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Spatial distribution of housing rental value in Amsterdam 1647-1652
<p>This dataset visualises the spatial distribution of the rental value in Amsterdam between 1647 and 1652. The source of rental value comes from the <em>Verponding </em>registration in Amsterdam. The <em>verponding</em> or the ‘<em>Verpondings-quohieren van den 8sten penning</em>’ was a tax in the Netherlands on the 8<sup>th</sup> penny of the rental value of immovable property that had to be paid annually. In Amsterdam, the citywide <em>verponding </em>registration started in 1647 and continued into the early 19<sup>th</sup> century. With the introduction of the cadastre system in 1810, the <em>verponding</em> came to an end.</p> <p>The original tax registration is kept in the Amsterdam City Archives (Archief nr. <a href="https://archief.amsterdam/inventarissen/details/5044/withscans/0/findingaid/5044/start/0/limit/10/flimit/5">5044</a>) and the four registration books transcribed in this dataset are Archief 5044, inventory <a href="https://archief.amsterdam/inventarissen/scans/5044/33.2/start/0/limit/10/highlight/2">255</a>, 273, <a href="https://archief.amsterdam/inventarissen/scans/5044/33.28/start/0/limit/10/highlight/4">281</a>, <a href="https://archief.amsterdam/inventarissen/scans/5044/33.31/start/0/limit/10/highlight/4">284</a>. The <em>verponding </em>was collected by districts (<em>wijken</em>). The tax collectors documented their collecting route by writing down the street or street-section names as they proceed. For each property, the collector wrote down the names of the owner and, if applicable, the renter (after ‘per’), and the estimated rental value of the property (in guilders). Next to the rental value was the tax charged (in guilders and stuivers). Below the owner/renter names and rental value were the records of tax payments by year.</p> <p>This dataset digitises four registration books of the <em>verponding </em>between 1647 and 1652 in two ways. First, it transcribes the rental value of all real estate properties listed in the registrations. The names of the owners/renters are transcribed only selectively, focusing on the properties that exceeded an annual rental value of 300 guilders. These transcriptions can be found in Verponding1647-1652.csv. For a detailed introduction to the data, see Verponding1647-1652_data_introduction.txt.</p> <p>Second, it geo-references the registrations based on the street names and the reconstruction of tax collectors’ travel routes in the <em>verponding</em>. The tax records are then plotted on the historical map of Amsterdam using the first cadaster of 1832 as a reference. Since the geo-reference is based on the street or street sections, the location of each record/house may not be the exact location but rather a close proximation of the possible locations based on the street names and the sequence of the records on the same street or street section. Therefore, this geo-referenced <em>verponding</em> can be used to visualise the rental value distribution in Amsterdam between 1647 and 1652. The preview below shows an extrapolation of rental values in Amsterdam. And for the geo-referenced GIS files, see Verponding_wijken.shp.</p> <p><strong>GIS specifications:</strong></p> <p>Coordination Reference System (CRS): Amersfoort/RD New (ESPG:28992)</p> <p>Historical map tiles <a href="https://images.diginfra.net/webmapper/maps/berckenrode/{z}/{x}/{y}.png">URL</a> (From <a href="https://tiles.amsterdamtimemachine.nl/#16/52.3691/4.8935">Amsterdam Time Machine</a>)</p> <p> </p> <p><strong>NB: This <em>verponding</em> dataset is a provisional version. The georeferenced points and the name transcriptions might contain errors and need to be treated with caution. </strong></p> <p><strong>Contributors</strong></p> <ul> <li><strong>Historical and archival research</strong>: Weixuan Li, Bart Reuvekamp</li> <li><strong>Plotting of geo-referenced points: </strong>Bart Reuvekamp</li> <li><strong>Spatial analysis</strong>: Weixuan Li</li> <li><strong>Mapping software</strong>: QGIS</li> <li><strong>Acknowledgements</strong>: Virtual Interiors project, Daan de Groot</li> </ul> <p> </p>
Data on public perceptions of, attitudes towards, and values for managing urban green infrastructure for carbon, biodiversity, and well-being outcomes in Helsinki, Finland
<p>A public participatory GIS -survey dataset detailing public perceptions of, attitudes towards, and values for managing urban green infrastructure for carbon, biodiversity, and well-being outcomes in Helsinki, Finland.</p>
Hydropower dataset of hourly inflow values for European bidding zones for ACDC-ESM
<p><strong>Energy Climate dataset consistent with ENTSO-E Pan-European Climatic Database (PECD 2021.3) in CSV and netCDF format</strong></p> <p> </p> <p><strong>TL;DR</strong>: this is a nationally aggregated hourly dataset for the capacity factors per unit installed capacity for storage hydropower plants and run-of-river hydropower plants in the European region. All the data is provided for 30 climatic years (1981-2010).</p> <p> </p> <p><strong>Method Description </strong><br> The hydro inflow data is based on historical river runoff reanalysis data simulated by the E-HYPE model. E-HYPE is a pan-European model developed by The Swedish Meteorological and Hydrological Institute (SMHI), which describes hydrological processes including flow paths at the subbasin level. E-hype only provides the time series of daily river runoff entering the inlet of each European subbasin over 1981-2010. To match the operational resolution of the dispatch model, we linearly downscale these time series to hourly. By summing up runoff associated with the inlet subbasins of each country, we also obtain the country-level river runoff.</p> <p>The hydro inflow time series per country is defined as the normalized energy inflows (per unit installed capacity of hydropower) embodied in the country-level river runoff. A dispatch model can be used to decides whether the energy inflows are actually used for electricity generation, stored, or spilled (in case the storage reservoir is already full).</p> <p><strong>Data coverage</strong><br> This dataset considers two types of hydropower plants, namely storage hydropower plant (STO) and run-of-river hydropower plant (ROR). Not all countries have both types of hydropower plants installed (see table). </p> <p>The countries and their acronyms for both technologies included in this dataset are:</p> <table> <thead> <tr> <th scope="col">Country</th> <th scope="col">Run-of-River </th> <th scope="col">Storage</th> </tr> </thead> <tbody> <tr> <td>Austria</td> <td>AT_ROR</td> <td>AT_STO</td> </tr> <tr> <td>Belgium</td> <td>BE_ROR</td> <td>BE_STO</td> </tr> <tr> <td>Bulgaria</td> <td>BG_ROR</td> <td>BG_STO</td> </tr> <tr> <td>Switzerland</td> <td>CH_ROR</td> <td>CH_STO</td> </tr> <tr> <td>Cyprus</td> <td>CZ_ROR</td> <td>CZ_STO</td> </tr> <tr> <td>Germany</td> <td>DE_ROR</td> <td>DE_STO</td> </tr> <tr> <td>Denmark</td> <td>DK_ROR</td> <td> </td> </tr> <tr> <td>Estonia</td> <td>EE_ROR</td> <td> </td> </tr> <tr> <td>Greece</td> <td>EL_ROR</td> <td>EL_STO</td> </tr> <tr> <td>Spain</td> <td>ES_ROR</td> <td>ES_STO</td> </tr> <tr> <td>Finland</td> <td>FI_ROR</td> <td>FI_STO</td> </tr> <tr> <td>France</td> <td>FR_ROR</td> <td>FR_STO</td> </tr> <tr> <td>Great Britain</td> <td>GB_ROR</td> <td>GB_STO</td> </tr> <tr> <td>Croatia</td> <td>HR_ROR</td> <td>HR_STO</td> </tr> <tr> <td>Hungary</td> <td>HU_ROR</td> <td>HU_STO</td> </tr> <tr> <td>Ireland</td> <td>IE_ROR</td> <td>IE_STO</td> </tr> <tr> <td>Italy</td> <td>IT_ROR</td> <td>IT_STO</td> </tr> <tr> <td>Luxembourg</td> <td>LU_ROR</td> <td> </td> </tr> <tr> <td>Latvia</td> <td>LV_ROR</td> <td> </td> </tr> <tr> <td>the Netherlands</td> <td>NL_ROR</td> <td> </td> </tr> <tr> <td>Norway</td> <td>NO_ROR</td> <td>NO_STO</td> </tr> <tr> <td>Poland</td> <td>PL_ROR</td> <td>PL_STO</td> </tr> <tr> <td>Portugal</td> <td>PT_ROR</td> <td>PT_STO</td> </tr> <tr> <td>Romania</td> <td>RO_ROR</td> <td>RO_STO</td> </tr> <tr> <td>Sweden</td> <td>SE_ROR</td> <td>SE_STO</td> </tr> <tr> <td>Slovenia</td> <td>SI_ROR</td> <td>SI_STO</td> </tr> <tr> <td>Slovakia</td> <td>SK_ROR</td> <td>SK_STO</td> </tr> </tbody> </table> <p> </p> <p><strong>Data structure description</strong><br> The files is provided in CSV (.csv) format with a comma (,) as separator and double-quote mark (") as text indicator. The first row stores the column labels. The columns contain the following:</p> <ul> <li>first column (or A) contains the row number <ul> <li>Label: unlabeled</li> <li>Contents: interger range [1,262968]</li> </ul> </li> <li>second column (or B) contains the valid-time <ul> <li>Label: T1h</li> <li>Contents represent time with text as [DD/MM/YYYY HH:MM])</li> </ul> </li> <li>column 3-52 (or C-AY) each contain the capacity factor for each valid combination of a country and hydropower plant type <ul> <li>Label: XX_YYY the two letter country code (XX) and the hydropower plant type (YYY) acronym for storage hydropower plant (STO) and run-of-river hydropower plant (ROR)</li> <li>Contents represent the capacity factor as a floating value in the range [0,1], the decimal separator is a point (.).</li> </ul> </li> </ul> <p><strong>DISCLAIMER</strong>: <em>the content of this dataset has been created with the greatest possible care. However, we invite to use the original data for critical applications and studies. </em></p>
