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.
4,725
datasets available to search
ShareScore release 0.9.0
Dataset results
4,725 results for “Normalization”
BRAIN Journal-Suicide: Neurochemical Approaches-Figure 2. Hippocampal BDNF and NGF levels of suicide subjects and normal controls
<p>Among the suicidal victims BDNF and NGF levels were significantly reduced in the<br> hippocampus compared to normal control subjects (tBDNF =5.43; df=18; p<0.001; tNGF =6.13; df=18;<br> p<0.001 Figure 2). Such observations clearly indicate the relation of chronic mental depression and<br> hippocampal neurotrophin levels.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 18. Accuracy of classification using the three methods, KNN, SVM and our method, for normal subjects
<p>We present three figures representing the accuracy of the classification using the three methods, KNN, SVM and our method for normal, MCI and Alzheimer subjects. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 2. Three Corpus Calosum: Normal, MCI, AD
<p>The three figures above present the Corpus Callosum relating to three topics: Normal Topic by MCI (Mild Cognitive Impairment), Alzheimer’s topic. Secondly, we will present our clustering method to classify the test subject between 3 classes: N (Normal), MCI (Mild Cognitive Impairment), and AD (Alzheimer's disease). </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 1. Three hippocampus: Normal, MCI, AD
<p>In this context is our work: performing a diagnostic computer-aided system for detecting Alzheimer's disease. Like any diagnostic system, our system contains three parts: preprocessing, segmentation and classification. Initially, we will present a new segmentation method to segment the Hippocampus and Corpus Callosum regardless of the patient's condition. </p>
hg19KIndel: Ethnicity normalized human reference genome
<p>The above zip files (hg19KIndel_Resource.zip) contains the following folders. Specific Details about how each file within the below mentioned folders were derived are present in individual README files for each folder</p> <p>1) hg19Kindel</p> <p> -hg19Kindel.fa - fasta file representing the modified assembly</p> <p>2) Gene Annotations</p> <p> -hg19_refGene.txt - RefSeq gene annotation downloaded from UCSC table browser (for hg19)<br> -hg19Kindel_refGene.txt - RefSeq gene annotation corresponding to hg19Kindel Genome</p> <p>3) SnpEff_Database</p> <p> -snpEffectPredictor.bin - Binary file used by SnpEff to annotate variants</p> <p>4) LiftOver_and_Chain_File</p> <p> -hg19Kindeltohg19.over.chain - UCSC chain file for lifting coordinates from hg19Kindel to hg19<br> -convert_cordinates_vcf.py - Python script to liftover variants(vcf) (only point coordinates) called on hg19Kindel to hg19 coordinate frame</p> <p> </p>
Buoyancy Waves in Earth's Nightside Magnetosphere: Normal-Mode Oscillations of Thin Filaments
<ul> <li>A theory has been developed for small oscillations of a thin filament in the magnetosphere.</li> <li>For the lowest-frequency even modes, the eigenfunctions are essentially buoyancy waves in the plasma sheet, but they are more like slow modes in the inner magnetosphere.</li> <li>For the lowest-frequency even modes, the eigenfrequencies (radians/s) have peak values of approximately 0.07 s<sup>-1</sup> between the inner plasma sheet and plasmapause, for an average magnetospheric field. </li> <li>This includes software and model data used in this paper.</li> <li>Submitted to JGR for review</li> </ul>
Text-fig. 3. "Glossanodon" musceli A – nearly complete specimen NM Pc 02875a; B – caudal skeleton of specimens NM Pc 02871b and NM Pc 02871a (B-1 and B-2 respectively; part and counterpart) and its tentative reconstruction (B-3); C – specimen NM Pc 02873a, general view (C-1) and detail of the head (C-2); D – specimen NM Pc 02874a (the white arrow shows normally developed neural spine on the anterior abdominal vertebra). Abbreviations: ao – antorbitale; d – dentale; epu – epurale; fr – frontale; hp1-6 – hypurals 1-6; io – infraorbitals; mx – maxillare; npu2 – neural spine of second preural vertebra; ph – parhypurale; pu1 – first preural vertebra; pop – preoperculum; psp – parasphenoideum; stu – stegurale; u1 – urale 1; u2 – urale 2. in An Annotated List Of The Oligocene Fish Fauna From The Osíčko Locality (Menilitic Fm.; Moravia, The Czech Republic)
Text-fig. 3. "Glossanodon" musceli A – nearly complete specimen NM Pc 02875a; B – caudal skeleton of specimens NM Pc 02871b and NM Pc 02871a (B-1 and B-2 respectively; part and counterpart) and its tentative reconstruction (B-3); C – specimen NM Pc 02873a, general view (C-1) and detail of the head (C-2); D – specimen NM Pc 02874a (the white arrow shows normally developed neural spine on the anterior abdominal vertebra). Abbreviations: ao – antorbitale; d – dentale; epu – epurale; fr – frontale; hp1-6 – hypurals 1-6; io – infraorbitals; mx – maxillare; npu2 – neural spine of second preural vertebra; ph – parhypurale; pu1 – first preural vertebra; pop – preoperculum; psp – parasphenoideum; stu – stegurale; u1 – urale 1; u2 – urale 2.
