/?tab=16">https://www.casd.eu/source/reseau-dinformation-comptable-agricole/?tab=16</a></p> <p><a href="https://www.douane.gouv.fr/la-douane/opendata?f%5B0%5D=categorie_opendata_facet%3A467">https://www.douane.gouv.fr/la-douane/opendata?f%5B0%5D=categorie_opendata_facet%3A467</a></p> <p><a href="http://www.inao.gouv.fr">www.inao.gouv.fr</a></p> <p><a href="https://www.data.gouv.fr/fr/datasets/?q=inao">https://www.data.gouv.fr/fr/datasets/?q=inao</a></p> <p><a href="https://maisons-champagne.com/fr/appellation/aire-geographique/">https://maisons-champagne.com/fr/appellation/aire-geographique/</a></p> <p><a href="https://info.agriculture.gouv.fr/boagri/document_administratif-4b9ef75e-29a7-449d-9e40-7e5253bfd642/telechargement">https://info.agriculture.gouv.fr/boagri/document_administratif-4b9ef75e-29a7-449d-9e40-7e5253bfd642/telechargement</a></p> <p><a href="https://agreste.agriculture.gouv.fr/agreste-web/download/publication/publie/Dos2203/2Pages%20de%20Dossier2022-3_CCAN_ChapitreII.pdf">https://agreste.agriculture.gouv.fr/agreste-web/download/publication/publie/Dos2203/2Pages%20de%20Dossier2022-3_CCAN_ChapitreII.pdf</a></p>
Fig. 2 in A New Measure Of Conservation Value Combining Rarity And Ecological Diversity: A Case Study With Light Trap Collected Caddisflies (Insecta: Trichoptera)
Fig. 2. The Rarity and Ecological Diversity (RED)-index of the different aquatic habitats (aquatic habitats with the same letter are not significantly different at p = 0.05 by non-parametric Tukey-test)
Fig. 1 in A New Measure Of Conservation Value Combining Rarity And Ecological Diversity: A Case Study With Light Trap Collected Caddisflies (Insecta: Trichoptera)
Fig. 1. The map of Hungary with the position of the sampling sites (filled squares show light traps)
Fig. 3 in A New Measure Of Conservation Value Combining Rarity And Ecological Diversity: A Case Study With Light Trap Collected Caddisflies (Insecta: Trichoptera)
Fig. 3. The diversity (A) and RAR-index (B) of the different aquatic habitats (aquatic habitats with the same letter are not significantly different at p = 0.05 by non-parametric Tukey-test)
Figures represent five groups respectively. In group, when the dispersal rate is equal to 1% values of the probability of disperser survival are 1, 0.9, 0.8, 0.7, 0.6, 0.5 respectively as in groups and in The Influence Of Dispersal On The Metapopulation Viability Of Giant Panda (Aliuropoda Melanoleuca) In The Minshan Mountains
Figures represent five groups respectively. In group, when the dispersal rate is equal to 1% values of the probability of disperser survival are 1, 0.9, 0.8, 0.7, 0.6, 0.5 respectively as in groups and
Wind Value: Second Conference 2024 Situation in Italy Carla De Laurentis Video
<p>Video 30 minutes 41 seconds, of the presentation by Carla De Laurentis on the topic " Factors shaping end-of-life decisions of ageing wind infrastructure in Italy". The presentation considers the issues behind repowering, life extension and decommissioning decisions in Italy. This took place at the second Wind Value conference on 29th May 2024 in the Ellen Hutchins Building, of the Environmental Research Institute of University College Cork, Ireland.</p>
Wind Value: Second Conference 2024 Real Option Analysis Peter Deeney Video
<p>Video 13mins 31 seconds, of the presentation by Peter Deeney on the topic " Planning is Optional". The presentation considers the preparatory work before a wind farm, to be a European call option. It also looks at the decisions at end-of-life to decommission, extend life or repower the wind farm. This took place at the second Wind Value conference on 29th May 2024 in the Ellen Hutchins Building, of the Environmental Research Institute of University College Cork, Ireland.</p>
Figure 2 in The value of regular monitoring and diverse sampling techniques to assess aquatic non-native species: a case study from Orkney
Figure 2. The total number of non-native and cryptogenic species recorded at each monitoring location (2012–2017).
Fig. 2 in Dendrological And Recreational Values Of Arlaviskės Juniper Formation
Fig. 2. Reasons for visiting Arlaviškes Junipers Fig. 3. Types of management proposed by visiformationt. tors.