F-IDF values at USGS stream gage sites
<p>This nc file contains F-IDF values at streamflow gage sites over the CONUS.</p> <p>The header of this netcdf file is shown below:</p> <p>----------------------------------------------------------------------------------------------------------</p> <p>netcdf flashiness_dataset {<br> dimensions:<br> stnid = 6987 ;<br> duration = 6 ;<br> frequency = 6 ;<br> variables:<br> double flashiness(stnid, duration, frequency) ;<br> flashiness:description = "It is calculated with the slope of a given time window (cfs/15min*len(window)), divided by the drainage area (sqkm) and convert to a standardized unit" ;<br> flashiness:units = "mm/h^2" ;<br> flashiness:_FillValue = NaN ; area (sqkm)" ;<br> string stnid(stnid) ;<br> int64 frequency(frequency) ;<br> int64 duration(duration) ;<br> double lon(stnid) ;<br> lon:_FillValue = NaN ;<br> double lat(stnid) ;<br> lat:_FillValue = NaN ;<br> double area(stnid) ;<br> area:_FillValue = NaN ;<br> area:units = "sqkm" ;<br> double data_length(stnid) ;<br> data_length:_FillValue = NaN ;<br> data_length:units = "years" ;<br> data_length:description = "years of available data from USGS 15-min observation" ;<br> double dor_pc_pva(stnid) ;<br> dor_pc_pva:_FillValue = NaN ;<br> dor_pc_pva:units = "percent" ;<br> dor_pc_pva:long_name = "degree of regulation" ;<br> dor_pc_pva:description = "degree of regulation retrieved from hydrobasin V10 level 12" ;<br> double slp_dg_uav(stnid) ;<br> slp_dg_uav:_FillValue = NaN ;<br> slp_dg_uav:units = "degree" ;<br> slp_dg_uav:long_name = "Terrain slope" ;<br> slp_dg_uav:description = "Terrain slope of total watershed upstream of a pour point" ;<br> double sgr_dk_sav(stnid) ;<br> sgr_dk_sav:_FillValue = NaN ;<br> sgr_dk_sav:units = "degree" ;<br> sgr_dk_sav:long_name = "Stream gradient" ;<br> sgr_dk_sav:description = "the stream gradient was calculated as the ratio between the elevation drop within the river reach (i.e. the difference between min. and max. elevation along the reach) and the length of the reach." ;<br> double tmp_dc_uyr(stnid) ;<br> tmp_dc_uyr:_FillValue = NaN ;<br> tmp_dc_uyr:units = "degree celsius" ;<br> tmp_dc_uyr:long_name = "Annual mean air temperature" ;<br> tmp_dc_uyr:description = "Annual mean air temperature retrieved from WorldClim, station-based monitoring network" ;<br> double pre_mm_uyr(stnid) ;<br> pre_mm_uyr:_FillValue = NaN ;<br> pre_mm_uyr:units = "mm" ;<br> pre_mm_uyr:long_name = "Annual mean precipitation" ;<br> pre_mm_uyr:description = "Annual mean precipitation retrieved from WorldClim, station-based monitoring network and interpolated by the thin-plate smoothing spline algorithm" ;<br> double pet_mm_uyr(stnid) ;<br> pet_mm_uyr:_FillValue = NaN ;<br> pet_mm_uyr:units = "mm" ;<br> pet_mm_uyr:long_name = "Annual mean potential evaporation" ;<br> pet_mm_uyr:description = "Annual mean PET based on termperature inputs from WorldClim and a simple temperature-based transfer model" ;<br> double aet_mm_uyr(stnid) ;<br> aet_mm_uyr:_FillValue = NaN ;<br> aet_mm_uyr:units = "mm" ;<br> aet_mm_uyr:long_name = "Annual mean actural evaporation" ;<br> aet_mm_uyr:description = "Annual mean AET based on the Global High-Resolution Soil-Water Balance dataset which contains gridded estimates of actual evapotranspiration and soil water deficit" ;<br> double ari_ix_uav(stnid) ;<br> ari_ix_uav:_FillValue = NaN ;<br> ari_ix_uav:units = "" ;<br> ari_ix_uav:long_name = "Global aridity index" ;<br> ari_ix_uav:description = "The Global Aridity Index (Global-Aridity) is modeled using data from WorldClim as input parameters" ;<br> double cmi_ix_uyr(stnid) ;<br> cmi_ix_uyr:_FillValue = NaN ;<br> cmi_ix_uyr:units = "" ;<br> cmi_ix_uyr:long_name = "Global climate moisture index" ;<br> cmi_ix_uyr:description = "The Climate Moisture Index (CMI) was derived from the annual precipitation (P) and potential evapotranspiration (PET) datasets as provided by the WorldClim v1.4" ;<br> double snw_pc_uyr(stnid) ;<br> snw_pc_uyr:_FillValue = NaN ;<br> snw_pc_uyr:units = "%" ;<br> snw_pc_uyr:long_name = "snow cover extent" ;<br> snw_pc_uyr:description = "data obtained from The MODIS/Aqua Snow Cover Daily L3 Global 500m Grid (MYD10A1)" ;<br> double cly_pc_uav(stnid) ;<br> cly_pc_uav:_FillValue = NaN ;<br> cly_pc_uav:units = "%" ;<br> cly_pc_uav:long_name = "clay fraction in soils" ;<br> cly_pc_uav:description = "Data obtained from SoilGrids1km" ;<br> double slt_pc_uav(stnid) ;<br> slt_pc_uav:_FillValue = NaN ;<br> slt_pc_uav:units = "%" ;<br> slt_pc_uav:long_name = "silt fraction in soils" ;<br> slt_pc_uav:description = "Data obtained from SoilGrids1km" ;<br> double snd_pc_uav(stnid) ;<br> snd_pc_uav:_FillValue = NaN ;<br> snd_pc_uav:units = "%" ;<br> snd_pc_uav:long_name = "sand fraction in soils" ;<br> snd_pc_uav:description = "Data obtained from SoilGrids1km" ;<br> double swc_pc_uyr(stnid) ;<br> swc_pc_uyr:_FillValue = NaN ;<br> swc_pc_uyr:units = "%" ;<br> swc_pc_uyr:long_name = "Soil water content" ;<br> swc_pc_uyr:description = "Soil water content is provided as part of the Global High-Resolution Soil-Water Balance dataset which contains gridded estimates of actual evapotranspiration and soil water deficit" ;<br> double kar_pc_use(stnid) ;<br> kar_pc_use:_FillValue = NaN ;<br> kar_pc_use:units = "%" ;<br> kar_pc_use:long_name = "Karst area extent" ;<br> kar_pc_use:description = "The World Map of Carbonate Rock Outcrops represents an upper limit of the area of exposed karst terrain." ;<br> double ero_kh_uav(stnid) ;<br> ero_kh_uav:_FillValue = NaN ;<br> ero_kh_uav:units = "kg/hectare per year" ;<br> ero_kh_uav:long_name = "Soil erosion" ;<br> string ero_kh_uav:description = "GloSEM erosion estimates were produced with a high resolution (250 × 250 m) global potential soil erosion model, using a combination of remote sensing, GIS modelling and census data" ;<br> double pop_ct_usu(stnid) ;<br> pop_ct_usu:_FillValue = NaN ;<br> pop_ct_usu:units = "count" ;<br> pop_ct_usu:long_name = "Population count" ;<br> pop_ct_usu:description = "The Gridded Population of the World (GPW) database." ;<br> double urb_pc_use(stnid) ;<br> urb_pc_use:_FillValue = NaN ;<br> urb_pc_use:units = "%" ;<br> urb_pc_use:long_name = "Urban extent" ;<br> urb_pc_use:description = "The Global Human Settlement (GHS) framework produces global spatial information about the human presence on the planet over time" ;<br> double rdd_mk_uav(stnid) ;<br> rdd_mk_uav:_FillValue = NaN ;<br> rdd_mk_uav:units = "meters per km^2" ;<br> rdd_mk_uav:long_name = "Road density" ;<br> rdd_mk_uav:description = "The Global Roads Inventory Project (GRIP) dataset" ;<br> double dis_m3_pyr(stnid) ;<br> dis_m3_pyr:_FillValue = NaN ;<br> dis_m3_pyr:units = "cms" ;<br> dis_m3_pyr:long_name = "Natural discharge" ;<br> dis_m3_pyr:description = "Simulated discharge by WaterGAP" ;<br> double run_mm_syr(stnid) ;<br> run_mm_syr:_FillValue = NaN ;<br> run_mm_syr:units = "mm" ;<br> run_mm_syr:long_name = "Land surface runoff" ;<br> run_mm_syr:description = "Simulated land surface runoff by WaterGAP" ;<br> double inu_pc_umx(stnid) ;<br> inu_pc_umx:_FillValue = NaN ;<br> inu_pc_umx:units = "%" ;<br> inu_pc_umx:long_name = "Annual maximum inundation extent " ;<br> inu_pc_umx:description = "GIEMS-D15 is a high-resolution global inundation map at a pixel size of 15 arc-seconds" ;<br> double ria_ha_usu(stnid) ;<br> ria_ha_usu:_FillValue = NaN ;<br> ria_ha_usu:units = "hectares" ;<br> ria_ha_usu:long_name = "River area" ;<br> ria_ha_usu:description = "River area was calculated using the the HydroSHEDS database at 15 arc-second resolution. It is based on a rating curve" ;<br> double riv_tc_usu(stnid) ;<br> riv_tc_usu:_FillValue = NaN ;<br> riv_tc_usu:units = "1000 m^3" ;<br> riv_tc_usu:long_name = "River volume" ;<br> riv_tc_usu:description = "River volume was calculated using the the HydroSHEDS database at 15 arc-second resolution." ;<br> double gwt_cm_sav(stnid) ;<br> gwt_cm_sav:_FillValue = NaN ;<br> gwt_cm_sav:units = "cm" ;<br> gwt_cm_sav:long_name = "Groundwater table depth" ;<br> gwt_cm_sav:description = "Fan et al. (2013) compiled global observations of water table depth from government archives and literature" ;<br> double ele_mt_uav(stnid) ;<br> ele_mt_uav:_FillValue = NaN ;<br> ele_mt_uav:units = "m" ;<br> ele_mt_uav:long_name = "Elevation" ;<br> ele_mt_uav:description = "Elevation above mean sea level based on EarthEnv-DEM90" ;<br> double glc_pc_u01(stnid) ;<br> glc_pc_u01:_FillValue = NaN ;<br> glc_pc_u01:units = "%" ;<br> glc_pc_u01:long_name = "Land cover area extent" ;<br> glc_pc_u01:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u02(stnid) ;<br> glc_pc_u02:_FillValue = NaN ;<br> glc_pc_u02:units = "%" ;<br> glc_pc_u02:long_name = "Land cover area extent" ;<br> glc_pc_u02:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u03(stnid) ;<br> glc_pc_u03:_FillValue = NaN ;<br> glc_pc_u03:units = "%" ;<br> glc_pc_u03:long_name = "Land cover area extent" ;<br> glc_pc_u03:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u04(stnid) ;<br> glc_pc_u04:_FillValue = NaN ;<br> glc_pc_u04:units = "%" ;<br> glc_pc_u04:long_name = "Land cover area extent" ;<br> glc_pc_u04:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u05(stnid) ;<br> glc_pc_u05:_FillValue = NaN ;<br> glc_pc_u05:units = "%" ;<br> glc_pc_u05:long_name = "Land cover area extent" ;<br> glc_pc_u05:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u06(stnid) ;<br> glc_pc_u06:_FillValue = NaN ;<br> glc_pc_u06:units = "%" ;<br> glc_pc_u06:long_name = "Land cover area extent" ;<br> glc_pc_u06:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u07(stnid) ;<br> glc_pc_u07:_FillValue = NaN ;<br> glc_pc_u07:units = "%" ;<br> glc_pc_u07:long_name = "Land cover area extent" ;<br> glc_pc_u07:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u08(stnid) ;<br> glc_pc_u08:_FillValue = NaN ;<br> glc_pc_u08:units = "%" ;<br> glc_pc_u08:long_name = "Land cover area extent" ;<br> glc_pc_u08:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u09(stnid) ;<br> glc_pc_u09:_FillValue = NaN ;<br> glc_pc_u09:units = "%" ;<br> glc_pc_u09:long_name = "Land cover area extent" ;<br> glc_pc_u09:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u10(stnid) ;<br> glc_pc_u10:_FillValue = NaN ;<br> glc_pc_u10:units = "%" ;<br> glc_pc_u10:long_name = "Land cover area extent" ;<br> glc_pc_u10:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u11(stnid) ;<br> glc_pc_u11:_FillValue = NaN ;<br> glc_pc_u11:units = "%" ;<br> glc_pc_u11:long_name = "Land cover area extent" ;<br> glc_pc_u11:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u12(stnid) ;<br> glc_pc_u12:_FillValue = NaN ;<br> glc_pc_u12:units = "%" ;<br> glc_pc_u12:long_name = "Land cover area extent" ;<br> glc_pc_u12:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u13(stnid) ;<br> glc_pc_u13:_FillValue = NaN ;<br> glc_pc_u13:units = "%" ;<br> glc_pc_u13:long_name = "Land cover area extent" ;<br> glc_pc_u13:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u14(stnid) ;<br> glc_pc_u14:_FillValue = NaN ;<br> glc_pc_u14:units = "%" ;<br> glc_pc_u14:long_name = "Land cover area extent" ;<br> glc_pc_u14:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u15(stnid) ;<br> glc_pc_u15:_FillValue = NaN ;<br> glc_pc_u15:units = "%" ;<br> glc_pc_u15:long_name = "Land cover area extent" ;<br> glc_pc_u15:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u16(stnid) ;<br> glc_pc_u16:_FillValue = NaN ;<br> glc_pc_u16:units = "%" ;<br> glc_pc_u16:long_name = "Land cover area extent" ;<br> glc_pc_u16:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u17(stnid) ;<br> glc_pc_u17:_FillValue = NaN ;<br> glc_pc_u17:units = "%" ;<br> glc_pc_u17:long_name = "Land cover area extent" ;<br> glc_pc_u17:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u18(stnid) ;<br> glc_pc_u18:_FillValue = NaN ;<br> glc_pc_u18:units = "%" ;<br> glc_pc_u18:long_name = "Land cover area extent" ;<br> glc_pc_u18:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u19(stnid) ;<br> glc_pc_u19:_FillValue = NaN ;<br> glc_pc_u19:units = "%" ;<br> glc_pc_u19:long_name = "Land cover area extent" ;<br> glc_pc_u19:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u20(stnid) ;<br> glc_pc_u20:_FillValue = NaN ;<br> glc_pc_u20:units = "%" ;<br> glc_pc_u20:long_name = "Land cover area extent" ;<br> glc_pc_u20:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u21(stnid) ;<br> glc_pc_u21:_FillValue = NaN ;<br> glc_pc_u21:units = "%" ;<br> glc_pc_u21:long_name = "Land cover area extent" ;<br> glc_pc_u21:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u22(stnid) ;<br> glc_pc_u22:_FillValue = NaN ;<br> glc_pc_u22:units = "%" ;<br> glc_pc_u22:long_name = "Land cover area extent" ;<br> glc_pc_u22:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double pnv_pc_u01(stnid) ;<br> pnv_pc_u01:_FillValue = NaN ;<br> pnv_pc_u01:units = "%" ;<br> pnv_pc_u01:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u01:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u02(stnid) ;<br> pnv_pc_u02:_FillValue = NaN ;<br> pnv_pc_u02:units = "%" ;<br> pnv_pc_u02:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u02:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u03(stnid) ;<br> pnv_pc_u03:_FillValue = NaN ;<br> pnv_pc_u03:units = "%" ;<br> pnv_pc_u03:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u03:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u04(stnid) ;<br> pnv_pc_u04:_FillValue = NaN ;<br> pnv_pc_u04:units = "%" ;<br> pnv_pc_u04:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u04:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u05(stnid) ;<br> pnv_pc_u05:_FillValue = NaN ;<br> pnv_pc_u05:units = "%" ;<br> pnv_pc_u05:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u05:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u06(stnid) ;<br> pnv_pc_u06:_FillValue = NaN ;<br> pnv_pc_u06:units = "%" ;<br> pnv_pc_u06:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u06:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u07(stnid) ;<br> pnv_pc_u07:_FillValue = NaN ;<br> pnv_pc_u07:units = "%" ;<br> pnv_pc_u07:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u07:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u08(stnid) ;<br> pnv_pc_u08:_FillValue = NaN ;<br> pnv_pc_u08:units = "%" ;<br> pnv_pc_u08:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u08:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u09(stnid) ;<br> pnv_pc_u09:_FillValue = NaN ;<br> pnv_pc_u09:units = "%" ;<br> pnv_pc_u09:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u09:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u10(stnid) ;<br> pnv_pc_u10:_FillValue = NaN ;<br> pnv_pc_u10:units = "%" ;<br> pnv_pc_u10:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u10:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u11(stnid) ;<br> pnv_pc_u11:_FillValue = NaN ;<br> pnv_pc_u11:units = "%" ;<br> pnv_pc_u11:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u11:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u12(stnid) ;<br> pnv_pc_u12:_FillValue = NaN ;<br> pnv_pc_u12:units = "%" ;<br> pnv_pc_u12:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u12:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u13(stnid) ;<br> pnv_pc_u13:_FillValue = NaN ;<br> pnv_pc_u13:units = "%" ;<br> pnv_pc_u13:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u13:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u14(stnid) ;<br> pnv_pc_u14:_FillValue = NaN ;<br> pnv_pc_u14:units = "%" ;<br> pnv_pc_u14:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u14:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u01(stnid) ;<br> wet_pc_u01:_FillValue = NaN ;<br> wet_pc_u01:units = "%" ;<br> wet_pc_u01:long_name = "Wet land area extent" ;<br> wet_pc_u01:description = "Wetland extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u02(stnid) ;<br> wet_pc_u02:_FillValue = NaN ;<br> wet_pc_u02:units = "%" ;<br> wet_pc_u02:long_name = "Wet land area extent" ;<br> wet_pc_u02:description = "Wetland extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u03(stnid) ;<br> wet_pc_u03:_FillValue = NaN ;<br> wet_pc_u03:units = "%" ;<br> wet_pc_u03:long_name = "Wet land area extent" ;<br> wet_pc_u03:description = "Wetland extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u04(stnid) ;<br> wet_pc_u04:_FillValue = NaN ;<br> wet_pc_u04:units = "%" ;<br> wet_pc_u04:long_name = "Wet land area extent" ;<br> wet_pc_u04:description = "Wetland extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u05(stnid) ;<br> wet_pc_u05:_FillValue = NaN ;<br> wet_pc_u05:units = "%" ;<br> wet_pc_u05:long_name = "Wet land area extent" ;<br> wet_pc_u05:description = "Wetland extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u06(stnid) ;<br> wet_pc_u06:_FillValue = NaN ;<br> wet_pc_u06:units = "%" ;<br> wet_pc_u06:long_name = "Wet land area extent" ;<br> wet_pc_u06:description = "Wetland extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u07(stnid) ;<br> wet_pc_u07:_FillValue = NaN ;<br> wet_pc_u07:units = "%" ;<br> wet_pc_u07:long_name = "Wet land area extent" ;<br> wet_pc_u07:description = "Wetland extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u08(stnid) ;<br> wet_pc_u08:_FillValue = NaN ;<br> wet_pc_u08:units = "%" ;<br> wet_pc_u08:long_name = "Wet land area extent" ;<br> wet_pc_u08:description = "Wetland extent for the drainage system. Data from EarthStat" ;</p> <p>// global attributes:<br> :description = "Created by Zhi Li (li1995@ou.edu)\nFitted empirical F-IDF values for USGS gauges." ;<br> }<br> </p>
Value creation stories anonymized open data set (Immunization Agenda 2030 Full Learning Cycle, 7 March - 20 June 2022)
<p># Title<br> Immunization Agenda 2030 (IA2030) 1st Movement Full Learning Cycle (FLC 2022) – “How are you doing?” Value Creation Stories Survey (Version 1.0)</p> <p># Research audience<br> Education researchers interested in the application of the “value creation stories” (VCS) conceptual framework elaborated by Etienne Wenger et al. in the study of communities of practice and other types of digital communities.</p> <p># Credits</p> <p>## Author<br> The Geneva Learning Foundation<br> 18 avenue Louis Casaï<br> CH-1209 Geneva, Switzerland<br> research@learning.foundation</p> <p>### Principal Investigator and corresponding author<br> Reda Sadki, The Geneva Learning Foundation (TGLF)<br> reda@learning.foundation</p> <p>## Project partners<br> Bridges to Development<br> University of South Australia Centre for Change and Complexity in Learning (C3L)</p> <p>## Roles and responsibilities<br> - Design: The Geneva Learning Foundation<br> - Implementation (sample collection): The Geneva Learning Foundation<br> - Processing: The Geneva Learning Foundation, Bridges for Development, Centre for Complexity and Change in Learning (C3L)<br> - Anonymization: The Geneva Learning Foundation and Bridges for Development<br> - Data cleaning: Bridges to Development<br> - Submission: The Geneva Learning Foundation</p> <p>## Funding sources or sponsorship that supported the data collection<br> Wellcome, Bill & Melinda Gates Foundation (BMGF)</p> <p>## Recommended citation<br> The Geneva Learning Foundation, 2023. Value Creation Stories (VCS) weekly feedback survey, 2022 Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) (Version 1.0). [Data Set]. The Geneva Learning Foundation. DOI: 10.5281/zenodo.7763922</p> <p># Description of the sample</p> <p>## File list:</p> <p>This file is IA2030_FLC_2022_Value_Creation_Stories.README.md</p> <p>IA2030-EN_FLC_2022_Value_Creation_Stories-questions_mapping.csv : List of the survey’s questions and their code in English as well as their unit. (21 questions) - Version 1: Geneva Learning Foundation, 31 March 2023. </p> <p>IA2030-EN_FLC_2022_Value_Creation_Stories.csv : Dataset Response of participants that replied in English. (n: 2101, obs:5601) - Version 1: Geneva Learning Foundation, 31 March 2023. </p> <p>IA2030-FR_FLC_2022_Value_Creation_Stories-questions_mapping.csv: List of the survey’s questions and their code in English as well as their unit. (21 questions) - Version 1: Geneva Learning Foundation, 31 March 2023.</p> <p>IA2030-FR_FLC_2022_Value_Creation_Stories-Google_translation.csv: Dataset Response of participants that replied in French translated to English using “Google Translate” (n: 1585, obs:4493) - Version 1: Geneva Learning Foundation, 31 March 2023.</p> <p>IA2030-FR_FLC_2022_Value_Creation_Stories.csv: Dataset Response of participants that replied in French (n: 1585, obs:4493) - Version 1: Geneva Learning Foundation, 31 March 2023. Relationship between files: The questions codes data set are the same code as the column variables and can be connected.</p> <p>## Relationship between files<br> The questions codes data set are the same code as the column variables and can be connected.</p> <p>## Related data sets<br> This is a subset of data collected by The Geneva Learning Foundation (TGLF) during the 1st IA2030 Full Learning Cycle (FLC). The complete data set is more comprehensive, and includes: demographic information (gender, country), health system information (respondent’s health system level), respondents’ analyses of challenges and priorities. </p> <p>Additional data sets for the first Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) are available from The Geneva Learning Foundation (TGLF) Insights Unit [insights@learning.foundation](insights@learning.foundation)</p> <p>## Other publicly accessible locations of the data<br> The Geneva Learning Foundation publishes data sets in relation to its Immunization Agenda 2030 (IA2030) Movement learning programme in the Zenodo open repository community: https://zenodo.org/communities/ia2030/</p> <p>## 1. Purpose and Objectives</p> <p>### Primary goal of the survey:<br> This survey had two goals in the context of TGLF’s IA2030 Movement Full Learning Cycle programme (2022): <br> 1. Provide an asynchronous mechanism for support between peers (participants) and from the TGLF team; and<br> 2. collect and measure programme participants’ value creation stories (VCS) during the programme.</p> <p>Martin de Laat’s “value creation stories” (VCS) has been used primarily in small-scale, qualitative studies of communities of practice, online forums, and education activities.</p> <p>This data set includes both quantitative (Likert) and qualitative (open text) responses to the VCS questions, collected over a period of four months (7 March – 20 June 2022) from a cohort that began with 6,185 participants on the start date.</p> <p>## 2. Population and Sample</p> <p>The target population were participants of the Geneva Learning Foundation’s Movement for Immunization Agenda 2030 (IA2030) learning programme. The initial cohort admitted to the programme was 6,185 individuals from 99 countries. Only participants who were formally admitted to the programme received the invitation to complete the survey.</p> <p>Programme participants were free to choose if and when to report (self-selection), and their responses were not checked against any other measures (self-reporting).</p> <p>### Languages: French and English</p> <p>## 3. Survey Design and Methods</p> <p>Data collection period: 7 March 2022 – 20 June 2022</p> <p>Between 7 March and 20 June 2023, participants in the Geneva Learning Foundation’s “Immunization Agenda 2030” (IA2030) Movement Full Learning Cycle (FLC) were asked to respond to a questionnaire titled “How are you doing?”.</p> <p>Participants received a personalized email with the request to share feedback about their experience during the week. The link to share feedback was also included in other reminder and information emails sent in response to participant needs.</p> <p>The first survey was launched on the 11 of March 2022 and the last at 17 of March 2022, totalizing 15 requests. Participants could answer the survey at any time and as many times that they wished.</p> <p> <br> The group of 6,185 participants grew over the course of the Cycle, as additional participants were able to join the initiative throughout the four-month period.