We present basic observational data on the 6C** sample. This is a new sample of radio sources drawn from the 151‐MHz 6C survey, which was filtered with radio criteria chosen to optimize the chances of finding radio galaxies at z > 4. The filtering criteria are a steep‐spectral index and a small angular size. The final sample consists of 68 sources from a region of sky covering 0.421 sr. We present Very Large Array radio maps, and the results of K‐band imaging and optical spectroscopy. Near‐infrared counterparts are identified for 66 of the 68 sources, down to a 3σ limiting magnitude of K∼ 22 mag in a 3‐arcsec aperture. Eight of these identifications are spatially compact, implying an unresolved nuclear source. The K‐magnitude distribution peaks at a median K≈ 18.7 mag, and is found to be statistically indistinguishable from that of the similarly selected 6C* sample, implying that the redshift distribution could extend to z≳ 4. Redshifts determined from spectroscopy are available for 22 (32 per cent) of the sources, over the range of 0.2 ≲z≲ 3.3. We measure 15 of these, whereas the other seven were previously known. Six sources are at z > 2.5. Four sources show broad emission lines in their spectra and are classified as quasars. Three of these show also an unresolved K‐band identification. 11 sources fail to show any distinctive emission and/or absorption features in their spectra. We suggest that these could be (i) in the so‐called ‘redshift desert’ region of 1.2 < z < 1.8 or (ii) at a greater redshift, but feature weak emission‐line spectra.

We use the 6C * * sample to investigate the comoving space density of powerful, steep-spectrum radio sources. This sample, consisting of 68 objects, has virtually complete K-band photometry and spectroscopic redshifts for 32 per cent of the sources. In order to find its complete redshift distribution, we develop a method of redshift estimation based on the K-z diagram of the 3CRR, 6CE, 6C * and 7CRS radio galaxies. Based on this method, we derive redshift probability density functions for all the optically identified sources in the 6C * * sample. Using a combination of spectroscopic and estimated redshifts, we select the most radio luminous sources in the sample. Their redshift distribution is then compared with the predictions of the radio luminosity function of Jarvis et al. We find that, within the uncertainties associated with the estimation method, the data are consistent with a constant comoving space density of steep-spectrum radio sources beyond z 2.5, and rule out a steep decline.

In this paper we present near-infrared K-band imaging of a sample of ultra-steep-spectrum (USS) radio sources selected at 74 MHz. The dual selection criteria of low frequency and USS mean that we should be sensitive to the highest-redshift (z > 5) radio galaxies. We have obtained K-band magnitudes for all of the objects in our sample of 26 and discuss the properties of each.There is a pronounced bias in this sample towards fainter magnitudes and thus higher redshifts when compared to complete unfiltered samples such as the 7CRS of Willott et al., implying that the steep-spectrum technique is still viable at 74 MHz. However, there are more bright (K < 17) sources in the 74-MHz sample than in a similar sample selected at 151 MHz, namely 6C*. This is principally due to the additional selection criterion of a small angular size for the radio sources in 6C*; four of the six sources in the 74-MHz USS sample with K < 17 have angular sizes >15 arcsec (the angular size cut-off of 6C*).We find that the distribution of K-band magnitudes from a sample selected at 74 MHz is statistically indistinguishable from the 6C* sample, when similar angular size filtering is applied to the 74-MHz sample.

In this paper, we explain our strategy for developing research data management policies at TU Delft. Policies can be important drivers for research institutions in the implementation of good data management practices. As Rans and Jones note (Rans and Jones 2013), "Policies provide clarity of purpose and may help in the framing of roles, responsibilities and requisite actions. They also legitimise making the case for investment". However, policy development often tends to place the researchers in a passive position, while they are the ones managing research data on a daily basis. Therefore, at TU Delft, we have taken an alternative approach: a policy needs to go hand in hand with practice. The policy development was initiated by the Research Data Services at TU Delft Library, but as the process continued, other stakeholders, such as legal and IT departments, got involved. Finally, the faculty-based Data Stewards have played a key role in leading the consultations with the research community that led to the development of the faculty-specific policies. This allows for disciplinary differences to be reflected in the policies and to create a closer connection between policies and day-today research practice. Our primary intention was to keep researchers and research practices at the centre of our strategy for data management. We did not want to introduce and mandate requirements before adequate infrastructure and professional support were available to our research community and before our researchers were themselves willing to discuss formalisation of data management practices. This paper describes the key steps taken and the most important decisions made during the development of RDM policies at TU Delft.