Text-fig. 7. Projection of the declinations and inclinations of primary component of the DRM vectors and a mean direction based on Fisher statistics A – samples with normal polarity (down - projection on the lower hemisphere), B – samples with reversed polarity (up - projection on the upper hemisphere). in New Updated Results Of Paleomagnetic Dating Of Cave Deposits Exposed In Za Hájovnou Cave, Javoříčko Karst
Text-fig. 7. Projection of the declinations and inclinations of primary component of the DRM vectors and a mean direction based on Fisher statistics A – samples with normal polarity (down - projection on the lower hemisphere), B – samples with reversed polarity (up - projection on the upper hemisphere).
Text-fig. 3. Cave deposits exposed in Section No. 2 and recorded paleomagnetic polarities. 1 – reworked deposits; 2 – clayey silt, light brown with abundant black dots, structureless; 3 – clayey silt to silty clay, brown, chaotically deposited; 4 – clayey silt, light brown, structureless; 5 – clayey silt, light brown, laminated; 6 – clayey silt to silty clay, brown, structureless; 7 – clayey silt to silty clay, light brown, structureless; 8 – clayey silt, brown with abundant white carbonate clasts; 9 – clayey sandy silt, brown with abundant lighter clayey fragments; 10 – clayey sandy silt, brown with sporadic lighter clayey fragments. Geomagnetic polarity scale: black (N) – normal polarities, white (R) – reversed polarities, grey – intermediate or uninterpretable polarities. For more details see text. in New Updated Results Of Paleomagnetic Dating Of Cave Deposits Exposed In Za Hájovnou Cave, Javoříčko Karst
Text-fig. 3. Cave deposits exposed in Section No. 2 and recorded paleomagnetic polarities. 1 – reworked deposits; 2 – clayey silt, light brown with abundant black dots, structureless; 3 – clayey silt to silty clay, brown, chaotically deposited; 4 – clayey silt, light brown, structureless; 5 – clayey silt, light brown, laminated; 6 – clayey silt to silty clay, brown, structureless; 7 – clayey silt to silty clay, light brown, structureless; 8 – clayey silt, brown with abundant white carbonate clasts; 9 – clayey sandy silt, brown with abundant lighter clayey fragments; 10 – clayey sandy silt, brown with sporadic lighter clayey fragments. Geomagnetic polarity scale: black (N) – normal polarities, white (R) – reversed polarities, grey – intermediate or uninterpretable polarities. For more details see text.