</p> <p>### Software- or Instrument-specific information needed to interpret the data<br> - Automated translation of French data was performed using [Google Translate](https://translate.google.com/?sl=en&tl=fr&op=docs)<br> - Methods used for removing or anonymizing personal identifiers or sensitive information:<br> - Unique identifier: Unique identifiers were anonymized using MD5 Hashing via the web site [Miracle Salad](https://www.miraclesalad.com/webtools/md5.php.).Unique identifiers can be used to identify respondents who may have answered the survey more than once, at different points in time. This approach provides a method to anonymize sensitive data using MD5 hashing.*Limitation: MD5 hashing is a one-way function; it is not possible to dehash the data and recover the original information.**<br> - Macros developed in Excel to replace Country names in qualitative responses. (No country information were collected in this survey, but some respondents referred to their specific contexts in their responses.) The macro did not account for typos, in case any country information is found please contact: [research@learning.foundation](research@learning.foundation)</p> <p>### Data collection start and end dates:<br> 7 March 2023 until 20 June 2023</p> <p>#### Events or circumstances during data collection that may have influenced results:<br> No requests for responses were sent during TGLF’s “Term break” between 16-30 April 2022.</p> <p>## 4. Data Processing and Cleaning</p> <p>- Incomplete or inconsistent responses: Not cleaned, as respondents were able to opt out of specific sections of survey or skip questions.<br> - Data transformations or imputations: None<br> - Treatment of outliers or extreme values: None</p> <p>## 5. Variables and Measures</p> <p>The survey included Likert scale questions and qualitative open texts based the conceptual framework for Value Creation Stories (VCS) developed by Wenger et. al. (2011). There are no derived or calculated variables. Items are Likert scale, multiple choice, and open text.</p> <p>## 6. Data Quality and Reliability<br> All the responses done before or after the FLC period (7 March – 20 June 2022) were excluded of the sample.</p> <p>## 7. Data Privacy and Anonymization</p> <p>### Methods used for removing or anonymizing personal identifiers or sensitive information:<br> - Unique identifier: Unique identifiers were anonymized using MD5 Hashing via the web site https://www.miraclesalad.com/webtools/md5.php. Unique identifiers can be used to identify respondents who may have answered the survey more than once, at different points in time. This approach provides a method to anonymize sensitive data using MD5 hashing.<br> - Limitation: MD5 hashing is a one-way function; it is not possible to dehash the data and recover the original information. <br> - Macros developed in Excel to replace Country names in qualitative responses. (No country information were collected in this survey, but some respondents referred to their specific contexts in their responses.)</p> <p>## 8. Data Availability and Accessibility<br> This data set is made available on Zenodo.org in the Zenodo community “Movement for Immunization Agenda 2030 (IA2030)”<br> https://zenodo.org/communities/ia2030/</p> <p>Requests for additional information should be addressed to research@learning.foundation.</p> <p>This is a subset of data collected by The Geneva Learning Foundation (TGLF) during the 1st IA2030 Full Learning Cycle (FLC).</p> <p>The complete data set is more comprehensive, and includes: demographic information (gender, country), health system information (respondent’s health system level), respondents’ analyses of challenges and priorities.</p> <p>### Other publicly accessible locations of the data<br> The Geneva Learning Foundation publishes data sets in relation to its Immunization Agenda 2030 (IA2030) Movement learning programme in the Zenodo open repository community: https://zenodo.org/communities/ia2030/</p> <p>### Related data sets<br> Additional data sets for the first Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) are available from The Geneva Learning Foundation (TGLF) Insights Unit insights@learning.foundation</p> <p>## 10. Ethical Considerations</p> <p>### Ethical guidelines followed during data collection:<br> TGLF’s research abides by the principles of the Cantonal Commission for Research Ethics (CCER), the Federal Law on Research on Human Beings (RS 810.30), Swiss Human Research Act (HRA) and the Ordinance on Organisational Aspects of the Human Research Act (HRA Organisation Ordinance, OrgO-HRA)</p> <p>### Informed consent and participant rights information:<br> In order to join TGLF’s IA2030 Full Learning Cycle programme, participants had to confirm their agreement to use of their responses “for research, learning, evaluation, communication, and advocacy, in line with the Foundation’s mission”.</p> <p>Participants were able to opt out of the VCS questions by selecting “No” when asked “Could we ask you five questions about your participation?”. They were informed these questions were asked in order to “share your feedback in the next weekly Assembly”, the weekly synchronous meeting for programme participants. The rationale for sharing such feedback was also explained; “Your contribution will help everyone understand how we are doing as a group, and also help us to better support you.”</p> <p>Data protection and confidentiality<br> Consent was requested during the application and submitted of action plan period for sharing data, in line with the Geneva Learning Foundation’s data protection and confidentiality policy.</p> <p># Copyright and license<br> The Geneva Learning Foundation © 2022. This data set and all associated files are licensed under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)</p> <p>© The Geneva Learning Foundation 2023</p> <p>Some rights reserved. This work is available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International; https://creativecommons.org/licenses/by-nc-sa/4.0/.</p> <p>Under the terms of this license, you may copy, redistribute and adapt the data set for non-commercial purposes, provided the work is appropriately cited, as indicated below. In any use of this data set, there should be no suggestion that the Foundation endorses any specific organization, products or services. The use of the Foundation logo is not permitted. If you use the data set, then you must license your work under the same or equivalent Creative Commons license. If you create a translation of this data set, you should add the following disclaimer along with the suggested citation: “This translation was not created by the Geneva Learning Foundation. The Foundation is not responsible for the content or accuracy of this translation. The original English edition shall be the binding and authentic edition.”</p> <p>Any mediation relating to disputes arising under the license shall be conducted in accordance with the mediation rules of the World Intellectual Property Organization.</p> <p>General disclaimers. The designations employed and the presentation of the data set do not imply the expression of any opinion whatsoever on the part of the Foundation concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted and dashed lines on maps represent approximate border lines for which there may not yet be full agreement.</p> <p>The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by the Foundation in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters.</p> <p>All reasonable precautions have been taken by the Foundation to verify the information contained in this data set. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall the Foundation be liable for damages arising from its use.</p> <p>This data set contains individual views and does not necessarily represent the decisions or the policies of the Foundation.</p> <p>Version 1.0 (31 March 2023): reviewed internally; reviewed externally. </p> <p># References<br> Wenger, E., Trayner, B., de Laat, M., 2011. Promoting and assessing value creation in communities and networks: a conceptual framework (Rapport No. 18). Oitpen Universiteit, Ruud de Moor Centrum.</p> <p>Wenger, E., Trayner, B., de Laat, M., 2011. Promoting and assessing value creation in communities and networks: a conceptual framework (Rapport No. 18). Oitpen Universiteit, Ruud de Moor Centrum.</p> <p>Victoria J. Marsick, Rachel Fichter, Karen E. Watkins, 2022. From Work-based Learning to Learning-based Work: Exploring the Changing Relationship between Learning and Work, in: The SAGE Handbook of Learning and Work. SAGE Publications.</p> <p>Watkins, K.E., Sandmann, L.R., Dailey, C.A., Li, B., Yang, S.