Рис. 1. Вероятность обнаружения меченых животных (среΑнее ± ошибка) при пяти- и Αесятиметровых интерваΛах межΑу прикормочными станциями в Αвух экспериментах. По второму эксперименту расчеты сΑеΛаны ΑΛя резуΛьтатов отΛова в течение первых трех и поΛных Αесяти Αней. Значение «p» отражает уровень статистической значимости разΛичий межΑу ΑоΛями животных с меткой при Αвух интерваΛах Fig. 1. Probability of finding marked animals (average±standard error) between feeding stations placed at intervals of five and ten meters in the two experiments. In the second experiment, calculations were made for the results of trapping during the first three days and during the whole period of ten days. The p value reflects the statistical significance of differences between the fractions of animals with a mark for two types of intervals in Verification of the bottle-based method for estimating abundance of small mammals using biomarkers
Рис. 1. Вероятность обнаружения меченых животных (среΑнее ± ошибка) при пяти- и Αесятиметровых интерваΛах межΑу прикормочными станциями в Αвух экспериментах. По второму эксперименту расчеты сΑеΛаны ΑΛя резуΛьтатов отΛова в течение первых трех и поΛных Αесяти Αней. Значение «p» отражает уровень статистической значимости разΛичий межΑу ΑоΛями животных с меткой при Αвух интерваΛах Fig. 1. Probability of finding marked animals (average±standard error) between feeding stations placed at intervals of five and ten meters in the two experiments. In the second experiment, calculations were made for the results of trapping during the first three days and during the whole period of ten days. The p value reflects the statistical significance of differences between the fractions of animals with a mark for two types of intervals
Fig. 1 in Hematological and morphometric blood value of four cultured species of economically important tropical foodfish
Fig. 1. Volume (mm3) of red blood cell and hemoglobin (g.dL-1) of jundiá (Rhamdia quelen), Nile tilapia (Oreochromis niloticus), piracanjuba (Brycon orbignyanus) and pacu (Piaractus mesopotamicus) maintained under controlled conditions for a period of 30 days. Values are means of 50 random cells measured in 11-15 blood smears.
Figure 1 in Intraspecific C-value variation and the outcomes in Psidium cattleyanum Sabine essential oil
Figure 1. Relative area (%) of terpenic classes in relation to accessions of Psidium cattleyanum (CAT1, CAT2, CAT3, CAT4, CAT5, CAT6 and CAT8). Hydrogenated monoterpenes (HM), oxygenated monoterpenes (OM), hydrogenated sesquiterpenes (HS) and oxygenated sesquiterpenes (OS).
Fig. 5. Maximum Likelihood tree for genus Thyridium with RPB2 dataset. Node numbers indicate bootstrap value above 70 in First report of seven unrecorded bambusicolous fungi in Korea
Fig. 5. Maximum Likelihood tree for genus Thyridium with RPB2 dataset. Node numbers indicate bootstrap value above 70%. Blue colored names indicate the strains isolated in this study. Type strains are indicated by "T".
Fig. 3. Maximum Likelihood tree for genus Macroconia with ITS dataset. Node numbers indicate bootstrap value above 70 in First report of seven unrecorded bambusicolous fungi in Korea
Fig. 3. Maximum Likelihood tree for genus Macroconia with ITS dataset. Node numbers indicate bootstrap value above 70%. Blue colored names indicate the strains isolated in this study. Type strains are indicated by "T".
◂Fig. 6 A molecular phylogeny of 56 systematically representative Peridiniaceae, including 42 accessions assignable to P. cinctum from various geographic regions. Maximum likelihood tree (– ln = 21,884.93), as inferred from a rRNA nucleotide alignment (1137 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (CZE Czech Republic, E East, GER Germany, HET Heterocapsaceae, N North, PPE Protoperidiniaceae, POL Poland, rbn ribotype n, S South, SWE Sweden, UKR Ukraine, W West) in Bumps on the back: An unusual morphology in phylogenetically distinct Peridinium aff. cinctum (= Peridinium tuberosum; Peridiniales, Dinophyceae)
◂Fig. 6 A molecular phylogeny of 56 systematically representative Peridiniaceae, including 42 accessions assignable to P. cinctum from various geographic regions. Maximum likelihood tree (– ln = 21,884.93), as inferred from a rRNA nucleotide alignment (1137 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (CZE Czech Republic, E East, GER Germany, HET Heterocapsaceae, N North, PPE Protoperidiniaceae, POL Poland, rbn ribotype n, S South, SWE Sweden, UKR Ukraine, W West)