Normal-Pothole-dataset
<p>In this dataset of 5000 images, there are 2500 images of Normal road and 2500 images of potholes, It help <span>to keep the public safe using machine learning and deep learning methods.</span></p>
Thin blood smear images of red blood cells with rouleaux formation morphology and normal morphology
<p>This dataset contains images of thin blood smear with normal red blood cell morphology and rouleaux red blood cell morphology. Ethical approval with approval number: NHREC/17/03//2018 was obtained from Kano state ministry of health. Blood samples from 100 malaria infected patients were collected from Asiya Bayero pediatric hospital, kano state, Nigeria. Thick and thin blood smear slides were prepared using field stain. To ensure there was no bias in slide preparation, slides used for hospital diagnosis prepared under limited and constrained conditions were used as such types of slides represent the true reality of malaria diagnosis in less developed countries.Thin blood smear microscopy was performed by an expert microscopist and each slide was labeled according to the presence of Rouleaux formation or not among others. Out of 100 samples collected, 28 samples had rouleaux formation morphology.</p> <p>A 12MP iPhone 10 camera was attached to a microscope’s eyepiece. Pictures of different field of views for each slide were captured using the iPhone’s camera. For each slide, a minimum of 10 different field of views were captured. 616 images were captured for slides with rouleaux formation. To create a balanced dataset an equal number, 616 images were also captured for slides with normal morphology. To increase the size and variation of the dataset. 312 Digital images of thin blood smear slides with Giemsa staining collected from Murtala Muhammad specialist hospital were added. out of the 312 images, 156 had rouleaux RBC morphology and 156 had normal RBC morphology. Image capture was conducted in the morning, afternoon and evening and in different rooms with different lighting conditions to introduce diverse levels of illumination in the images The captured images from both hospitals had a size of 4032x3024 pixels. The background of the images were cropped to give a size 2500x2500 which were then sliced to give a final size of 750x750 pixels. The final data set consists of 12,356 thin blood smear images with rouleaux formation morphology and 12,356 thin blood smear images with normal red blood cell morphology. Different CNN architectures were trained for the binary classification of the dataset.</p>
Dataset package for the Manuscript "Absence of bulk charge density wave order in the normal state of UTe2"
<p>The attached dataset contains raw data, normalized to the respective attenuater, reported in the manusript: </p> <p>"Absence of bulk charge density wave order in the normal state of UTe2".</p> <p>The files "Figure4a.dat", "Figure4b.dat", and "Figure4c.dat" contain data that were presented in Figure 4a, Figure4b, and Figure4c of the manuscript. The first columns contain the x-axis values, the second columns the intensities, and the third column the errorbars.</p> <p>The files "Figure3_N.dat" present the data in Figure 3 c. Here, N labels the (K,L)-coordinates. These are orivuded in "Figure3_KL.dat", where for a number N the N-th row presents the K and L values in the first and second column, respectively.</p> <p>The files "Fig2a.dat" and "Fig2b.dat" contain the datapoints presented in Figure 2a and Figure 2b, where the first column corresponds to the x-axis coordinate and the second column to the recorded intensity.</p>
Normalized profile of industrial demand response
<p>Normalized profile of industrial demand response based on an industrial facility in Austria in 2019</p>
Armature formula of P1–P4 as follows: P5 (Fig. 2B). With outer seta of BENP arising from long setophore. Endopodal lobe triangular, reaching middle of exopod; with small spinules along outer margin and at base of inner setae; with five elements – one outer subdistal, one apical and one inner subdistal normal seta, and two inner bifurcate elements. Exopod elongate, 2.8 times as long as wide; with spinules along inner margin and with few proximal outer spinules; with six elements – three outer slender, short setae, two apical elements, of which outermost one shorter, and one inner seta. in Proposal of new genera and species of the subfamily Diosaccinae (Copepoda: Harpacticoida: Miraciidae)
Armature formula of P1–P4 as follows: P5 (Fig. 2B). With outer seta of BENP arising from long setophore. Endopodal lobe triangular, reaching middle of exopod; with small spinules along outer margin and at base of inner setae; with five elements – one outer subdistal, one apical and one inner subdistal normal seta, and two inner bifurcate elements. Exopod elongate, 2.8 times as long as wide; with spinules along inner margin and with few proximal outer spinules; with six elements – three outer slender, short setae, two apical elements, of which outermost one shorter, and one inner seta.