-E., Galen, R.S., Sadki, R., 2022. Accelerating problem-solving capacities of sub-national public health professionals: an evaluation of a digital immunization training intervention. BMC Health Serv Res 22, 736. https://doi.org/10.1186/s12913-022-08138-4</p> <p>Watkins, K.E., Kim, K., 2019. Measuring the Impact of the WHO Scholar Programme Courses for Immunization (2016-2018) (Evaluation report). University of Georgia at Athens, Athens, United States.</p> <p>Watkins, K.E., Bhattarai, A., 2019. Analysis of the Impact Accelerator Launch Pad Individual Acceleration Reports in July 2019. University of Georgia at Athens, Athens, United States.</p> <p># Questionnaire</p> <p>## Hello {{fname}} {{lname}}. How are you doing in the Movement for Immunization Agenda 2030?</p> <p>## Do you need help? Do you want to share your experience? We would like to know how you are doing.<br> - I am doing fine.<br> - I have a problem and need help.<br> - I want to share my experience.</p> <p>## Tell us more about what you want to share. Be specific and detailed so that we can understand. Share your lessons learned, successes, and challenges.</p> <p>## Did you complete your action for the week? If you did it, how did it turn out? What did you learn in the process? Did anything surprise you? What will you do next? If you did not complete your action, what will you do differently next week? This is a good way to write your thoughts if you did not get to speak in the last session. You can also record an audio message in the IA2030 Movement Dialogue https://t.me/+-PwJxPpyWfQ0ZjRk or share an idea or practice https://accelerator.wazoku.com/ccc/learning in the Ideas Engine.</p> <p>## What do you need help with?<br> - I do not know what I am supposed to do<br> - I need help with my IA2030 challenge<br> - I want to catch up<br> - I have poor connectivity<br> - I have a problem with technology<br> - Something else</p> <p>## Tell us more about the problem you are facing.<br> What have you tried to solve this problem? Where did you get stuck? The more information you provide, the better colleagues will be able to help you.</p> <p>## Have you tried taking time to read and follow the instructions? </p> <p>Click here https://www.learning.foundation/products/movement-for-immunization-agenda-2030-full-learning-cycle-1-march-2022 to access the video tutorials and slide decks on www.learning.foundation https://www.learning.foundation/login. </p> <p>Use your email email to log in. Don’t remember your password?</p> <p>Click here to recover it https://www.learning.foundation/password/new. </p> <p>Take the time to read the instructions – and then follow them step-by-step. Do not forget to come back to finish this questionnaire. </p> <p>## Do not suffer in silence. 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Malware Finances and Operations: a Data-Driven Study of the Value Chain for Infections and Compromised Access
<p><strong>Description</strong><br> <br> The datasets demonstrate the malware economy and the value chain published in our paper, <a href="https://doi.org/10.1145/3600160.3605047"><em>Malware Finances and Operations: a Data-Driven Study of the Value Chain for Infections and Compromised Access</em></a>, at the 12th International Workshop on Cyber Crime (IWCC 2023), part of the ARES Conference, published by the International Conference Proceedings Series of the ACM ICPS.</p> <p>Using the well-documented scripts, it is straightforward to reproduce our findings. It takes an estimated 1 hour of human time and 3 hours of computing time to duplicate our key findings from MalwareInfectionSet; around one hour with VictimAccessSet; and minutes to replicate the price calculations using AccountAccessSet. See the included README.md files and Python scripts.</p> <p>We choose to represent each victim by a single JavaScript Object Notation (JSON) data file. Data sources provide sets of victim JSON data files from which we've extracted the essential information and omitted Personally Identifiable Information (PII). We collected, curated, and modelled three datasets, which we publish under the <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <p><strong>1. MalwareInfectionSet</strong><br> We discover (and, to the best of our knowledge, document scientifically for the first time) that malware networks appear to dump their data collections online. We collected these infostealer malware logs available for free. We utilise 245 malware log dumps from 2019 and 2020 originating from 14 malware networks. The dataset contains 1.8 million victim files, with a dataset size of 15 GB.</p> <p><strong>2. VictimAccessSet</strong><br> We demonstrate how Infostealer malware networks sell access to infected victims. Genesis Market focuses on user-friendliness and continuous supply of compromised data. Marketplace listings include everything necessary to gain access to the victim's online accounts, including passwords and usernames, but also detailed collection of information which provides a clone of the victim's browser session. Indeed, Genesis Market simplifies the import of compromised victim authentication data into a web browser session. We measure the prices on Genesis Market and how compromised device prices are determined. We crawled the website between April 2019 and May 2022, collecting the web pages offering the resources for sale. The dataset contains 0.5 million victim files, with a dataset size of 3.5 GB.</p> <p><strong>3. AccountAccessSet</strong><br> The Database marketplace operates inside the anonymous Tor network. Vendors offer their goods for sale, and customers can purchase them with Bitcoins. The marketplace sells online accounts, such as PayPal and Spotify, as well as private datasets, such as driver's licence photographs and tax forms. We then collect data from Database Market, where vendors sell online credentials, and investigate similarly. To build our dataset, we crawled the website between November 2021 and June 2022, collecting the web pages offering the credentials for sale. The dataset contains 33,896 victim files, with a dataset size of 400 MB.</p> <p><strong>Credits Authors</strong></p> <ul> <li>Billy Bob Brumley (Tampere University, Tampere, Finland)</li> <li>Juha Nurmi (Tampere University, Tampere, Finland)</li> <li>Mikko Niemelä (Cyber Intelligence House, Singapore)</li> </ul> <p><strong>Funding</strong></p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under project numbers 804476 (SCARE) and 952622 (SPIRS).<br> <br> <strong>Alternative links to download:</strong> <a href="https://mega.nz/folder/aJwVyIYJ#9SWh-Z3-TpPfjHZeFxbeew">AccountAccessSet</a>, <a href="https://mega.nz/folder/iUQ3RaKB#48ZkXnFYSR0qXLcbkrZLqw">MalwareInfectionSet</a>, and <a href="https://mega.nz/folder/aNYCFCrK#pbDkJL-PNWjn1ABXbtdR4w">VictimAccessSet</a>.</p>
Dataset: A Systematic Literature Review on the topic of High-value datasets
<p>This dataset contains data collected during a study ("T<a href="http://https://arxiv.org/abs/2305.10234">owards High-Value Datasets determination for data-driven development: a systematic literature review</a>") conducted by Anastasija Nikiforova (University of Tartu), Nina Rizun, Magdalena Ciesielska (Gdańsk University of Technology), Charalampos Alexopoulos (University of the Aegean)<sup> </sup>and Andrea Miletič (University of Zagreb)<br> It being made public both to act as supplementary data for "Towards High-Value Datasets determination for data-driven development: a systematic literature review" paper (pre-print is available in Open Access here -><a href="http://https://arxiv.org/abs/2305.10234"> https://arxiv.org/abs/2305.10234</a>) and in order for other researchers to use these data in their own work.</p> <p><br> The protocol is intended for the Systematic Literature review on the topic of High-value Datasets with the aim to gather information on how the topic of High-value datasets (HVD) and their determination has been reflected in the literature over the years and what has been found by these studies to date, incl. the indicators used in them, involved stakeholders, data-related aspects, and frameworks. The data in this dataset were collected in the result of the SLR over Scopus, Web of Science, and Digital Government Research library (DGRL) in 2023.</p> <p> </p> <p>***Methodology***</p> <p>To understand how HVD determination has been reflected in the literature over the years and what has been found by these studies to date, all relevant literature covering this topic has been studied. To this end, the SLR was carried out to by searching digital libraries covered by Scopus, Web of Science (WoS), Digital Government Research library (DGRL).