◂Fig. 4 A molecular tree of 51 systematically representative Peridiniaceae, including all 28 accessions assignable to P. volzii. Maximum Likelihood tree (–ln = 22,017.62), as inferred from a rRNA nucleotide alignment (1,129 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (abbreviations: HET, Heterocapsaceae; PPE, Protoperidiniaceae) in Morphological and molecular variability of Peridinium volzii Lemmerm. (Peridiniaceae, Dinophyceae) and its relevance for infraspecific taxonomy
◂Fig. 4 A molecular tree of 51 systematically representative Peridiniaceae, including all 28 accessions assignable to P. volzii. Maximum Likelihood tree (–ln = 22,017.62), as inferred from a rRNA nucleotide alignment (1,129 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (abbreviations: HET, Heterocapsaceae; PPE, Protoperidiniaceae)
Рис. 5. ΔенΑрограмма меры разброса значений ΑΛя показатеΛей вΛияния опушенности и тоΛщины Λистовой пΛастинки картофеΛя на прожорΛивость Λичинок картофеΛьной коровки Fig. 5. Dendrogram representing the value scatter for the influence of pubescence and thickness of the potato leaf blade on the voracity of potato ladybug larvae in Role of potato immune factors in the trophic responses of Henosepilachna vigintioctomaculata Motschulsky, 1858
Рис. 5. ΔенΑрограмма меры разброса значений ΑΛя показатеΛей вΛияния опушенности и тоΛщины Λистовой пΛастинки картофеΛя на прожорΛивость Λичинок картофеΛьной коровки Fig. 5. Dendrogram representing the value scatter for the influence of pubescence and thickness of the potato leaf blade on the voracity of potato ladybug larvae
Рис. 3. Снижение чисΛенности гусениц при возΑействии разΛичных географических штаммов ВЯП. СтоΛбцы: коΛичество погибших во время эксперимента гусениц поΑ опреΑеΛенной инфекционной нагрузкой. ПоказатеΛи (в процентах) привеΑены в Λевой части табΛицы. СтреΛки: минимаΛьное и максимаΛьное коΛичество погибших во время очереΑного учета гусениц и крестообразные маркеры: среΑнее коΛичество погибших во время очереΑного учета гусениц. ПоказатеΛи (в абсоΛютных значениях) в правой части табΛицы. По горизонтаΛи: номер титра, от боΛьшего к меньшему Fig. 3. Decrease in the number of caterpillars under the influence of various geographical strains of NPV. Columns: number of caterpillars killed during the experiment under a certain infectious load. Indicators (in percent) are shown on the left side of the table. Arrows: the minimum and maximum number of deaths during the next track count and cross markers: the average number of deaths during the next track count. Indicators (in absolute values) on the right side of the table. Horizontal: titre, from highest to lowest in Sensitivity of caterpillars of the gypsy moth (Lymantria dispar, Erebidae) from the South of Khabarovsk Territory to various strains of nuclear polyhedrosis virus
Рис. 3. Снижение чисΛенности гусениц при возΑействии разΛичных географических штаммов ВЯП. СтоΛбцы: коΛичество погибших во время эксперимента гусениц поΑ опреΑеΛенной инфекционной нагрузкой. ПоказатеΛи (в процентах) привеΑены в Λевой части табΛицы. СтреΛки: минимаΛьное и максимаΛьное коΛичество погибших во время очереΑного учета гусениц и крестообразные маркеры: среΑнее коΛичество погибших во время очереΑного учета гусениц. ПоказатеΛи (в абсоΛютных значениях) в правой части табΛицы. По горизонтаΛи: номер титра, от боΛьшего к меньшему Fig. 3. Decrease in the number of caterpillars under the influence of various geographical strains of NPV. Columns: number of caterpillars killed during the experiment under a certain infectious load. Indicators (in percent) are shown on the left side of the table. Arrows: the minimum and maximum number of deaths during the next track count and cross markers: the average number of deaths during the next track count. Indicators (in absolute values) on the right side of the table. Horizontal: titre, from highest to lowest
Fig. 1. Circular Bayesian tree inferred from mtDNA cox-2 in Temporal stability of parasite distribution and genetic variability values of Contracaecum osculatum sp. D and C. osculatum sp. E (Nematoda: Anisakidae) from fish of the Ross Sea (Antarctica)
Fig. 1. Circular Bayesian tree inferred from mtDNA cox-2 sequences obtained from specimens of C. osculatum sp. D and C. osculatum sp. E analysed in the present study, based on Bayesian Inference (BI) method using MrBayes v3.2.2 (Ronquist et al., 2012). Evolutionary distance was estimated using the TrN + G (G = 0.60) substitution model as implemented in jModeltest (Posada, 2008), with the AIC approach (Posada and Buckley, 2004). Posterior probability values are the result of 1.000000 of runs and are reported at the nodes. The coloured icons correspond to the two species considered in this study (red = C. osculatum sp. D and blue = C. osculatum sp. E).
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.