Normalized Difference Vegetation Index for Andalusia Region based on MODIS
<p><strong>Normalized Difference Vegetation Index</strong> (NDVI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the ndvi was calculated. </p> <p>NDVI quantifies vegetation by measuring the difference between near-infrared and red light (which vegetation absorbs). NDVI is a standardized way to measure healthy vegetation. High NDVI values indicates healthy vegetation.</p> <p><br> Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description.</p> <p>spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842</p> <p> </p> <p>To easily manage the data, each file follow name structure:</p> <p>YYYYMMDD_medgold_workpackage_AoI_sensor_index.</p> <p>YYYYMMDD: Imagery acquisition date</p> <p>medgold: Project name</p> <p>sensor: sensor name</p> <p>workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Grape Wine sector, WP4- Durum wheat pasta sector)</p> <p>AoI: Andalusia, Douro Valley.</p> <p>Index: NDVI, NMDI, NDWI</p> <p>Example:</p> <p>20000218_medgold_wp2_andalusia_MOD09A1_ndvi</p> <p> </p>
Normalized Difference Water Index for Douro Valley based on MODIS
<p><strong>Normalized Difference Water Index </strong>(NDWI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the ndwi was calculated. </p> <p>The Normalized Difference Water Index (NDWI) (Gao, 1996) is a satellite-derived index from the Near-Infrared (NIR) and Short Wave Infrared (SWIR) channels. Its usefulness for drought monitoring and early warning has been demonstrated in different studies (e.g., Gu et al., 2007; Ceccato et al., 2002). It is computed using the near infrared (NIR) and the short wave infrared (SWIR) reflectance, which makes it sensitive to changes in liquid water content and in spongy mesophyll of vegetation canopies (Gao, 1996 ; Ceccato et al., 2001).</p> <p><a href="https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_ndwi.pdf">https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_ndwi.pdf</a></p> <p>Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description.</p> <p>spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842</p> <p> </p> <p>To easily manage the data, each file follow name structure:</p> <p>YYYYMMDD_medgold_workpackage_AoI_sensor_index.</p> <p>YYYYMMDD: Imagery acquisition date</p> <p>medgold: Project name</p> <p>sensor: sensor name</p> <p>workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector)</p> <p>Aoi: Andalusia, Douro Valley.</p> <p>Index: NDVI, NMDI, NDWI</p> <p>Example:</p> <p>20000218_medgold_wp3_douro_MOD09A1_ndwi</p> <p> </p>
Normalized Multi-band Drought Index for Douro Valley based on MODIS
<p><strong>Normalized Multi-band Drought Index</strong> (NMDI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the nmdi was calculated. </p> <p>NMDI uses the 860 nm channel as the reference; instead of using a single liquid water absorption channel, however, it uses the difference between two liquid water absorption channels centered at 1640 nm and 2130 nm as the soil and vegetation moisture sensitive band. Analysis revealed that by combining information from multiple near infrared, and short wave infrared channels, NMDI has enhanced the sensitivity to drought severity, and is well suited to estimate both soil and vegetation moisture.( <a href="https://agupubs.onlinelibrary.wiley.com/action/doSearch?ContribAuthorStored=Wang%2C+Lingli">Lingli Wang</a>, 2007)</p> <p><a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007GL031021">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007GL031021</a></p> <p> </p> <p>Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description.</p> <p>spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842</p> <p> </p> <p>To easily manage the data, each file follow name structure:</p> <p>YYYYMMDD_medgold_workpackage_AoI_sensor_index.</p> <p>YYYYMMDD: Imagery acquisition date</p> <p>medgold: Project name</p> <p>sensor: sensor name</p> <p>workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector)</p> <p>Aoi: Andalusia, Douro Valley.</p> <p>Index: NDVI, NMDI, NDWI</p> <p>Example:</p> <p>20000218_medgold_wp3_douro_MOD09A1_nmdi</p>
Normalized Difference Vegetation Index for Douro Valley based on MODIS