</p> <p>These databases were queried for keywords <em>("open data" OR "open government data") AND ("high-value data*" OR "high value data*"</em>), which were applied to the article title, keywords, and abstract to limit the number of papers to those, where these objects were primary research objects rather than mentioned in the body, e.g., as a future work. After deduplication, 11 articles were found unique and were further checked for relevance. As a result, a total of 9 articles were further examined. Each study was independently examined by at least two authors.</p> <p>To attain the objective of our study, we developed the protocol, where the information on each selected study was collected in four categories: (1) descriptive information, (2) approach- and research design- related information, (3) quality-related information, (4) HVD determination-related information.</p> <p> </p> <p>***Test procedure***<br> Each study was independently examined by at least two authors, where after the in-depth examination of the full-text of the article, the structured protocol has been filled for each study.<br> The structure of the survey is available in the supplementary file available (see Protocol_HVD_SLR.odt, Protocol_HVD_SLR.docx)<br> The data collected for each study by two researchers were then synthesized in one final version by the third researcher.</p> <p>***Description of the data in this data set***</p> <p>Protocol_HVD_SLR provides the structure of the protocol<br> Spreadsheets #1 provides the filled protocol for relevant studies.<br> Spreadsheet#2 provides the list of results after the search over three indexing databases, i.e. before filtering out irrelevant studies</p> <p>The information on each selected study was collected in four categories:<br> (1) descriptive information,<br> (2) approach- and research design- related information,<br> (3) quality-related information,<br> (4) HVD determination-related information</p> <p>Descriptive information <br> 1) Article number - a study number, corresponding to the study number assigned in an Excel worksheet<br> 2) Complete reference - the complete source information to refer to the study<br> 3) Year of publication - the year in which the study was published<br> 4) Journal article / conference paper / book chapter - the type of the paper -{journal article, conference paper, book chapter}<br> 5) DOI / Website- a link to the website where the study can be found<br> 6) Number of citations - the number of citations of the article in Google Scholar, Scopus, Web of Science<br> 7) Availability in OA - availability of an article in the Open Access<br> 8) Keywords - keywords of the paper as indicated by the authors<br> 9) Relevance for this study - what is the relevance level of the article for this study? {high / medium / low}</p> <p>Approach- and research design-related information<br> 10) Objective / RQ - the research objective / aim, established research questions<br> 11) Research method (including unit of analysis) - the methods used to collect data, including the unit of analy-sis (country, organisation, specific unit that has been ana-lysed, e.g., the number of use-cases, scope of the SLR etc.)<br> 12) Contributions - the contributions of the study<br> 13) Method - whether the study uses a qualitative, quantitative, or mixed methods approach?<br> 14) Availability of the underlying research data- whether there is a reference to the publicly available underly-ing research data e.g., transcriptions of interviews, collected data, or explanation why these data are not shared?<br> 15) Period under investigation - period (or moment) in which the study was conducted<br> 16) Use of theory / theoretical concepts / approaches - does the study mention any theory / theoretical concepts / approaches? If any theory is mentioned, how is theory used in the study?</p> <p>Quality- and relevance- related information <br> 17) Quality concerns - whether there are any quality concerns (e.g., limited infor-mation about the research methods used)?<br> 18) Primary research object - is the HVD a primary research object in the study? (primary - the paper is focused around the HVD determination, sec-ondary - mentioned but not studied (e.g., as part of discus-sion, future work etc.))</p> <p>HVD determination-related information <br> 19) HVD definition and type of value - how is the HVD defined in the article and / or any other equivalent term?<br> 20) HVD indicators - what are the indicators to identify HVD? How were they identified? (components & relationships, “input -> output")<br> 21) A framework for HVD determination - is there a framework presented for HVD identification? What components does it consist of and what are the rela-tionships between these components? (detailed description)<br> 22) Stakeholders and their roles - what stakeholders or actors does HVD determination in-volve? What are their roles?<br> 23) Data - what data do HVD cover?<br> 24) Level (if relevant) - what is the level of the HVD determination covered in the article? (e.g., city, regional, national, international)</p> <p><br> ***Format of the file***<br> .xls, .csv (for the first spreadsheet only), .odt, .docx</p> <p>***Licenses or restrictions***<br> CC-BY</p> <p> </p> <p>For more info, see README.txt</p>
Bird plumage brightness scores and blood parasite prevalence values of North American passerine species
<p>Dataset with bird plumage brightness scores and blood parasite prevalence values for 114 North American passerine host species. One file contains the data table. One file contains a table with descriptions of the columns in the data table.</p> <p>Note: These data were reconstructed from files used in Read & Harvey 1989 (<a href="https://doi.org/10.1038/339618a0">https://doi.org/10.1038/339618a0</a>) with column headings inferred with the help of Read 1991 (<a href="https://doi.org/10.1086/285225">https://doi.org/10.1086/285225</a>).</p>
Dataset for: Improving the accuracy of δ18O and δ17O values of O2 measured by continuous-flow isotope-ratio mass spectrometry with a multi-point isotope-ratio calibration
<p>Dataset containing raw and treated isotope-ratio mass spectrometry and laser absorption spectroscopy data, accompanying the manuscript "Improving the accuracy of δ18O and δ17O values of O2 measured by continuous-flow isotope-ratio mass spectrometry with a multi-point isotope-ratio calibration". This dataset also contains O2 concentration and partial pressure data used for figures in the associated manuscript.</p>
Concentrations of methane, sulfate and lipid biomarkers and carbon isotope values oof lipids in the sediments from the outer Laptev Sea
<p>The dataset contains the concentrations of methane, sulfate and microbial lipid biomarkers, and the carbon isotope composition of lipids in the sediment collected from the SWERUS-C3 expedition in 2014. The core sediment samples were from stations 13, 14 and 23 in the outer Laptev Sea. The field investigation reveals it is a methane seep area. </p>
Valuing multiple ecosystem services under contrasting land use scenarios, Grand River Basin (Iowa and Missouri, USA), 2016
This dataset includes spatial and tabular data used to evaluate ecosystem service outcomes under three land-use scenarios (baseline, buffered, and productivity-based) within the Grand River Basin, spanning southwest Iowa and northwest Missouri. The dataset comprises geospatial layers and model inputs for land cover, soil characteristics, and hydrology, as well as outputs from the InVEST modeling suite for nutrient delivery ratio, sediment delivery ratio, carbon storage, and pollinator abundance. It also includes financial data used to estimate the net present value of bioenergy grassland systems, including enterprise budgets and biomass yield assumptions. The scenarios simulate the conversion of cropland to native grassland either adjacent to streams (Buffered) or on low-productivity soils (Productivity-based), and the dataset captures associated changes in land cover, ecosystem service indicators, and potential biomass production. This dataset is intended to support further research and planning related to landscape-scale conservation, ecosystem service valuation, and bioenergy development in agricultural regions.
Burned soil surface radiocarbon values for moss macrofossils plucked from the Anaktuvuk River Fire sites
We used radiocarbon dating of the organic soil surface remaining post-fire to examine whether the fire burned into ancient and likely irreplaceable soil C pools. Suprisingly, it did not; all radiocarbon dates from burned soil surfaces contained bomb carbon, setting the maximum age of the burned soil surfaces at ~50 years.
Black spruce soil delta15N values from 31 sites centered around Fairbanks, AK collected in 2007.
This dataset contains delta 15N values of bulk organic soils, dissolved organic nitrogen, ammonium, and nitrate. Soil delta 15N values were measured using the denitrifier method at the University of Florida.
del 15N Values for Tanana River Floodplain Soils and Xylem Sap Samples
Because soil microoorganisms preferencially discriminate either for or against heavy nitrogen isotopes in organic compounds we can learn something about the route these compounds take through any given system by looking at the isotopic ratio of heavy to light nitrogen within a pool as compared to the ratio for the system as a whole. An enriched pool means that 15N has been discriminated for, while a depleted pool means 15N has been discriminated against. By determining the del 15N values for soil protein, amino acids, ammonium, the soil microbial population, and plant roots and xylem we can learn something about where plants are deriving there nitrogen nutrition.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): The radiocarbon value of ecosystem respiration, 2009-2012 I: Reco.
In this larger study, we are asking the question: Is old carbon that comprises the bulk of the soil organic matter pool released in response to thawing of permafrost? We are answering this question by using a combination of field and laboratory experiments to measure radiocarbon isotope ratios in soil organic matter, soil respiration, and decomposition in tundra ecosystems. The objective of these proposed measurements is to develop a mechanistic understanding of the SOM sources contributing to C losses following permafrost thawing. We are making these measurements at an established tundra field site near Healy, Alaska in the foothills of the Alaska Range. Field measurements center on a natural experiment where permafrost has been observed to warm and thaw over the past several decades. This area represents a gradient of sites each with a different degree of change due to permafrost thawing. As such, this area is unique for addressing questions at the time and spatial scales relevant for change in arctic ecosystems. This data set includes the growing season radiocarbon values of ecosystem and plant respiration at the thaw gradient and CiPEHR (a permafrost warming experiment). Depending on the year, the measurements were taken monthly during the growing season or just once a growing season.
Hyperspectral reflectance values and biophysicochemical properties of biocrusts and soils in the Fryxell Basin, McMurdo Dry Valleys, Antarctica (2019)
This data package includes ecological parameters of biocrust and soil from samples collected in-situ within the Lake Fryxell Basin of the McMurdo Dry Valleys, Antarctica during December of 2019. Parameters include biological (ash-free dry mass, pigment concentration, and counts of soil invertebrates), physical (water content, electrical conductivity, and pH), and chemical properties (inorganic nitrogen, inorganic phosphorous, total nitrogen, and total organic carbon) of the surface soil, biocrust, and underlying soil. This data package also contains reflectance measurements of biocrust, soil, and granite samples acquired in a laboratory using a hyperspectral spectrometer. Included are hyperspectral reflectance measurements of a laboratory study using a variety of mixtures of soil and biocrust (0 – 100% biocrust), as well as reflectance measurements of individual grab samples collected from each of the field plots. These data aid our understanding of the ecological structure and functioning of biocrust microhabitats as well as the location of these communities throughout the Lake Fryxell Basin. This work also aims to assist in understanding the carbon budget of the basin and highlight the importance of these snowpack-fed biocrust communities, which are understudied but likely an important piece of the overall carbon budget in this region.
Biophysicochemical properties and hyperspectral reflectance values of biocrust and soil samples, Fryxell Basin, McMurdo Dry Valleys, Antarctica (2022)
This data package includes biophysicochemical properties of biocrust and soil samples collected in-situ within the Lake Fryxell basin of the McMurdo Dry Valleys, Antarctica during December 2022 at 64 different terrestrial locations. These parameters include biological (ash-free dry mass, pigment concentration, soil invertebrate counts), physical (gravimetric water content, electrical conductivity, pH), and chemical (inorganic nitrogen, inorganic phosphorous, total nitrogen, soil organic carbon) properties of the surface soil, biocrust, and underlying soil. This package also contains reflectance measurements of individual grab samples of biocrust, soil, and rock collected from each of the field sites, acquired in a laboratory using a hyperspectral spectrometer. Additionally, this package contains parameters derived solely from geospatial data for each of the field sites, including aspect, slope, elevation, gravimetric water content, and snow frequency. These data were used to model habitat suitability of biocrusts in the Fryxell Basin using both in-situ field survey data and geospatial parameters. They aid in our understanding of the ecology of biocrust communities, where they are located throughout the Fryxell basin, and the structure and functioning of these microhabitats as well as improving understanding of the regional carbon budget. These data also highlight the importance of snow patch-associated biocrust communities, which are understudied but likely an important piece of the overall carbon budget in this region.
Data to support "delta13C and delta15N values from a mesocosm experiment showing sea urchins mediate the availability of kelp detritus to benthic consumers"
The purple sea urchin Strongylocentrotus purpuratus is an important herbivore and detritivore in southern California giant kelp (Macrocystis pyrifera) forests that grow on shallow rocky reefs (~3-20m depth) off southern California, including Santa Barbara where this study was done. To investigate the shredder activity of purple urchins, we assembled communities of common detritivores and suspension feeders from local reefs in mesocosms. Three species of brittle stars (Ophiopteris papillosa, Ophioplocus esmarki, and Ophiothrix spiculata), one vermetid gastropod (Thylacodes squamigerus), two barnacles (Chthamalus sp. and Megabalanus californicus), one polychaete worm (Chaetopterus sp.), and three sea cucumbers (Cucumaria piperata, Pachythyone rubra, and Cucumaria salma) were collected from the seafloor of local kelp forests at 5-15m depth in the Santa Barbara Channel. Ten of each species were placed in each of six 50 L (51x38x27 cm) flow-through unfiltered seawater tanks, each containing two concrete bricks for hard substrate on top of 3 cm of sand. Because sea urchins are highly mobile, capable of bulldozing other occupants, we isolated them above the experimental communities on plastic mesh (1cm). Mesh dividers were secured horizontally across each mesocosm 16 cm above the bottom. Ten individuals of each consumer species, along with 2-3 blades, totaling 47.3 g (±1.2 SE), of isotopically labeled kelp (see below) were placed on the floor of each tank, below the divider. On top of the mesh divider, we placed an additional 229.8 g (±6.1 SE) of enriched kelp blades, and in half of the tanks, 10 adult urchins (total mass 343.3 g ±1.3 SE) per tank. The kelp below the mesh ensured that the detritivores had direct access to degrading kelp detritus regardless of the urchin treatment, similar to the situation in the kelp forest, allowing us to more clearly ascertain the degree to which the detritivores were dependent on urchins for kelp detritus assimilation. The experiment wa
Valuing diversification benefits through intercropping in Mediterranean agroecosystems: A choice experiment approach.
<p>This data set contains information from a choice experiment survey developed to value the socio-economic benefits of intercropping practices in Mediterranean agroecosystems.</p> <p>These data correspond to the open-access article "Valuing diversification benefits through intercropping in Mediterranean agroecosystems: A choice experiment approach" published in Ecological Economics. (<a href="https://doi.org/10.1016/j.ecolecon.2020.106593">https://doi.org/10.1016/j.ecolecon.2020.106593</a>), funded by he European Commission Horizon 2020 project Diverfarming [grant agreement 728003]. </p>
Fig. 3 in First chromosomal analysis of Gymnorhamphichthys britskii: the remarkable lowest diploid value within the family Rhamphichthyidae (Gymnotiformes)
Fig. 3. Karyotypes of Gymnorhamphichthys britskii arranged a. from Giemsa stained; b. C-banded; and c. after double- FISH with 18S rDNA (red) and 5S rDNA (green) probes.. The NOR-bearing chromosomes (pair 9) are in the box. Note the size heteromorphism involving the NORs detected by the Ag- NOR and 18S rDNA-FISH techniques. Scales bar = 10 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.