<p><strong>Normalized Difference Vegetation Index</strong> (NDVI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the ndvi was calculated. </p> <p>NDVI quantifies vegetation by measuring the difference between near-infrared and red light (which vegetation absorbs). NDVI is a standardized way to measure healthy vegetation. High NDVI values indicates healthy vegetation.</p> <p>Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description.</p> <p>spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842</p> <p> </p> <p>To easily manage the data, each file follow name structure:</p> <p>YYYYMMDD_medgold_workpackage_AoI_sensor_index.</p> <p>YYYYMMDD: Imagery acquisition date</p> <p>medgold: Project name</p> <p>sensor: sensor name</p> <p>workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector)</p> <p>Aoi: Andalusia, Douro Valley.</p> <p>Index: NDVI, NMDI, NDWI</p> <p>Example:</p> <p>20000218_medgold_wpe_douro_MOD09A1_ndvi</p> <p> </p>
Normalized Multi-band Drought Index for Andalusia Region based on MODIS
<p><strong>Normalized Multi-band Drought Index </strong>(NMDI) was calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the nmdi was calculated. </p> <p> </p> <p>NMDI uses the 860 nm channel as the reference; instead of using a single liquid water absorption channel, however, it uses the difference between two liquid water absorption channels centered at 1640 nm and 2130 nm as the soil and vegetation moisture sensitive band. Analysis revealed that by combining information from multiple near infrared, and short wave infrared channels, NMDI has enhanced the sensitivity to drought severity, and is well suited to estimate both soil and vegetation moisture.(Lingli Wang, 2007)</p> <p><a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007GL031021">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007GL031021</a></p> <p> </p> <p>Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description.</p> <p>spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842</p> <p> </p> <p>To easily manage the data, each file follow name structure:</p> <p>YYYYMMDD_medgold_workpackage_AoI_sensor_index.</p> <p>YYYYMMDD: Imagery acquisition date</p> <p>medgold: Project name</p> <p>sensor: sensor name</p> <p>workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector)</p> <p>Aoi: Andalusia, Douro Valley.</p> <p>Index: NDVI, NMDI, NDWI</p> <p>Example:</p> <p>20000218_medgold_wp2_andalusia_MOD09A1_nmdi</p> <p> </p>
Normalized Difference Water Index for Andalusia Region based on MODIS
<p><strong>Normalized Difference Water Index</strong> (NDWI) calculated using MODIS09 imagery provided by USGS/EROS.The original MODIS09 bands were used as data source and then the ndwi was calculated. </p> <p>The Normalized Difference Water Index (NDWI) (Gao, 1996) is a satellite-derived index from the Near-Infrared (NIR) and Short Wave Infrared (SWIR) channels. Its usefulness for drought monitoring and early warning has been demonstrated in different studies (e.g., Gu et al., 2007; Ceccato et al., 2002). It is computed using the near infrared (NIR) and the short wave infrared (SWIR) reflectance, which makes it sensitive to changes in liquid water content and in spongy mesophyll of vegetation canopies (Gao, 1996 ; Ceccato et al., 2001).</p> <p><a href="https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_ndwi.pdf">https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_ndwi.pdf</a></p> <p> </p> <p>Each compressed file contains the NDVI by years. Internally each file have an ISO TC 211 metadata with a complete geographical description.</p> <p>spatial resolution: 463.313m<br> format: GeoTiff<br> reference system:SR-ORG 6842</p> <p> </p> <p>To easily manage the data, each file follow name structure:</p> <p>YYYYMMDD_medgold_workpackage_AoI_sensor_index.</p> <p>YYYYMMDD: Imagery acquisition date</p> <p>medgold: Project name</p> <p>sensor: sensor name</p> <p>workpackage: sectorial workpackage name ( WP2 - Olive Oil Sector, WP3 – Wine Grape sector, WP4- Durum wheat pasta sector)</p> <p>Aoi: Andalusia, Douro Valley.</p> <p>Index: NDVI, NMDI, NDWI</p> <p>Example:</p> <p>20000218_medgold_wp2_andalusia_MOD09A1_ndwi</p>
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.